The lowest knowledge scores were found in the areas of optimal energy intake, optimal weight gain, and the role of micronutrients in diet during pregnancy. In conclusion, the study shows limited nutrition knowledge of Czech pregnant women in some areas of nutrition. Increasing nutritional knowledge and nutrition literacy in Czech pregnant women is crucial for supporting their optimal course of pregnancy and the future health of their offspring.Adequate nutrition and the nutritional status of pregnant women are critical for the health of both the mother and the developing foetus. Research has shown a significant impact of nutrition on the child’s health and the future risk of developing chronic noncommunicable diseases (NCDs), such as obesity, diabetes, hypertension, and cardiovascular disease. There is currently no data on the level of nutritional knowledge of Czech pregnant women. This survey aimed to evaluate their level of nutritional knowledge and literacy. An analytical cross-sectional study was conducted in two healthcare facilities in Prague and Pilsen between April and June 2022. An anonymous self-administered paper-form questionnaire for assessing the level of nutritional knowledge (40 items) and the Likert scale for assessing nutrition literacy (5 items) were used. A total number of 401 women completed the questionnaire. An individual’s nutritional knowledge score was calculated and compared with demographic and anamnestic characteristics using statistical methods. The results showed that only 5% of women achieved an overall nutritional score of 80% or more. University education ( The number of people suffering from overweight, obesity and related diseases is increasing worldwide. According to the World Health Organization (WHO), chronic noncommunicable diseases (NCDs) are the leading cause of morbidity and mortality worldwide. By 2022, 41 million people will die from these causes each year, accounting for 74% of all deaths worldwide. Therefore, great emphasis is placed on early prevention of these NCDs, e.g., hypertension, overweight and obesity, hyperglycemia, and hyperlipidemia ,6,7,8,9.A healthy lifestyle, including a balanced diet and weight control, is undoubtedly crucial for the adequate development of pregnancy in terms of maternal and fetal health ,4,10,11.In today’s world, the online environment offers countless possibilities for obtaining information. With the internet being more accessible to an increasing number of people, it could be thus assumed that easy access to information allows women to obtain all the necessary knowledge related to adequate nutrition, elimination of food risks, and nutrition-related complications associated with pregnancy. However, studies show that this is far from the case and that women do not follow nutritional recommendations ,16,17,18A woman’s eating behaviour during pregnancy is influenced by many multifaceted and complex factors. One of the most important factors is the level of nutritional knowledge, the lack of which can be a barrier to adopting healthy behaviours and other postnatal weight management practices. Nutrition knowledge is not only about facts and processes but also about how to apply them in practice . Most stLee et al. noted that there is a lack of published research on the assessment of pregnant women’s comprehensive nutrition knowledge . GeneralThere is no definition of the minimum nutritional knowledge that pregnant women should know. The abovementioned studies focusing on the nutritional knowledge of pregnant women are very heterogeneous in this regard, making it difficult to establish a single tool and score system. Women should demonstrate a general overview of all areas of dietary recommendations  without favouring any one area. Each recommendation has a particular rationale related to the health of the woman and the developing foetus. The nutrition knowledge classification is an indicator of success that provides an overall picture; however, far more important is to identify areas in which the level of nutrition knowledge is lowest ,19.Nutritional knowledge is one of the cornerstones of health and nutrition literacy, which represents the ability to obtain, understand, and use information that ultimately leads to an increase in one’s own influence on the quality of one’s health. Nutritional knowledge alone does not completely influence an individual’s behaviour, but it can significantly shape their attitudes, which can be reflected in a person’s actions .As for the health and nutritional policy in the Czech Republic on pregnant women regarding nutrition advice and nutritional supplement recommendation during medical appointments, there is not anything like that. For these reasons, it is not entirely clear who should provide this care and counselling to pregnant women, so it is fragmented among health professionals. Although gynaecologists are at the frontline of basic prenatal care, the number of patients and the lack of dedicated time for them detract from prevention, including both nutritional education and the recommendation of dietary supplements . Other cIn the Czech Republic, no data are available on pregnant women’s nutritional knowledge. Insufficient attention is paid to this issue, and there is a lack of current nutritional recommendations at the national level, and it is not obvious to what extent healthcare professionals should devote time to nutrition education. Thus, women primarily depend on available sources of information . To increase awareness of this issue, this study aimed to describe the nutritional knowledge level of Czech pregnant women with attention to the influence of selected sociodemographic and anamnestic factors that may contribute to the level of this knowledge and nutrition literacy.This analytical cross-sectional survey-based study was conducted between April and June 2022. The questionnaires were distributed to the target participants in a paper form, which they completed on the spot and then dropped in a box. There was no time limit for completion, and the average completion time was 20–30 min. Fully anonymised completed questionnaires were collected securely before being transferred to electronic format.The target group was Czech pregnant women. Inclusion criteria were as follows: Czech citizenship, last month of the third trimester (≥36th week of pregnancy), and singleton pregnancy. Exclusion criteria included: non-Czech nationality, low gestational age (˂38 weeks of pregnancy), multiple pregnancies, and age < 18 years.Data collection occurred in the Gynaecology and Obstetrics Clinic in Pilsen and the Institute for Maternal and Pediatric Care in Prague. These medical facilities were randomly selected; however, there is a significant difference between Prague and Pilsen. Prague is the capital of the Czech Republic (1.3 million inhabitants), and Pilsen represents a smaller town  in the western part of the country. The purpose of the visits to these medical facilities was regular last prenatal check-ups before they come under the control of the birthing facility. Women were asked to participate in the study when they visited an antenatal clinic. Trained health professionals provided women with information about the study, which was also given in the written form.TM version 7.2.5  utilising the following assumptions [i.Confidence level (CI) = 95%;ii.Acceptable error margin = 5%;iii.Target population size ≈ 111,425 (the average number of live births in the Czech Republic between 2010 and 2021);iv.Number of clusters = 2;v.Expected frequency of the primary outcome, which is the satisfactory level of knowledge score > 80%.The minimum sample size required for this study was estimated using Epi Infoumptions :i.Confidn = 401).At least 384 valid responses were required to establish statistically robust inferences between putative demographic and anamnestic predictors and the current levels of nutritional knowledge. A total of 457 questionnaires were completed, of which 31 were discarded because of incomplete demographic or anamnestic data, and 25 because of the woman’s low gestational age (<38 weeks of pregnancy). Only fully completed questionnaires were used to assess nutritional knowledge  was thus preceded by an extensive search of scientific literature related to the topic, the selected target group, and the questionnaire methodology. The selection of studies was gradually narrowed down, and the instrument construction was based on modified questionnaires according to selected studies ,23. The The resulting questionnaire was pilot tested on a sample of 30 women representing the target population and modified based on the feedback as necessary. After final modifications, the reliability of the questionnaire was tested, and the kappa value for test–retest reliability was calculated. The mean value of 0.914 showed perfect test–retest reliability.The questionnaire consisted of two parts. The first part focused on basic sociodemographic and anamnestic data such as age, level of education, place of residence, pregnancy order, body mass index (BMI), chronic diseases, medication and supplements, adherence to an alternative diet and anthropometric measurements, specifically pregestational height and weight, which were taken from the medical records of each pregnant woman. The cutoff point of age groups (≤28 and 28 years) was determined according to the average age of the first-time mother, which is 28 years in the Czech Republic. Listeria monocytogenes and Salmonella).The second part of the questionnaire consisted of 40 multi-choice questions testing nutritional knowledge divided into five categories. The first category focused on knowledge of micronutrients , the second category focused on knowledge of macronutrients, and the third category on knowledge of nutritional recommendations  and optimal daily energy intake, weight gain, and the effect of excess weight on a woman’s health. The fourth and fifth categories were devoted to food supplements and food safety concerns  consisted of 40 items with one correct answer; therefore, the items were considered binary (correct = 1/incorrect = 0). Each item had a ‘do not know’ option to eliminate guessing. The sum of the correct answers (maximum 40) represented the final nutrition knowledge score, which was assessed in relation to the main variables . A satisfactory level of nutritional knowledge was defined to be >80%.The study was approved by the Ethics Committee of the Faculty of Medicine, Masaryk University, Brno (ref. no. 4/2022), and by the Ethics Committee of the University Hospital in Pilsen and the Faculty of Medicine, University of Pilsen (ref. no. 74/2022), and the Ethics Committee of the Institute for Mother and Child Care in Prague (ref. no. 1/1/2022). The respondents confirmed their consent to participate in the study before completing and submitting a questionnaire.Sig.) of <0.05. Consequently, descriptive statistics were used to summarise the sample characteristics (independent variables) and nutritional health knowledge and literacy (dependent variables). Qualitative variables such as education level, pregnancy order, and city were summarised using frequencies and percentages; while numerical variables such as knowledge score were summarised using means and standard deviations (µ ± SD). Then, inferential statistics were carried out to perform hypothesis testing using the chi-squared test (χ2), Fisher’s exact test, Mann–Whitney test (U), and Kruskal–Wallis test (H). All analytical tests were carried out with a significance level (Sig.) of <0.05.All statistical analyses were carried out using the Statistical Package for Social Sciences (SPSS) version 28.0  and the R-based open software jamovi ,35. InitOf the 401 questionnaires collected, 264 (65.8%) were collected in Prague and 137 (34.2%) in Pilsen. Most women were aged 30–34 years (40%). The average age of women was 31.6 years, and the median age was 31 years. All of the women were in the third trimester of pregnancy. More than half of the participants had a university degree (57.4%), more of them in Prague (44.9%) than in Pilsen (12.5%). Fifty-three percent of the women were expecting their first child, and their median age was 30.3 years. Most women had a normal body weight at the beginning of pregnancy (63.7%).The most commonly reported disease accompanying pregnancy was thyroid disease in 14.5% of women, 6% of women reported gestational diabetes mellitus, and 1% of women reported high blood pressure. About a quarter (24.9%) of women were taking multivitamin preparations, 23.7% of women took iron, 14% magnesium and 10.7% folic acid supplements. However, multivitamin supplements are also likely to contain folic acid and iron; contents of these were not controlled in this work due to a huge variability in multivitamin preparations. Only 2.5% of the women reported adherence to an alternative diet that was mostly vegetarian or vegan . Salmonella, sources of protein in nutrition, sources of ω − 3 unsaturated fatty acids  demonstrated better results in questions related to iron (q. #1 and #4), folic acid (q. #6 and #7), omega-3 unsaturated fatty acids (q. #11), vitamin D (q. #14), iodine (q. #15 and #16), vitamin A (q. #17), major nutrients (q. #19 and #20), nutritional recommendations , nutritional supplements (q. #35), and food safety (q. #37) knowledge as compared with pregnant women with pre-university educational level .Notably, pregnant women with a pre-university education level were more successful with the question about salt consumption in pregnant women (q. #24) compared with pregnant women with a university education level. For further analysis, the effect of the age group was also evaluated. As the average age of first-time mothers in the Czech Republic is 28 years, the pregnant women involved in this study were separated into two groups: (1) ≤28 years old and (2) >28 years old women .The evaluation of the questionnaire showed only a limited effect of age on performance in the NKT. The statistical analysis revealed that pregnant women with higher average age (>28 years old) demonstrated better results in questions related to vitamin D (q. #13), iodine (q. #15 and #16), nutritional recommendations (q. #21 and #28), and nutritional supplements (q. #35) as compared to pregnant women with an average age of 28 years or less; however, the group of ≤28 years-old pregnant women showed a higher percentage of successful answers in the question about salt consumption in pregnant women (q. #24) Next, the effect of body mass index (BMI) and pregnancy order on the performance in the NKT was evaluated. It was found that pregnant women scored as underweight or extremely obese demonstrated worse nutritional knowledge in questions related to iron (q. #3 and #4), omega-3 unsaturated fatty acids (q. #12), iodine (q. #15), major nutrients (q. #19 and #20), and nutritional recommendations (q. #30) as compared with pregnant women scored as normal weight, overweight, or obese. For pregnancy order, the pregnant women tested in this study were separated into two groups: (1) primiparous and (2) multiparous. Primiparous women demonstrated a higher percentage of successful answers in questions related to calcium (q. #9), vitamin A (q. #17), nutritional recommendations (q. #22), and food safety  compared with multiparous women. On the other hand, multiparous women demonstrated better results in questions related to iron (q. #3) and vitamin D (q. #14) than primiparous women .The nutritional knowledge score  analysis revealed several main factors that influenced the NKT outcome. It was found that one of the most important factors in nutritional knowledge was the education level of pregnant women. Women with a university level of education (undergraduate and graduate degrees) demonstrated better results than those with a pre-university education level. Further, a significant effect was also found in the city of origin. Pregnant women in Prague demonstrated better performance compared with women from Pilsen. Additionally, pregnancy order was also found as a significant factor in the NKT outcome. Primiparous pregnant women showed higher total nutritional knowledge scores than multiparous women. The Kruskal–Wallis test also revealed a significant effect of BMI on the total NKT score. The following analysis showed that the pregnant women with extreme values of BMI (underweight or extremely obese) showed lower scores in NKT compared with pregnant women who scored as normal weight, overweight, or obese. Notably, pregnant women with NCDs demonstrated better results in the NKT than pregnant women without NCDs .A linear regression model was established for the overall nutritional health knowledge score incorporating all the independent variables that were found to be significant in the univariate analyses. According to the model, education level had an adjusted odds ratio (aOR) of 3.06 (CI 95%: 1.97–4.15) and noncommunicable diseases had an aOR of 1.13 (0.03–2.23). On the other hand, city and pregnancy order were not found to be statistically significant in this model .p = 0.036), utilisation (p = 0.031), and help-seeking (p = 0.036). For university-educated women, it was more difficult to recognise valuable sources of information, but after receiving the proper information, it was much easier for them to use it. Interestingly, both groups (pre-university and university) reported that it was mainly easy and very easy for them to seek professional help when needed . In this regard, it is desirable that especially at risk groups of women have sufficient knowledge about the optimal increase in energy requirements for proper weight gain during pregnancy.Statistical relationships between the level of nutritional knowledge and women’s weight at the beginning of pregnancy have been demonstrated. Underweight and extremely obese women showed lower knowledge than other women. The problem is found in the lower level of education of these risk groups in the issue of recommending energy intake in the second and third trimesters , risks associated with Listeria monocytogenes (p = 0.003) and risks of this bacterium to the foetus (p = 0.01). This assumption thus may be correct.Our results showed a higher level of nutritional knowledge of first-time mothers compared to that of women with more children. It was hypothesised that there might be a significant relationship between pregnancy order and level of nutritional knowledge. These women may have more time to educate themselves or perceive a higher degree of responsibility for the optimal course of pregnancy. Indeed, primiparous women demonstrated a higher level of knowledge in the area of food safety (mercury content in fish (p < 0.001), city (p < 0.001), pregnancy order (p = 0.041), BMI (p = 0.024), and NCDs (p = 0.044). A cross-sectional study from Ghana found satisfactory knowledge of food risks among pregnant women but suggested that food safety knowledge may not be associated with appropriate nutritional behaviour . In thisOur study demonstrated a higher nutritional knowledge score among women with higher education compared to other groups of women. The factor of the place of data collection is debatable, as it was a question of large cities with a high proportion of university-educated respondents, and the connection of this result is not entirely clear. According to the national survey from 2021, the population level of university education was 18.7%, the highest concentration was in Prague (35.9%), and lower percentage was in Pilsen (14.5%) . The samIn the field of research, we do not find many studies to compare with our results. We can compare the results from the Istanbul study on a sample of 736 pregnant women, which showed a relationship between the age of the women and the level of nutritional knowledge. Women aged < 18 years presented the lowest level of knowledge compared to the other age groups, 25–29 years, 30–34 years, and ≥35 years. In this study, high school graduates had higher scores of nutritional knowledge than primary school graduates, and finally, the order of pregnancy emerged as significant, with women in their first pregnancy having higher nutritional knowledge scores than those with more than five pregnancies. BMI was not a statistically significant factor in this study [In contrast, a cross-sectional study assessing the nutritional knowledge of pregnant women according to the Australian Dietary Guidelines confirmed significant demographic differences in nutritional knowledge scores. Multiple regression analysis confirmed significant independent effects of education level, income, age, stage of pregnancy, language, and health/nutrition qualifications on respondents’ nutritional knowledge scores . p < 0.001), but women’s age, trimester, and parity were statistically insignificant [Some studies focus on isolated knowledge of nutrition in conjunction with sociodemographic data of pregnant women. A Turkish study investigated the knowledge of iodine in 150 pregnant women aged 19 to 45 in relation to the sociodemographic characteristics of the study participants. Only 68% of women knew that iodine deficiency could have serious consequences during pregnancy. Knowledge was significantly associated with the level of education , city (p < 0.001), order of pregnancy (p = 0.041), BMI (p = 0.024), and NCDs (p = 0.044) were statistically significant factors. For targeted prevention, it is necessary to continue to measure and evaluate the level of internal knowledge. Nutritional education should therefore increase the level of knowledge in the areas mentioned above, which may lead to changes in inappropriate eating habits and support the adoption of healthy eating habits.The study’s results showed low nutritional knowledge among Czech pregnant women: only 5% of women demonstrated knowledge above 80%. Lack of knowledge was demonstrated in key areas such as optimal energy intake, optimal weight gain, and micronutrients. Women found it difficult to understand the meaning of the recommendations and their use in practice. Level of education (


Violence against children and adolescents is a widespread problem. However, most studies conducted in this field has been carried out in Western countries and studies are needed in non-Western countries, especially in Sub-Saharan Africa, where rates of child physical violence are high. The present study aimed firstly to document the different forms of physical violence and attitudes toward corporal punishment (CP) across Cameroon, Switzerland, and Togo. The second objective aimed, on the one hand, to understand the influence of cultural context, childhood physical abuse, and parental attitudes on physically violent parental practices in these three different cultural contexts. On the other, this study aimed to investigate the mediating role of childhood physical abuse and parental attitudes on the effect of cultural contexts on parental practices. Five hundred and forty-seven parents from Togo, Cameroon, and Switzerland filled out questionnaires concerning violent parental practices (ICAST-P), childhood physical abuse (CTQ-SF), and parental attitudes in favor of CP. Firstly, results highlighted some cultural differences regarding parental attitudes and practices. Secondly, the hierarchical regression showed that physical violence could be partially predicted by the cultural context, childhood abuse, and attitudes in favor of CP. Finally, childhood abuse and parental attitudes mediated the link between the cultural context and parental practices. This study underscores the importance of considering the cultural context when examining parental practices. Moreover, these results provide a better understanding of these types of parental practices in less studied contexts. Furthermore, banning corporal punishment (CP) is recommended in every context  are factors that can influence parenting, and mutual influence may also occur , of whom 178 were male (32.54%) and 369 were females (67.45%). They were recruited in Cameroon , Switzerland , and Togo . Moreover, parents indicated the age of their child for whom they were answering. The mean age was 6.87 (SD = 4.19) years for the Cameroonian children, 7.05 (SD = 4.46) years for the Swiss children, and 8.20 (SD = 4.67) years for the Togolese children  parental practices. The socio-demographic variables were entered in the first step, including the context, the parent’s gender, and the child’s gender. To compare, on the one hand, two African countries (Togo and Cameroon) and, on the other hand, a non-Western country to a Western country, Togo was chosen as the reference level. Then, in the second step, childhood physical abuse variable was added and finally, the variable concerning parental attitudes in favor of CP was added. Finally, a multiple mediation was conducted to investigate the role of childhood physical abuse and attitudes in favor of CP on the association between the cultural context and the adoption of physically violent practices.To begin, preliminary analyses were conducted to assess the reliability of the subscales used. Then, descriptive analyses were performed to answer the study’s first objective. For these analyses only, the parental attitudes variable was dichotomized in one group including parents who The statistical analyses were conducted using “RStudio” (version 1.2.5019) and “Jamovi” (version 2.0.0.0) software programs. The significance threshold was 0.05 for all statistical analyses. The multiple imputation method , followed by Togo (44.22%), and finally Switzerland (4.76%). More specifically, the percentages for the items 1 and 3  = 50.0, p < 0.001). The influence of childhood physical abuse, entered for the second step of the model, was statistically significant (p < 0.001) and explained an additional 3.8% of the variance of the MVP. The addition of the attitudes in favor of CP in the third step explained an additional 3.5% of the variance of the MVP (p < 0.001). The overall model is significant  = 46.9, p < 0.001) and explain 34.7% of MVP variance. As depicted in Table ß = -0.67, p < 0.001). Then, female parents used less MVP than male parents  and boys experienced more MVP than girls . Parents who had experienced more physical abuse in childhood reported more MVP  and parents who reported more attitudes in favor of CP as well .Concerning the hierarchical regression analyses, the socio-demographic variables explained 27.3% of the moderate violent practices (MVP) variance  and when comparing Switzerland and Togo . The indirect effect through the attitudes in favor of CP was also significant in both cases .Finally, the multiple mediation analysis explored the mediating role of childhood physical abuse and parental attitudes in favor of CP on the link between the cultural context and physically violent practices. First, the direct effect was not significant when comparing Cameroon and Togo (I -0.48, .58; see The first purpose of this study was to document the different forms of parents’ physically violent practices across cultures. The results indicated that physically violent parental practices and attitudes in favor of CP manifest themselves differently according to the three cultural contexts. The second aim was to better understand the influence of the cultural context, the childhood physical abuse, and parental attitudes in favor of CP on physically violent parental practices in Togo, Cameroon, and Switzerland. The findings showed that violent physical practices could be partially predicted by the cultural context, childhood physical abuse, attitudes in favor of CP, child’s gender, and parent’s gender. Moreover, childhood abuse and parental attitudes turned out to play a mediator role on the link between the cultural context and the adoption of physically violent practices.In this study, the cultural context was found to play a role in the disclosure of physically violent parental practices. The percentages of any forms of moderate violence reported by the parents were high in Cameroon and Togo, at more than 90% for both countries, which is slightly higher than the results of Dassa et al.  and LansFurthermore, our results showed that the cultural context seemed to also influence parental attitudes in favor of CP, which supports Bornstein  and RubiThe results of the present study showed that experiencing physical abuse during childhood is associated with the adoption of physically violent practices. This is consistent with the meta-analysis of Assink et al.  which foThe experience of physical abuse during childhood and the context are not the only factors influencing parenting. Indeed, our results showed that parents’ attitudes in favor of CP influence parenting. Parents who had more attitudes in favor of CP were more likely to report adopting physically violent practices toward their children. This result is consistent with those of Clément et al.  and AteaBesides, the parent’s gender appeared to play a role in the adoption of physically violent practices, as well as the child’s gender. Being a male parent was related to using more likely physical violence, which is not completely in line with other findings. For instance, the study of Lansford et al.  across nAs described above, since attitudes toward CP were associated with the adoption of physically violent practices, a modification of these attitudes could be an effective way to prevent the use of physical violence, as Holden et al.  did in tFuture studies could develop more culturally adapted scales to measure violent parental practices, as the conceptualization of these can vary from one context to another. In addition, the conceptualization of violence against children is based on predominantly Western literature. Therefore, it might be interesting to study these different conceptualizations in these contexts through qualitative interviews. Then, future research could include mixed methods, such as observation tasks, to bring out elements that are explored by means other than questionnaires. This could continue with examinations of the needs of parents in these countries, as well as the knowledge and skills of professionals, which is an important step in the implementation of prevention programs in low- and middle-income countries (Brodard & Naudin, The present study has several limitations. The questionnaires were filled out by the parents themselves. Therefore, the figures reported above are based on their perceptions and memories, and not necessarily a reflection of reality. Due to the self-reported style of the questionnaire, the presence of a social desirability bias may be a considerable limitation of this study Krumpal, . In factIn light of current concerns about violence against children and its negative consequences, this study was focused on physically violent parental practices. Given the cultural differences in parenting, a cross-cultural perspective seemed relevant. Since the results showed that culture influences parental attitudes toward CP and physically violent practices, this study highlighted the importance of considering cultural background when examining parental practices. Although violent parental practices in Sub-Saharan African contexts have rarely been investigated, these findings could provide a better understanding of these types of parental practices notably for parents from Togo and Cameroun who move to the West. Moreover, the focus on Togo and Cameroon in this study also helped to differentiate parental practices in these two contexts. However, research in this field is still needed, notably to understand how maltreatment is conceptualized in these contexts. In addition, this research highlighted the mediating role of the experience of violence during childhood and the parental attitudes in favor of CP, providing new findings regarding mechanisms underlying intergenerational transmission in the contexts studied. To conclude, further investigations should be carried out to identify factors involved in violent parenting, to help break the cycle of violence.


Echocardiography is a commonly used and cost-effective test to assess heart conditions. During the test, cardiologists and technicians observe two cardiac phases—end-systolic (ES) and end-diastolic (ED)—which are critical for calculating heart chamber size and ejection fraction. However, non-essential frames called Non-ESED frames may appear between these phases. Currently, technicians or cardiologists manually detect these phases, which is time-consuming and prone to errors. To address this, an automated and efficient technique is needed to accurately detect cardiac phases and minimize diagnostic errors. In this paper, we propose a deep learning model called DeepPhase to assist cardiology personnel. Our convolutional neural network (CNN) learns from echocardiography images to identify the ES, ED, and Non-ESED phases without the need for left ventricle segmentation or electrocardiograms. We evaluate our model on three echocardiography image datasets, including the CAMUS dataset, the EchoNet Dynamic dataset, and a new dataset we collected from a cardiac hospital (CardiacPhase). Our model outperforms existing techniques, achieving 0.96 and 0.82 area under the curve (AUC) on the CAMUS and CardiacPhase datasets, respectively. We also propose a novel cropping technique to enhance the model’s performance and ensure its relevance to real-world scenarios for ES, ED, and Non ES-ED classification. Early diagnosis is critical for effective risk factor management and treatment. Several tests can be performed for cardiac examination, including cardiac magnetic resonance imaging (MRI), echocardiography, and computed tomography (CT). While MRI and CT provide high-quality images2, echocardiography is the most affordable, widely available, and noninvasive technique with a low acquisition time3. Transthoracic echocardiography is the most commonly used method, and it can diagnose several heart diseases.Cardiovascular disease is a major cause of death worldwide, accounting for 43.8% of all deaths annually, particularly in individuals with low socioeconomic status4. Accurate identification of the end-diastolic (ED) and end-systolic (ES) phases is crucial for measuring heart function and chamber size, which help calculate the ejection fraction5, global longitudinal strain, wall thickness, and stroke volume6. However, there are some frames in the echocardiography modality that do not represent either ES or ED phases, which we refer to as Non-ESED frames. Identifying ES and ED phases from a small echocardiography sequence, which consists of ES, ED, and Non-ESED images, can be time-consuming and highly dependent on the interpreter’s expertise  and end-systolic (ES) phases is essential for assessing heart function and chamber size. Traditionally, LV segmentation-based methods10 have been used to identify the ES and ED frames based on the assumption that the smallest and largest LV segmented cross-sections in a cardiac cycle represent the ES and ED frames, respectively. However, these methods require complex preprocessing and are prone to errors due to the noisy nature of echocardiograms. Furthermore, they typically require initialization from user input using active contours and deformable models, which can be time-consuming and impractical for real-world applications. Researchers have also used various image-processing techniques, such as velocity spectrum envelopes11 and Gaussian mixture techniques1 , for automatic interpretation. However, deep learning techniques12, which learn and recognize patterns from data, provide better accuracy and require minimal preprocessing. In this paper, the authors use a convolutional neural network for identifying ES and ED frames in echocardiography images.Research has shown that overworked cardiologists may misdiagnose cardiovascular diseases. According to Gene et al.’sThe focus of this research is on developing a method for automatically classifying the ED, ES and Non-ESED phases in echocardiography imaging without the need for manual measurement or automated segmentation of the left ventricle. The approach is named DeepPhase, which stands for “Deep learning based cardiac Phase detection using echocardiography imaging”. This is the first attempt to detect phases using this approach. The contributions of this study include proposing an effective CNN model for the classification tasks, introducing a custom loss function that enhances the prediction accuracy of the CNN model, providing a new dataset annotated by two expert cardiologists, demonstrating the efficacy of the approach through experiments on benchmark and newly introduced datasets, and introducing a cropping technique to improve performance on the three-phase classification task. The effectiveness of the DeepPhase model was found to be better than relevant state-of-the-art techniques.In this section, we review previous studies that explored computer vision, classical machine learning, and DL techniques for the classification and segmentation of echocardiography imaging. We found that most of the work related to echocardiography images focused on two primary purposes: segmentation of the left ventricle (LV) and classification of cardiac views and phases.13 treated the LV segmentation task as a smoothing problem; accordingly, they proposed an adaptive sparse smoothing technique for LV segmentation from echocardiograms and compared the performance of their method with that of the active contour method. To segment an anterior mitral leaflet, Sultan et al.14 proposed sampling of echocardiographic videos over time by exploring a partly automated evaluation of a scanning line, which provided a new image space as an output, referred to as the Virtual M-mode. However, this method was not fully automatic because to initialize the Virtual M-mode, a doctor needed to manually specify a single point for the posterior wall of the aorta. The recall and precision values of 0.94 and 0.50, respectively, were achieved. However, segmentation oversizing was the limitation of the proposed method, typically caused by improper scanning.Guo et al.15 developed a framework to detect the posterior wall of the LV from echocardiography images by merging a light gradient boosting machine with morphological features considering the PLAX view. They achieved 67% sensitivity and 98% specificity. Li et al.16 proposed a method to realize the exact segmentation of myocardium in echocardiography. This method combined boundary box detection, a statistical shape model, and a random forest algorithm and could be applied to nonmedical images containing large intensity variations. They achieved a standard deviation of 1.43. In addition, Moghaddasi and Nourian17 proposed a model to categorize the severity of mitral regurgitation disease, in which the features were first extracted using the local binary pattern. Then, a support vector machine was used to grade the videos. The dataset considered in this study comprised 102 videos, and 99% accuracy was obtained. Ghori et al.1 proposed an echocardiography view classification method that employed a Gaussian mixture model and supports a vector machine. They introduced the ECHO 1.0 dataset, which contained 637 echocardiography videos. They achieved an accuracy of 90.8%.Melo et al.12 utilized multiple DL models, including UNet, UNet++, and stacked hourglasses, to segment the left ventricular (LV) endocardium and myocardium using the Cardiac Acquisitions for Multistructure Ultrasound Segmentation (CAMUS) dataset. Similarly, Madani et al.18 applied a semi-supervised DL approach, which combined a convolutional neural network (CNN), generative adversarial networks, and UNet, to classify echocardiographic views and segment LV hypertrophy. Nizar et al.19 employed faster region-based convolutional neural networks and a single-shot multibox detector for aortic valve segmentation, while Moradi et al. used UNet and feature pyramid network on the CAMUS dataset for LV segmentation. Barcaro et al.9 used Otsu’s threshold for LV segmentation and ED and ES frame classification for computing LV ejection fraction. Lane et al.20 formulated ES and ED phase detection as a regression problem using a CNN-based model, and Wegner et al.21 used a CNN based on VGG19 for view classification from 2D echocardiography. Litjens et al.22 identified lack of uniform performance evaluation and annotated data as the main challenges in DL-based echocardiography research and suggested involving physicians and medical staff in identifying areas where automation is required. Finally, Dezaki et al.23 explored DL techniques for ED and ES phase detection from four-chamber view echocardiography cine series, using a custom loss function based on mean squared error and a combination of CNN and recurrent neural network modules. The current study is motivated by Dezaki et al.’s23 work, but it differs in several aspects, such as using echocardiography images instead of cine series, and formulating ES and ED detection as a classification problem instead of a regression problem. The study justifies the use of echo images due to their efficient storage and transmission, as well as their suitability for accurate diagnosis by cardiologists and computer systems.The section titled “This study is expected to provide a foundation for achieving impressive results for cardiac phase detection in the apical two-chamber view and apical four chamber view. Unlike previous research, we carried out the task without LV segmentation, using only a custom CNN architecture and a dataset of 1000 images.This section is divided into two subsections. We discussed the ES and ED phase classification methodology in the first subsection. Afterwards, we discussed the methodology for ES, ED and Non-ESED frames classification.24. The high level architecture of DeepPhase is given in Figs.We divided the methodology into two phases: model training and model testing. Each phase has several substages, e.g., preprocessing and data augmentation, optimizing and testing the DeepPhase model, and calculating results12. The lack of comprehensive and publicly available data for echocardiography motivated the creation of the CAMUS dataset. It includes the reports of 500 patients with ED and ES frames in four-chamber and two-chamber views, acquired from the St. Etienne hospital in France using GE Vivid E95 ultrasound scanners with a GE M5S probe. The dataset comprises good-, poor-, and medium-quality images, and labelling was performed by three cardiologists.The DeepPhase model was trained and validated using the CAMUS dataset, which was developed by Leclerc et al. in 2019To improve the generalization of the CNN network and reduce overfitting, data augmentation was used. Multiple variants of each training image were generated by varying augmentation hyperparameters across specified ranges. Four augmentation methods were employed:Rescale Pixel values in gray-scale images range from 0 to 255. To transform every pixel value from the range (i) Shear A shear intensity of 0.2 was set to distort the image along an axis and rectify or create the perception angles. This augmentation method models how human beings see things from different angles.(ii) Zoom A zoom range of 0.2 was set to randomly zoom the image and add new pixels.(iii) Horizontal flip The augmented images were generated by flipping the original image horizontally.(iv) 25. In this study, we propose a 9-layer CNN architecture, including input, convolutional, pooling, fully connected, and output layers, specifically designed for cardiac phase detection24. The CNN architecture consists of three convolutional layers, three pooling layers, and a single fully connected layer. Each convolutional layer applies a  convolution kernel to the input to generate feature maps. The rectified linear unit (ReLU) is used as the activation function to prevent vanishing gradients during backpropagation and introduce nonlinearity to the convolution layer output. Figure A typical CNN architecture consists of convolutional, pooling, and fully connected layersModel optimization involves hyperparameter selection and loss function construction, which are described below.Hyperparameter selection The hyperparameter settings are described in “(i) Loss function The loss function approximates the model’s loss so that the weights can be adjusted to reduce the loss in the next epoch. To reduce training loss, we employed regularization techniques that efficiently decrease the model’s variance without significantly increasing its bias. The proposed loss function is based on the mean squared error (MSE) and consists of two parts represented by Eqs. ((ii)  by Eqs.  and 2)..Loss funIn this research, the DeepPhase model’s performance was evaluated on two datasets. The first dataset consisted of 100 images from the CAMUS dataset, which was already partitioned by the dataset authors. The second dataset, called the CardiacPhase dataset, was fully annotated and developed specifically for this study. It contains echocardiograms from 35 patients who were treated at the CardioLife diagnostic center in Hyderabad, Pakistan. No specific data selection method was used to ensure clinical realism, resulting in a highly heterogeneous dataset of both pathological cases and image quality, typical of routine clinical practice data. The dataset comprised 70 images and was used as a testing dataset to evaluate the DeepPhase model’s performance. The echocardiography data were obtained between June and September 2020, and two cardiologists annotated the data in this dataset. The data were captured in the ED and ES phases in the two-chamber view for each patient. The data collection was performed following the principles of the Declaration of Helsinki.To ensure that the images from both datasets appeared similar, several preprocessing techniques were applied because they were obtained using different scanners, probes, and protocols. The images were cropped to eliminate unwanted labels and lines, and the image size was adjusted to 150 x 150 x 1, and the images were denoised using linear and mean filters. The cropping process was necessary to ensure data anonymity. Figure This section discusses the methodology followed for ES, ED and Non-ESED classification. The overall process, shown in Fig. 26 created the EchoNet-Dynamic dataset containing 10,030 echocardiography videos covering a variety of typical lab imaging acquisition conditions. All images have labelled measurements, including LV volume at end-systole and end-diastole, ejection fraction and expert tracings of the left ventricle. These measurements are obtained by a registered sonographer and verified by a level 3 echocardiographer. The dataset contains apical-4-chamber echocardiography videos from patients who underwent echocardiography tests between 2016 and 2018 at Stanford University Hospital. Each video was cropped to delete the text and information outside of the scanning sector, and the resulting images were downsampled into standardized 112x112 pixel videos.Ouyang et al.26 for each video, and the Non-ESED frame is chosen randomly from the rest of the frames.We have randomly selected 60 videos from training, 20 videos from validation and 20 videos from the test set of the EchoNet Dynamic dataset. We have chosen three frames from each video, i.e., ES, ED and Non-ESED. The ES and ED frames are labelled by the authorsn is the total number of images.To improve the performance of the model, we cropped all the frames to enable the model to extract the most significant features from the frames. We clipped the images so that only the section which helps differentiate between ES, ED and Non-ESED frames is fed to the model. We utilized the metadata provided with the ‘EchoNet Dynamic’ dataset to crop the images. The metadata contains tracings of LV at the endocardial border. The expert tracings are represented by a collection of paired coordinates corresponding to each human tracing. The initial pair of coordinates show the direction and length of the long axis of the LV as represented by a vertical line in Fig.  in Eqs.  and 5)..\documenquations , 8), an, an\docuWe used the CAMUS, CardiacPhase and EchoNet Dynamic datasets . Here, the most frequently used configuration for pooling layers was a pool size of . With these settings, each pooling layer discarded 75% of the activations. Siddiqi’s27 proposed architecture uses a dropout layer (with a dropout rate of 0.2) after each pooling layer. In our proposed architecture, we did not use dropout layers. The primary purpose of a dropout layer is to prevent the model from overfitting. We employed several techniques to avoid overfitting in the DeepPhase model, e.g., the regularization of the loss function and data augmentation. Finally, we employed a single dense layer. By contrast, Siddiqi’s model27 uses two dense layers.The key differences between the DeepPhase model and Siddiqi’s20. The researchers performed ES and ED detection by combining CNN and LSTM model on EchoNet dataset.We have compared the performance of DeepPhase model for ES, ED and Non-ESED classes with the model proposed by Lane et al.Wegner et al. proposed a model for view classification from 2D echocardiography. Their model consists of CNN using the VGG19 as a base model.We have evaluated the performance of the DeepPhase model with pretrained models such as VGG16, ResNet, EfficientNet and ModeileNet.(i)DeepPhase-MAE The DeepPhase model uses the mean absolute error (MAE) loss function (instead of the custom loss function).(ii)DeepPhase-CrossEntropy The DeepPhase model uses the cross entropy loss function (instead of the custom loss function).(iii)DeepPhase-MSE The DeepPhase model uses the mean squared error (MSE) loss function (instead of the custom loss function).(iv)DeepPhase-L1 Regularizer We set the value of 0.1 for L1 regularizer and train it with MSE loss function. These parameters, namely the value of 0.1 for L1 regularizer and training with MSE loss function, were chosen based on their superior performance compared to other parameters. The decision was made after evaluating the competing models and their corresponding results, as shown in Table (v)DeepPhase-L2 Regularizer In this experiment, we set the value of 0.1 for L2 regularizer and train it with MSE loss function.The publishing authors divided the CAMUS dataset into training and testing sets. There are 900 frames or images (two images per patient) in the training set and 100 frames in the testing set. From the 900 images of the training set, we allocated 90 images as a validation set. The validation set helps avoid overfitting and assists in parameter selection. The model was optimized for other hyperparameters such as learning rate, batch size, and optimizer using the randomized grid search method. Here, the Adam optimizer was used with a batch size of 10 and a learning rate of 0.00001.The compared models were coded in Python  using the open-source Keras (version 2.1.5) library as the backend and open-source TensorFlow (version 2.5.0) library for the CNN algorithms. The models were executed using the GPU runtime setting of Google Colab on the Google cloud.The performance of each model in all experiments was evaluated based on the area under the receiver operating characteristic curve (AUC) and loss.24 and then evaluated the model’s performance for ES, ED and Non-ESED cardiac phases.We have divided this section into two parts. Initially, we discussed the performance of the DeepPhase model for ES and ED classificationWe have compared the performance of the DeepPhase model with relevant state-of-the-art models, and the results are given in the following subsections.27 and all four pre-trained models on the CAMUS and CardiacPhase datasets. The performance of ResNet is satisfactory for the CAMUS dataset with an AUC of 0.92, while for the CardiacPhase dataset, VGG-16 achieved an AUC of 0.79. The performance of MobileNet is equally suitable for both CAMUS and CardiacPhase datasets, with the AUC of 0.92 and 0.8, respectively. Siddiqi’s CNN27 performed better on the CAMUS dataset than the CardiacPhase dataset, with an AUC of 0.7 and 0.61, respectively.The comparisons of each model’s performance are shown in Table In our custom loss function, shown in Eq. , \documeentclass1pt{minimaentclass1pt{minimaIn this section, we carried out the following experiments to prove the generalization of DeepPhase.In this experiment, we evaluate the performance of our model when trained with two chambers and tested with four-chamber viewpoints (and vice-versa). We utilized the data from the CAMUS dataset, and hyper-parameters have been adopted from the above apical two chamber view experiments.27 achieved the highest AUC of 0.87, respectively. The second bar in Fig. 27 achieved the highest AUC of 0.85. The third bar of the graph visualizes experiment 03, where the DeepPhase model is trained over the apical four chamber viewpoint and tested over the apical two chamber viewpoint of the CAMUS dataset. The DeepPhase model achieved the highest AUC of 0.9 with loss = 0.08. The DeepPhase performance declined from 0.97 to 0.9 when trained over an apical two chamber view. It is noticeable here that the DeepPhase model performed consistently well when trained over an apical four chamber view. After learning from this viewpoint, the model successfully identified ES and ED frames from another viewpoint. This observation can be useful when no or less data is available for one of the viewpoints. In experiment 04, we trained the model over the apical four-chamber viewpoint and tested it over the CardiacPhase dataset, which contains apical two chamber ES and ED frames. Afterwards, the fourth bar of the graph shows that the highest AUC achieved by the DeepPhase model is 0.72, while the other model achieved 0.62. All of the above experiments confirmed the performance consistency of the DeepPhase model not only on daily clinical data but also another clinically significant cardiac views.The first bar in Fig. To further evaluate the performance of the custom loss function, we performed the following two experiments.28. A model with large weights can signify an unsteady network where small changes in the input can lead to significant changes in the output29. This will result in poor performance when predicting new data. Our custom loss function results in smaller weights and a simpler model. Figure Deep learning models with smaller weights result in more stable training and are less likely to overfitIn order to show the effectiveness of the custom loss function, we compared its convergence rate with other traditionally used loss functions such as MSE, MAE and binary cross-entropy. Figure We evaluated the model performance in this experiment when trained and tested over ES, ED and Non-ESED frames. We used the mean value of 5-fold cross-validation for the results reported in this section.27, VGG16, MobileNet, EfficientNet, ResNet, Lane et al.20, and Wegner et al.21 for ES and Non-ESED frames classification.The Table 20 and Wegner et al.21 achieved AUC scores of 0.71 and 0.76, respectively. Lane et al.28 has the precision of 0.72, indicating that it performed better at identifying true positives among the predicted positives than Wegner et al.21. The remaining models, including CNN proposed by Siddiqi27, VGG16, MobileNet, EfficientNet, and ResNet, has lower performance scores compared to DeepPhase, Lane et al.20, and Wegner et al.21.Among these models, DeepPhase achieved the highest AUC score of 0.8, indicating better classification performance than other models. DeepPhase also has the lowest Loss value of 0.15, indicating that it has the lowest difference between the predicted output and actual output. Additionally, DeepPhase has the highest Recall of 0.75, indicating that it was better at identifying positive samples in the dataset. Lane et al.The Table 21 has the AUC score of 0.81, and Lane et al.20 has the AUC score of 0.8. These models also has high Precision scores of 0.71 and 0.7, respectively, indicating that they performed well in identifying true positives among the predicted positives. EfficientNet also performed well in this dataset, achieving an AUC score of 0.76 and Precision score of 0.7. CNN proposed by Siddiqi27, VGG16, and ResNet has AUC scores ranging from 0.68 to 0.71, with Recall scores ranging from 0.65 to 0.69.Wegner et al.27 has the lowest AUC and precision. Among the other models, Wegner et al.’s21 model performed better than Lane et al.’s model20. EfficientNet and ResNet have comparable performance while MobileNet and VGG16 models have lower AUC and precision than other models.In Table 20 for cardiac phase detection. The imbalanced class problem would arise because Non-ESED class would be greatly over-represented. Another study30 proposed a binary classification approach, which labeled all frames in the diastole phase as 1 and systole phase as 0. However, this approach can overlook high-level spatial and temporal markers and crucial physiological differences throughout the entire cardiac cycle, leading to lower accuracy and suboptimal model performance. By randomly selecting Non-ESED frames from the rest of the frames and ensuring that each video has an ES, ED, and Non-ESED frame, our approach provides a more balanced and unbiased dataset that can capture the complexity and variability of the cardiac cycle more accurately. Therefore, this research work represents a significant improvement over the previous study and can potentially enhance the accuracy and reliability of cardiac phase detection modelsOne of the key contributions of the proposed approach is that it provides a solution to the imbalanced classification problem faced by Lane et al.20 in the context of detecting end-systolic (ES) and end-diastolic (ED) frames in echocardiography videos. As noted in26, there can be a lot of variation in the images due to differences in patient anatomy and imaging conditions. The DeepPhase classification model can better handle this variability by focusing on the most relevant features for each class. Additionally, the DeepPhase model is more scalable than a regression model, as it can be trained on a smaller dataset, and it may be better at generalizing to new data than a regression model, as it focuses on the separation of classes rather than on fitting the data to a specific function32. These advantages are supported by the results presented in the paper, and they highlight the potential of the DeepPhase model in improving the accuracy and reliability of cardiac phase detection models.In the results section, it is evident that the DeepPhase model has several advantages over previously proposed regression models30 that were constrained by fixed length videos containing only one cardiac cycle.Moreover, by randomly selecting three frames from each video, including ES, ED, and Non-ESED frames, we created a dataset that addressed the challenge of variable length videos. Specifically, this approach enabled us to train and test our model on a set of videos with different lengths while ensuring that each video contained the necessary frames to classify the cardiac phase accurately. This helped us overcome the limitations of previous studiesWe asked the echocardiographer’s opinion about some of the images misclassified by the model, as shown in Fig. We conducted the following analysis as an ablation study to compare the performance of DeepPhase model with other traditional loss functions. As part of this study, we also performed binary classification for ES, ED and Non-ESED with and without cropped images.In this study, we show the effectiveness of customizing the loss function. The training AUC of DeepPhase with customized and traditional loss functions at epochs 30, 40, and 50 are given in Fig. In the experiment, we explored the best results on CAMUS when the In this study, we report the effect of image cropping by comparing competing models performance on uncropped images. We evaluated the model performance in this experiment when trained and tested over (1) ES and Non-ESED (2) ED and Non-ESED classes and (3) ES, ED and Non-ESED. We used the mean value of 5-fold cross-validation for the results reported in this section. Table Table Looking at the Table Furthermore, the precision score increased from 0.63 to 0.76, and the recall score increased from 0.7 to 0.78 on the cropped dataset, indicating that the model’s ability to correctly identify positive samples improved with the cropped images. we can also see that the performance of other models improved on the cropped dataset, but the magnitude of improvement varies among the models.In this paper, we propose a method to directly solve the ES, ED, and Non-ESED phase detection problem without the need for LV segmentation. The DeepPhase model successfully classifies frames from unseen data, and experimental results confirm that the proposed MSE-based loss function outperforms other standard loss functions. To mitigate the lack of publicly available data for echocardiography, we introduce a dataset of 70 images, which we use as a test set in our experiments. To test the generalizability of the DeepPhase model, we also experiment with the apical four chamber view and obtain comparable results. Additionally, we introduce a cropping technique that can be useful for other medical images. However, to further improve the results presented in this paper, we acknowledge the need for a larger dataset to train the DeepPhase model. Therefore, in future work, we plan to develop a larger dataset for echocardiography views and train the DeepPhase model using transfer learning techniques to enhance its performance.


Background and objectives: Herpes zoster (HZ) is caused by the reactivation of a pre-existing latent varicella zoster virus, which is one of the viruses that causes hearing loss, and hearing loss may occur due to a systemic immune response even if it does not invade the auditory nerve. This study aimed to determine the correlation between sudden sensorineural hearing loss (SSNHL) in older adult patients who received HZ treatment. Materials and Methods: We used the cohort data of patients aged 60 years and above  between 2002 and 2015 provided by the National Health Insurance Service. The patients were divided into two groups: those who were diagnosed with HZ between 2003 and 2008  and those who had not been diagnosed with HZ between 2002 and 2015 . Results: In the main model  adjusted for sex, age, and income, and the full model  adjusted for all comorbidities, group H had a lower risk of SSNHL than group C. Conclusions: This study showed that patients who received HZ treatment had a lower incidence of SSNHL within five years after diagnosis. Herpes zoster (HZ) is a disease in which the varicella-zoster virus (VZV) causes chickenpox in childhood, and the virus does not disappear, even after treatment is complete, and remains dormant in the ganglion of the spinal cord, enters the nerve, and resumes activity when immunity is low ,2. It isThe number of patients with HZ is increasing due to the growth in the older adult population. Additionally, the numbers of HZ neuralgia and other complications are equally increasing ,4. One sThe cause or mechanism of idiopathic SSNHL is unknown, but if HZ is diagnosed and treated, antiviral or steroid treatment may affect the incidence of viral or idiopathic SSNHL. Therefore, we hypothesized that the incidence of hearing loss would be lower in patients treated for HZ with antiviral and/or steroidal therapy. A study that followed up on the incidence of SSNHL for two months in patients who had recently suffered from HZ reported no correlation between the two diseases. However, the follow-up period was too short to confirm the effect . This stThis study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board  of the Korea University Ansan Hospital.n = 624,646) in 2002, were provided. The cohort database included basic demographic information, socioeconomic data, medical comorbidities, diagnostic codes using the International Classification of Disease-10 (ICD-10), status of medical resource utilization , status of clinic and elderly long-term nursing service, and death records. The data were supplied after patient de-identification by the NHIS.The Korean government has implemented a compulsory National Health Insurance Service (NHIS) since 1989, which is a nationwide health care system that provides medical services for almost all of the country’s residents. All demographic data, including age and sex, are collected under the NHIS according to the patient’s Korean ID number. As the Health Insurance Review and Assessment Service (HIRA) was developed for medical billing purposes at the NHIS, all medical acts including diagnosis, physical and laboratory examination, treatment, prescription, nursing acts, and hospitalization are recorded relatively precisely in a computerized database along with the individual’s Korean ID number. Data collection was performed using the elderly cohort data provided by the NHIS between 2002 and 2015. The qualified individuals, those who were over the age of 60 (n = 4196). The patients were divided into two groups: those who had received the first diagnosis of HZ (ICD-10: B02) and received any treatment between 2003 and 2008 , and those without a diagnosis of HZ . We followed the medical records of the study participants for a minimum of 7 years and a maximum of 13 years. The occurrence of SSNHL in the patient was confirmed by ICD-10: H912. It is defined as the sudden onset of hearing loss of at least 30 dB in three consecutive frequencies in an audiometry test within 72 h. Among them, only patients who underwent an audiometry test were included. In group H, patients who had been diagnosed with SSNHL prior to a diagnosis of HZ or who had died between 2003 and 2008 were excluded (n = 669). In group C, those newly diagnosed with HZ between 2009 and 2015 were excluded . Finally, 1:1 matching between the two groups was achieved according to age, sex, and income , as shown in We included 624,646 patients who were aged 60 years and above in 2002 in this study. The exclusion criteria include patients who had already been diagnosed with HZ before 2002 or those who had died during the follow-up period  for SSNHL incidence. In this analysis, the main model , full model , and main with each additional listed comorbidity model were used. ® ver. 9.4  and R software version 3.3.2  were used for all the statistical analysis. p values less than 0.05 were statistically significant. SASp = 0.005). The rate of a diagnosis of comorbidities, such as myocardial infarction, cerebrovascular disease, rheumatic or connective disease (autoimmune disease), diabetes mellitus, moderate or severe renal failure, otitis media, and hypertension, was higher in group H than group C (p < 0.05).p < 0.001). The cumulative incidence of SSNHL was found to be significantly higher in patients without HZ (group C) than in patients with HZ (group H).p < 0.005). However, p < 0.001).p < 0.001) adjusted for sex, age, and income, and the full model  adjusted for all comorbidities, group H had a lower risk of SSNHL than group C. Similar results were found in the sensitivity analysis of the potential confounding variables caused by each comorbidity . Gender stratification analysis showed that women had a significantly lower risk of developing SSNHL in group H than in group C . Age stratification analysis showed a significant association between the incidence of HZ and SSNHL in patients aged 70–80 years and those above 80 years .In the elderly database, the incidence of SSNHL in HZ patients was higher than in group C and the cumulative incidence of SSNHL was found to be significantly higher in patients without HZ (group C) than in patients with HZ (group H). However, in the multivariable Cox regression model adjusted for comorbidities, there was a significantly lower incidence of SSNHL in group H than in group C, and the risk of SSNHL was significantly lower in females and patients above 70 years.HZ, also known as shingles, is characterized by a blistering rash that occurs in bands along the dermatome and causes severe pain ,11. DepeThe lifetime expected risk of being diagnosed with HZ in the general population is approximately 30%, and the risk increases rapidly after age 50, up to 50% for those over 80 years . There iSSNHL is a sudden onset of sensorineural hearing loss ≥ 30 dB or more, at least three contiguous audiometric frequencies, within 72 h . The preWhen summarized into broad categories, the known causes of SSNHL include infectious, autoimmune, metabolic, neoplastic, neurologic, otologic, toxic, and traumatic . Among tIn general, the treatment includes the use of corticosteroids, which are administered through oral medication or injection behind the eardrum ,14. SSNHVZV is a double-stranded enveloped DNA virus that belongs to the Herpesviridae family. VZV is a highly contagious virus transmitted by air droplets when an infected person coughs or sneezes, or through direct contact with herpetic vesicle exudates . Some paPatients with T-lymphocyte-macrophage-mediated immune system deficiency have a more severe viral spread, which can cause complications in the visceral or nervous system beyond the ganglion, nerve, and dermatome units ,11. The Ramsay Hunt syndrome, or HZO, is caused by the reactivation of latent VZV in the geniculate ganglion ,35,36. TSeveral previous studies have reported no association between idiopathic SSNHL and HZV infection ,37. HoweIn this study, we analyzed the long-term follow-up data from a minimum of seven to a maximum of 13 years. Considering that the HZ treatment effect may be short-term, the follow-up period was analyzed by dividing it into less than one year, between one and five years, and more than five years. In addition, multivariate analysis was also performed, including various underlying diseases that could affect the results. The therapeutic role of corticosteroids and/or corticosteroids with antiviral agents for SSNHL has not been fully proven yet, although corticosteroids are known to alleviate hearing loss and reduce cochlear damage in patients receiving this treatment by strongly suppressing immune-mediated inflammatory reactions ,38,39. AThe live attenuated vaccines against HZ are used worldwide and are recommended for people over the age of 50 with an increasing incidence of HZ. It has been reported that the HZ prevention effect is about 53% when targeting the population aged 60 years or older, and the morbidity of chronic PHN is reduced in 63% of cases .This study analyzed a large national population using reliable data provided by the National Health Insurance Service of Korea. As the entire population is obligated to subscribe to the national health insurance and it is managed in Korea, individual medical data are not omitted or duplicated, and the records are thoroughly coded. Although the data are reliable, there are some limitations to this study. First, as this study is a retrospective cohort study, it is difficult to explain the exact mechanism by which the factors related to HZ treatment reduce the incidence of SSNHL. This is because there is a commonly used treatment protocol, but not all patients receive the same treatment. Second, many underlying diseases were considered and applied when matching the control group. However, some diseases that are not common were not considered. Third, cases of spontaneously resolved SSNHL may have been missed in this study population. In our data, the incidence of SSNHL was approximately 6% in patients over 60 years old, which was approximately 0.5% when converted to the total population. In previous studies, the incidence of SSNHL was 1.5–1.7%. However, our study showed a low incidence. This could be due to differences in the prevalence rates by country and race. Fourth, our populations have many comorbidities; therefore, many of these patients received a lot of medication for various comorbidities during this five-year follow-up period, which may influence the incidence of SSNHL. Therefore, we included comorbidities (diseases described in In conclusion, the incidence of SSNHL in patients with HZ is reduced for up to five years after diagnosis. According to our findings, a series of HZ treatments or an acquired immunity to VZV influenced the reduction in the incidence of SSNHL within five years of HZ diagnosis. As our study was a retrospective cohort study, prospective randomized control studies to confirm our findings on the effect of HZ on SSNHL are recommended in the future.


Bladder cancer, one of the most prevalent cancers globally, can be regarded as considerable morbidity and mortality for patients. The bladder is an organ that comes in constant exposure to the environment and other risk factors such as inflammation.In the current study, we used machine learning (ML) methods and developed risk prediction models for bladder cancer.This population‐based case–control study is focused on 692 cases of bladder cancer and 692 healthy people. The ML, including Neural Network (NN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Gradient Boosting (GB), and Logistic Regression (LR), were applied, and the model performance was evaluated.The RF  had the best performance, and the RT  was in the next rank. Based on variable importance analysis in RF, recurrent infection, bladder stone history, neurogenic bladder, smoking and opium use, chronic renal failure, spinal cord paralysis, analgesic, family history of bladder cancer, diabetic mellitus, low dietary intake of fruit and vegetable, high dietary intake of ham, sausage, can and pickles were respectively the most important factors, which effect on the probability of bladder cancer.Machine learning approaches can predict the probability of bladder cancer according to medical history, occupational risk factors, and dietary and demographical characteristics. According to the global cancer observatory, bladder cancer is the 10th most frequent cancer in the general population.Machine learning (ML) approaches is a branch of computer science in medical research. Being tremendously on the rise, many researchers apply different ML methods to develop models to predict the risk of diseases, make diagnostic criteria more accurate, or even diagnose the outcome of treatment based on different factors. Knowing the risk factors might help to strengthen the primary prevention plans of healthcare systems to achieve their goals and make them more accurate. The goal of the study, which included a relatively large sample size population, was to employ ML strategies to determine the influence each risk factor has on bladder cancer.2This population‐based case–control study includes 692 cases of bladder cancer and 692 healthy people. Bladder cancer patients were selected from the cancer registry system, and one of the right door neighbors in each case, matched based on sex and age, was recruited as a control. More details are presented in a previously published article.2.1The statistically missing values were synthesized in the data preprocessing phase. The large dataset includes 1384 samples, and 27 features and bladder status were used as response variables. The binning discretization mechanisms were carried out to convert features with more discrete values into categorical values. Moreover, normalization, centering to the mean and scaling to a standard deviation of 1, was served in continuous variables. Due to the equal sample of health and patients' number, the distribution was considered balanced.In the study, Neural Network (NN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Gradient Boosting (GB), and Logistic Regression (LR) were developed. The multi‐layer perceptron network was applied for NN modeling. The activation function of the rectified linear unit function and weight optimization of the stochastic gradient‐based optimizer was served. The L2 penalty parameter was considered as 0.0001. A DT approach was applied, which divided data according to class purity. The RF makes a set of decision trees by bootstrap sampling from the training data. The dataset was split into a 5‐fold cross‐validation method in the approximate ratio of 4:1 to the derivation and validation sets. The four folds and one‐fold were made to train and test models, respectively. In other words, data were randomly divided into 80% and 20% for the training and the testing sets each time. Regularization was applied to handle the accuracy difference between train and test datasets, which means the model, may not generalize as well for the test set as the training set. The regularization terms were used as follows: including L2 penalty parameter for NN, Ridge L2 (standard regularizer) for stochastic gradient descent, and logistic regression. Also, for reducing the overfitting of models, the grid‐search method was applied when tuning hyperparameters and trying to select the best combination of parameters for the data. In addition, feature selection was performed to reduce the features and avoid overfitting. The flowcharts of data preprocessing and model selection are shown in Figure 2.2Descriptive statistics were reported as mean ± SD for continuous variables and frequency and percentage for categorical ones. The relationship between categorical variables was explained using the Chi‐square test, and the quantitative variable was tested using the independent sample t‐test. The significance level of 0.05 was considered in the analysis. The missing data were imputed using model‐based imputer methods. In this way, a separate model is constructed for each attribute. The default model is 1‐Nearest neighbor learner, which takes the value from the most similar example. Lifestyle and occupational risk factors for bladder cancer were explored using the ML approaches. The calibration plot, the area under curve (AUC) of the receiver operating characteristic (ROC) curve, precision, sensitivity, specificity, and F1 indexes were obtained to determine model performance. In the visualization, the best model was selected to calculate the probability of bladder cancer using lifestyle and occupational risk factors. All ML strategies were implemented using Orange software version 3.21.0, which builds data analysis workflows visually. The visual workflows in Orange and the dataset supporting this study's findings are available from the corresponding author upon request.3p‐value<.05). Occupational factors did not present a significant association in univariable analysis. In the case of lifestyle items, smoking, opium, and analgesics were significant factors for bladder cancer . Finally, fruit and vegetable use and pickles had a significant effect on bladder cancer . Histograms for all numerical features were presented in A total number of 1384 subjects, including 692 bladder cancer patients and 692 healthy people, were included in the study. The basic information and characteristics of the samples have been summarized in Table Subsequently, various ML algorithms were applied to assess the models for predicting bladder cancer, considering lifestyle and occupational risk factors. Table The ROC curves in Figure Finally, a visualization of one of the best trees in random forest has been illustrated in Figure 4In this case–control study, we used ML methods to develop a risk prediction model for bladder cancer according to lifestyle and occupational risk factors. The univariable results presented that half of the 12 important factors of bladder cancer were related to past medical history  and family history of bladder cancer. Finally, three factors depend on bladder cancer; the lifestyle items: smoking, opium, and analgesics use. The last two ones were fruits and vegetables and pickles consumption per day, which belong to the dietary factors category. In addition to the importance of acquiring a complete patient history, lifestyle, and dietary risk factors must be considered. Having a better understanding of these risk factors will prove to be a great asset to the prevention and management of bladder cancer in the future.In 2018, a systematic review study divided the risk factors into six major groups: smoking, occupational exposure, dietary factors, environmental carcinogens, gender, race, and socioeconomic status.Dietary factors contain numerous variables that may lead to or prevent bladder cancer depending on their quantity and quality. In our study, the daily consumption of fruits and vegetables, and pickles was also associated with a significant reduction in the risk of bladder cancer, with an importance of fifth position in Figure Finally, the past medical conditions of patients must be considered when estimating the risk of bladder cancer. According to the results, we discovered recurrent UTI, neurogenic bladder, and bladder stones were all significantly associated with bladder cancer in Table In recent years, with the development of technology, studies have used deep learning (DL) and ML models to come up with programs that can help in the diagnosis of cancers, predict their prognosis and survival outcomes, and selection the best route of treatment for patients.The study has some strengths and limitations. We used a relatively large sample size of patients randomly chosen from the Iranian Cancer Registry system and developed risk prediction models. One limitation of our study was that we could not evaluate the effect of other risk factors. Another limitation is that dietary and lifestyle factors are greatly influenced by culture, ethnicity, and geographical and historical factors, and our study was conducted on the Iranian population, so the results might differ in various areas.5Machine learning approaches can predict the probability of bladder cancer according to medical history, occupational risk factors, and dietary and demographical characteristics.Conceptualization, N.B., H.A.; Acquisition of Data, N.S. and M.T.; Methodology, analysis, N.B., A.T., H.A.; Writing‐original draft preparation, K.S, M.P., M.T. and N.B.; Writing‐review and editing, N.B., A.T., H.A., and N.S.; Administrative and Supervision, N.B. H.A.The authors declare no conflicts of interest.The study protocol was approved by the Ethics Committee of the Urology and Nephrology Research Center at Shahid Beheshti University of Medical Sciences. Informed consent was obtained from all subjects, and all methods were performed in accordance with the Declaration of Helsinki.Supplementary 1. Histograms for numerical features.Click here for additional data file.


Complementary and Alternative Medicine (CAM) interventions may prove to be an attractive option for the treatment of depression. The aim of this scientometric analysis is to determine the global scientific output of research regarding managing depression with CAM and identify the hotspots and frontiers within this theme.R-package, VOSviewer, and CiteSpace.Publications regarding the utilization of CAM for treating depression were collected from the Web of Science Core Collection from 1993 to 2022, and analyzed and visualized by Bibliometrix Journal of Alternative and Complementary Medicine was the most productive periodical. The CAM therapies of most interest to researchers were acupuncture and body–mind techniques, such as yoga, meditation and mindfulness. Systematic review and meta-analysis are commonly used methods. “Inflammation,” “rating scale” and “psychological stress” were identified as the most studied trend topics recently.A total of 1,710 publications were acquired. The number of annual publications showed an overall rapid upward trend, with the figure peaking at 179 in 2021. The USA was the leading research center. Totally 2,323 distinct institutions involving 7,638 scholars contributed to the research theme. However, most of the cooperation was limited to within the same country, institution or research team. The Managing depression with evidence-based CAM treatment is gaining attention globally. Body–mind techniques and acupuncture are growing research hotspots or emerging trending topics. Future studies are predicted to potentially investigate the possible mechanisms of action underlying CAM treatments in reducing depression in terms of modulation of psychological stress and inflammation levels. Cross-countries/institutes/team research collaborations should be encouraged and further enhanced. DepressIn primary care, depression is most often managed with antidepressants, psychotherapy or a combination of both . AntidepSt John’s Wort, aromatherapy, nutritional supplements and yoga often being used to address moderate depression  is also popularly taken up by patients who are depressed , 19. CAMpression .Over the past few decades, the use of CAM has grown in popularity and orthodox healthcare providers have acknowledged the value of the existence of CAM. This is attributed to the surge in CAM research productivity, that is the increase in the amount of published literature available that details therapies . The ideMany narrative , 33 and Yagya  of journals, and the h-index of scholars from the WoS. The quartile in JCR and IF are widely used as two measures of the prestige of a journal  quartile and impact factor , number of citations (Nc), and h-index] were extracted by the software. The Np and Nc are usually used to quantify productive capacity and demonstrate impact, respectively  independently examined the qualifying papers. The full records of the resultant publications were then imported to Bibliometrix package 4.1.2 in ectively ; and thep and Nc .R environment, it is highly flexible and can be rapidly upgraded and integrated with other R packages to support statistical operations  and Australia (117/6.8%). Amongst these 10 countries, the countries with the most research cooperation with other countries were, in order, the USA, China and Australia. Italy, although lower than other countries in number of publications, was higher than Iran, South Korea and Brazil in the closeness of research cooperation with other countries .The USA was also the country/region where papers were most cited, with papers referenced 24,205 times. Although there were less papers published in Brazil than in Iran and South Korea, the total citations for papers published in Brazil was much higher than citations for papers published in the other two countries .3.3.Totally 2,323 distinct affiliations were involved in the publication of current research theme. Among them, 979 institutions published papers as the corresponding author affiliation. In these 979 institutions, 11 institutions published a minimum of 10 papers. Most of these 11 institutions were located in the USA, Australia or China, which is consistent with the findings derived from the contributions of countries/regions. Evidently, these institutions play a crucial role in expanding national influence. Harvard University (USA) was found to be the most productive institution with 21 papers, followed by the University of Duisburg-Essen (Germany) and the Chengdu University of Traditional Chinese Medicine (China) each with 18 papers. On average, papers from Harvard University (USA) were cited 444.1 times, which was much higher than the number of citations for other institutions .In addition, 747 links were found among the top 100 most productive institutions in the co-author analysis network .3.4.h-index at 13. Furthermore, among the top 10 authors, Edzard Ernst was also the first scholar to publish a paper in the research field. We notice that the top 10 authors were mainly from the USA, Australia and Germany. It suggests that there are more excellent researchers focusing on CAM management of depression in these three countries.A total of 7,638 authors contributed to the research regarding CAM therapies for depression, and 11 authors published at least 10 papers. The top ten most productive authors were listed in Price’s Law  represents the number of papers by the author with the most publications in the field), authors who have published equal to or more than three papers were determined as the core authors in the current research field. A total of 219 authors (publications ≥3) were included in a network map of authors and were grouped into nine clusters , followed by Complementary Therapies in Medicine (n = 41) and Evidence-Based Complementary and Alternative Medicine (n = 40). The journal with the highest Nc  was JAMA - Journal of the American Medical Association (IF: 120.7), although it only published five papers within the current topic. Half of the top 10 active journals were in the CAM field, and the remaining journals were comprehensive medical journals, or journals in the fields of psychiatry, psychology, or oncology. According to the JCR 2022 standards, most of these productive journals ranged from Q3 to Q1. We note that Evidence-Based Complementary and Alternative Medicine (JCR Q3 and IF: 2.650 in 2021–2022) was moved out of the SCIE index in the 2023 update to its annual Journal Citation Reports.Collectively, 658 academic journals published articles on research regarding CAM therapies for depression. The top 10 most productive journals published 337 papers on this theme, accounting for 19.7% of all publications . JournalJournal of Alternative and Complementary Medicine has been publishing papers regarding CAM therapies for depression since 1998 and continues to do so today. The peak in the number of articles published on this topic, in this journal, occurred in 2018. After that, it has lost its numerical dominance in publishing papers on this topic to two comprehensive medical journals, i.e., Medicine and BMJ Open. Although Supportive Care in Cancer and Integrative Cancer Therapies are not journals in CAM field, they published papers related to the current research topic from 2004 and 2007, respectively . However, inflammation, rating scale, psychological stress, and mindfulness were the most studied trend topics recently and might become the future research hotspots.We also selected keywords that appeared at least five times over 3 years to characterize the emerging topics in the target discipline, and in turn predict future research trends . The mos4.4.1.The current study adopted a quantitative scientometric method, and provides a comprehensive snapshot concerning CAM treatment for depression over the previous 30 years. The relevant publications in general show a rapid year-on-year increase trend, showing this research theme is gaining attention worldwide. The USA, China, and Australia were the main research powers in the current research field. This may be due to the fact that more affiliations or excellent scholars in these countries are looking at the target research, thus expanding the national influence in the field. Harvard University was the institution with the highest research strength and a strong citation burst, with published papers were cited 444.1 times on average. Roger B. Davis from this institution was one of the most influential authors. However, collaboration among authors within the current research field was less than intimate and it was usually limited to one institution or one country/region. Cross-team cooperation was insufficient. The studies on the management of depression using CAM involve multidisciplinary fields, including psychology, clinical science, nursing, sociology, education, and molecular biology. In addition to being a stand-alone mental illness, when depression is viewed as a comorbidity in cancer patients, CAM treatments for this symptom have widely received attention. Of the various CAM modalities, researchers are most keen to study mind–body techniques and acupuncture, with the research focus gradually shifted from effects and safety assessment to investigation of mechanisms of actions.4.2.In comparison with the previously published various types of reviews within the similar topic, our current scientometric analysis provides a more timely, visual, and unbiased approach to track the emerging trends and frontiers , and outFew limitations should be acknowledged. First, nearly all information was extracted and analyzed by bibliometric software based on machine learning and natural language processing, which may lead to potential bias . Second,4.3.The continued growth in the number of publications implies that more researchers are becoming interested in managing depression with CAM. The ICD-11 code set, which was officially in effect in 2022, first contains traditional medicine diagnoses, recognizing the value of traditional medicine in disease management and health promotion . Member Instead of being marginalized, some CAM systems, such as traditional Chinese medicine  and tradAlthough 7,638 scholars contributed to the current research theme, most of the cooperation between them is limited to the same country or same institution. Such forms of collaboration with close internal connection but less external connection restricts the sharing of resources, ideas, and the exchange and discussion of individual perspectives among researchers . To prodIF of all these journals is below 4.0, indicating that the academic value and impact of such papers still need to be strengthened. It has been suggested that CAM-related papers were rarely accepted by mainstream medical journals, particularly those with high IF. This is because many clinical trials (and their resulting reports) of CAM therapies were of poor methodological quality , acupuncture and mindfulness-based intervention are growing research hotspots or emerging trending topics. Given evidence-based results were preferred, research methods such as systematic review and/or meta-analysis were widely used. In addition to appraising the efficacy and safety of CAM therapies, the promising research directions might concentrate on clarifying the possible mechanisms of action underlying CAM therapies in attenuating depression in terms of modulation of psychological stress and neural inflammation. Although countries with mature CAM healthcare systems, such as China, South Korea, and Iran, are enthusiastic about the current research theme, the USA maintained the leading position in this research field published in English. For higher quality research outputs, more intimate cross-country/regional research collaborations are required.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.F-YZ: Conceptualization, Formal analysis, Writing – original draft, Funding acquisition, Investigation. PX: Methodology, Software, Writing – original draft. ZZ: Supervision, Writing – review & editing. RC: Supervision, Writing – review & editing. YX: Writing – review & editing, Data curation. L-PY: Writing – review & editing, Investigation. H-RW: Funding acquisition, Writing – review & editing. Y-MW: Funding acquisition, Writing – review & editing. Y-XL: Resources, Writing – review & editing. C-YL: Validation, Writing – review & editing. W-JZ: Funding acquisition, Investigation, Writing – review & editing. Q-QF: Visualization, Methodology, Software, Writing – review & editing. GK: Project administration, Writing – review & editing.