Background: Whole-body plethysmography (WBP) is the gold standard for measuring lung volume, but its clinical application is limited as it requires expensive equipment and is not simple to use. Studies have shown that the single-breath helium dilution (SBHD) method, which is commonly used in clinical practice, significantly underestimates lung volume in patients with obstructive lung disease (OLD). By comparing the differences in lung volume measured using SBHD and WBP, we aimed to establish a correction equation for the SBHD method to determine the total lung volume in patients with OLD of different severities.Methods: From 628 patients with OLD simultaneously subjected to SBHD and WBP, 407 patients enrolled between January 2018 and November 2019 were in the training group and 221 enrolled between December 2019 and December 2020 were in the prospective verification cohort. The multiple linear regression equation was used for data in the training group to establish a correction equation for SBHD to determine the total lung volume, and this was validated in the prospective validation cohort.Results: There was a moderate positive correlation between total lung capacity (TLC) determined using the SBHD [TLC (SBHD)] and WBP methods [TLC (WBP)] , and the differences between TLC (SBHD) and TLC (WBP) (ΔTLC) were related to the severity of obstruction. As the severity of obstruction increased, the TLC was underestimated by the SBHD method. We established the following correction equation: TLC (adjusted SBHD) (L) = −0.669 + 0.756*TLC(SBHD)(L) – 0.047*height (cm)–0.009*weight(kg)(r2 = 0.753 and adjusted r2 = 0.751). Next, we validated this equation in the validation cohort. With the correction equation, no statistical difference was observed between TLC (adjusted SBHD) and TLC (WBP) among the obstruction degree groups (P > 0.05).Conclusions: The SBHD method is correlated with WBP to measure the total lung volume, but the SBHD method presents limitations in determining the total lung volume in patients with obstructive lung disease. Here, we established an effective and reliable correction equation in order to accurately assess the total lung volume of patients with OLD using the SBHD method. The American Thoracic Society/European Respiratory Society (ATS/ERS) has proposed the following definitions of various lung disease patterns : (i) obsVarious methods can be used to determine the TLC. The ATS/ERS guide proposes whole-body plethysmography (WBP), helium dilution, nitrogen wash-out, computed tomography (CT), and chest X-ray (CXR) to determine the TLC . WBP andSix hundred twenty-eight participants with obstructive pulmonary disease who undertook the SBHD and WBP tests were continuously enrolled from January 2018 to December 2020. We included 407 patients enrolled from January 2018 to November 2019 as the modeling group, and 221 patients enrolled from December 2019 to December 2020 served as the verification cohort.The inclusion criteria were as follows: (1) age 18–80 years and 2) comply with the ATS/ERS definition of obstructive lung disease ( comply wThe exclusion criteria were as follows: 1) contraindications for spirometry, pulmonary diffusion function test, and WBP  contrain, 5, 7 anThis study was approved by the institutional review board of Second Affiliated Hospital of Zhejiang University School of Medicine.We recorded each participant's sex, age (years), weight (kg), and height (cm). All participants had an adequate pharmacological washout  before the start of the protocol. Each participant underwent spirometry and SBHD tests first, and then the WBP test after resting for 10 min . All lunWBP: The participant sits in a body box, wears a nose clip, and breathes calmly. A valve is closed at the end of tidal expiration, and shallow and rapid breathing is required at a frequency of 0.5–1.0 Hz . Small cSBHD: After the participant breathes calmly and steadily, they fully exhale to the residual volume position, evenly and quickly inhale the mixed gas to ≥90% VC, hold their breaths for 8–10 s, and then, exhale evenly and moderately to the residual volume position in 2–4 s. The percentage concentration of alveolar helium before and after dilution can be used to calculate alveolar volume, and the TLC can be obtained by adding the alveolar volume to the estimated dead space volume .According to the ATS/ERS guidelines , the sevt-test was used to compare means between groups. The one-way analysis of variance was used to compare means among multiple groups, Wilcoxon test was used for non-parametric indicators, and Pearson correlation was used for correlation analysis. Results with P < 0.05 were considered statistically significant. Multiple linear regression analysis was used to establish a regression equation model for predicting TLC (WBP), and the variables included in the model were significant at P < 0.05. Bland-Altman plots were used to assess the agreement of the different methods for measuring the total lung volume.Statistical analysis was performed using SPSS Statistics 23.0 software . A paired P = 0.000). There were no significant differences in sex, age, weight, or TLC between the training and validation groups. We further compared the results of the two methods to determine whether TLC was normal. We found that underestimation of TLC by the SBHD method easily caused misjudgment of lung disease patterns. We defined the misjudgment as: when the SBHD method judged TLC < LLN, but WBP judged TLC ≥ LLN, the proportion of this false result in the population. With the increase in the severity of obstruction, the misjudgment rate showed an upward trend, rising from 16 to 64% (The baseline features and lung function results of the 628 subjects are shown in 6 to 64% .P = 0.000) (P = 0.000) (P = 0.000) (P = 0.000)  and TLC (WBP) in each patient and defined it as ΔTLC. Pearson correlation analysis was used to evaluate TLC (SBHD) and TLC (WBP), as well as the correlations of ΔTLC with FEV1%pred and ΔTLC with FEV1/FVC. The results showed that TLC (SBHD) and TLC (WBP) were moderately positively correlated  , the cor= 0.000) , and the= 0.000) . After e= 0.000) .P = 0.000; 2 = 0.753 and adjusted r2 = 0.751). Furthermore, after grouping by different degrees of obstruction, there was no statistical difference between the TLC (adjusted SBHD) and TLC (WBP)  with TLC (WBP) in the training cohort of 407 cases, and the results showed that TLC (SBHD) (4.78 ± 0.95 L) was significantly lower than TLC (WBP)   and TLC (WBP) in different obstruction degree groups. TLC (SBHD) was lower than TLC (WBP) ( = 0.88) . MoreoveWe used Bland–Altman plots to compare TLC (WBP) with TLC (adjusted SBHD) in all 628 patients. There was a very good agreement between TLC (WBP) and TLC (adjusted SBHD) in In the study, we established a correction model for the SBHD method using WBP as the gold standard; to the best of our knowledge, our study sample size is currently the largest. We found that the SBHD method and WBP are moderately positively correlated in patients with obstructive lung disease, and the difference between the two gradually increases as the degree of airflow obstruction increases. We established a correction equation for the SBHD method, which allows SBHD to accurately assess the total lung volume in patients with obstructive pulmonary disease with different degrees of obstruction.p < 0.01). In groups of patients with mild to moderate OLD and severe OLD, the difference in TLC between the methods ranged from 1.58 to 2.00 L. Similarly, Milite et al. (P < 0.001).For patients with obstructive pulmonary disease, the SBHD method significantly underestimated lung volumes, which is consistent with previous findings. In a retrospective cross-sectional study, Coertjens et al.  found the et al.  measuredA possible explanation for the underestimation of the SBHD method is that the lung function of patients with OLD primarily manifests as airflow limitation. As the disease progresses, air trapping in the small peripheral airways, functional residual capacity or residual volume is significantly increased, airflow limitation causes compensatory responses in the body, the patient over-breathes, the chest cavity is overinflated, and the TLC is significantly increased , 16. TheAn additional explanation of the discrepancy between WBP and SBHD methods is that plethysmography may overestimate TLC in airway obstruction due to a compliant extrathoracic airway . O'DonneThe choice of methods for measuring the lung volume depends on cost, availability, convenience, and accuracy. Although the multiple-breath helium dilution method enables a more even distribution of the inhaled gas, the testing time is longer. In patients with severe airflow obstruction, the difference between the two helium dilution methods is as high as 34%, and the two still underestimated the lung volume compared with the total lung volume measured by WBP . CT can The correction equation of TLC (SBHD) adjusted for the degree of airflow obstruction constitutes an important contribution of our study. Hopkins Asthma and Allergy Center  establisIn our study, we included physiological factors, such as sex, age, height, and weight, in establishing the correction equation to improve the fit of the regression model. It is known that age, sex, weight, height, and ethnicity are the main physiological determinants of lung volumes –28. HeigOur study had certain limitations. First, we only included test patients from a single hospital, and we will conduct multi-region and multi-center verification in the future. Moreover, the number of female patients was smaller than that of male patients, especially in the severe and highly severe groups. Domestic epidemiological surveys –35 have In summary, in patients with lung disease affected by airway obstruction, the SBHD method underestimated lung volumes, and the difference between TLC (SBHD) and TLC (WBP) increased gradually with increasing degrees of airflow obstruction. After using the correction equation, the TLC (WBP) values can be more accurately predicted based on TLC (SBHD). The use of the correction equation makes the simple and low-cost SBHD method a reliable method for measuring the TLC of patients with OLD, providing important value in the diagnosis and treatment of respiratory diseases as well as in the course of observation and preoperative evaluation of patients.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the institutional review board of Second Affiliated Hospital of Zhejiang University School of Medicine. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.YX, GH, and YZ were involved in the conception and design. QL, LZ, PF, JL, BS, LH, YW, and GH were in charge of data acquisition, analysis, statistical analysis, and interpretation of data. QL, LZ, and YX were responsible for drafting and critical revision of the manuscript. All authors have read and approved the final manuscript.This work was supported by the Medical and Health Technology Program of Zhejiang Province (2018ZH013), Zhejiang Provincial Natural Science Foundation [LY20H010004], and National Natural Science Foundation of China [81870022].The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


A growing body of work points toward the existence of a clinically symptomatic prodromal phase in multiple sclerosis (MS) that might span 5–10 years or more. A prodrome is an early set of signs or symptoms predating the onset of classical disease, which in turn predates a definitive diagnosis. Evidence for a prodromal phase in MS could have major implications for prevention, earlier recognition and treatment, as well as an improved disease course or prognosis. This Perspective provides a succinct overview of the recent advances in our understanding of the MS prodrome and current key challenges. Many of the MS prodromal features characterized thus far are non-specific and are common in the general population; no single feature alone is sufficient to identify an individual with prodromal MS. Biomarkers may increase specificity and accuracy for detecting individuals in the MS prodromal phase, but are yet to be discovered or formally validated. Progress made in the elucidation of prodromal phases in other neurological and immune-mediated diseases suggests that these barriers can be overcome. Therefore, while knowledge of a prodromal phase in MS remains nascent, how best to move from the rapidly growing evidence to research-related action is critical. Immediate implications include refining the concept of the MS continuum to include a prodromal phase. This will help inform the true “at risk” period when considering exposures that might cause MS. Major long-term implications include the earlier recognition of MS, improved prognosis, through earlier disease management, and the future possibility of MS disease prevention. A prodrome is an early set of signs or symptoms predating the onset of classical disease , which iactionable evidence, thus providing an outline of how the field should harness knowledge of the MS prodrome to effect change, both now and in the future. Finally, we propose a refined timeline for MS, conceptualized as a continuum, which includes the prodromal phase  were for peer-reviewed journal articles . Case reports and case series were excluded. Select older studies representing landmark advances were included, as necessary, in order to place current findings in context.References for this article were identified by searching PubMed for journal articles published in English, with a focus on the last 5 years, using the following terms : The last 5 years have seen the emergence of population-based studies which objectively measured signs and symptoms occurring before classical MS onset , 11–18. p = 0.0095, equivalent to a 6 IQ-point difference) (n = 27) vs. low (n = 20) risk of developing MS, based on a genes-environment score, exhibited poor vibration perception in their great toe   . The menference) . Intriguference) . While tference) , 23, if djusted] . Whetherdjusted] .Box 2Definition of a prodrome: an early set of signs or symptoms related to a disease, but predating the onset of classical symptoms, which in turn predates a definitive diagnosis.The challenge: identifying the onset of classical disease can be difficult and differs across studies. For the purposes of this article, we have summarized the most common used below, and indicate what the timing (date) of each likely represents:MS symptom onset: typically recorded by a MS neurologist in a patient's medical record and is based on a careful medical history.• Represents the closest to actual classical onset of MS, based on current knowledge.demyelinating diagnostic code: typically captured in health administrative data  or in electronic medical records.• First Represents the first formal medical recognition of a demyelinating event.MS diagnostic code -9/10 340 or G35, or Read codes): typically captured as for a first demyelinating diagnostic code.• First Represents the first formal medical recognition of MS.classical MS onset' is used to refer to either MS symptom onset or a first demyelinating code, as needed . MS symptom onset is arguably the closest possible to classical MS onset, thus enabling studies to avoid capturing the period between classical MS onset and diagnosis. This period, while of interest, should not be considered part of the prodromal phase.For the purposes of this article, ‘There is little research on whether the clinical presentation of the MS prodrome differs by age, sex, or the subsequent disease course , 16, 18.p = 0.045  in the 5 years before MS symptom onset was observed compared to relapsing-onset MS  . This maTogether, these studies demonstrate that clinical features suggestive of an MS prodrome can be objectively measured at the population-level. Clearly, many of the MS prodromal features identified are also non-specific and common in the general population; no single feature alone will be sufficient to identify an individual with prodromal MS. Findings also suggest that an iterative approach is required; as earlier recognition and diagnosis of MS is achieved, then this could refine understanding of the MS prodrome. Thus, there is sufficient evidence to warrant further investment of resources and research funds in this area. The Table provides key examples. One low-cost, but valuable endeavor would be to re-evaluate previous studies for signs and symptoms suggestive of the MS prodrome.Given the wide range of common and non-specific clinical symptoms observed at the population-level before the onset of MS symptoms, biomarkers for prodromal MS would be tremendously helpful. Such biomarkers could increase specificity and accuracy for identifying individuals in the MS prodrome.One potential biomarker is abnormal neuroimaging, such as in people with RIS. RIS is the clinical syndrome in which individuals underwent MRI scans of the brain for reasons other than suspected MS, resulting in an MRI finding suggestive of MS  . Formal n = 19) and MS (n = 26), but were lower in these 45 individuals together as compared to 21 controls , and whole brain volumes were generally lower in individuals with RIS compared to controls –37. One controls . In thoscontrols , 40.* at 3T) in individuals with MS as compared to those with migraine in one study  , while not specific to MS, may be useful for the prodrome. In a nested case-control study of US military personnel, serum NfL levels were elevated in 30 individuals who subsequently developed MS as compared to 30 matched controls (median 16.7 vs. 15.2 pg/mL = 0.04) . Serum sp = 0.012 and 1.02, 95% CI: 1.00–1.04; p = 0.019, respectively)  . Prelimictively) . Other ectively) . Other pctively) –51. Finactively) , 53 and ctively)  may be bTogether these findings suggest that there may be measurable biomarkers for the MS prodrome including those in serum, CSF, and on MRI that warrant further study see . ExploraThe duration of the MS prodrome can be defined as the time between the initiation of MS pathology and the appearance of the classical clinical demyelinating events that eventually lead to an MS diagnosis . KnowledThere are currently four environmental risk factors for MS that evidence suggests may have a causal role in MS development: infection with Epstein-Barr virus (EBV), low sunlight exposure/low serum vitamin D levels, obesity in early life, and cigarette smoking . One necStudies of other risk factors that have been measured within the presumed prodromal phase, i.e., within 5–10 years of MS symptom onset, include migraines, lower levels of physical activity, diet quality, pregnancy and oral contraceptive use –73. PregGiven the evidence that prodromal MS may precede MS symptom onset by 5 or more years, a review and re-evaluation of the MS environmental risk factor literature should be conducted to determine whether any associations (null or otherwise) may be explained by the exposure being measured in the presumed prodromal phase rather than before. Further, future study designs of environmental risk factors of MS need to factor in the time of a possible prodromal phase, assessing exposure at multiple time points prior—perhaps up to 10 years or more–to MS symptom onset, though this is not without challenges.Evidence for a prodromal phase of MS has major implications for prevention, earlier recognition and diagnosis of MS, as well as improved disease prognosis. Immediate implications include refining the conception of a timeline for MS that includes a prodromal phase as part of the MS continuum . This wiresearch criteria for prodromal MS. A probability score, estimating the likelihood of an individual being in the prodromal phase of MS is envisaged. Building on current knowledge, including prior MS genetic risk scores  and the Charles H. Hood Foundation. KM was funded by NIH/NINDS and the US Department of Defense. She has received travel expenses to participate in conferences from the National MS Society  and ECTRIMS/ACTRIMS . She was a paid consultant for serving on a scientific advisory committee . HT was the Canada Research Chair for Neuroepidemiology and Multiple Sclerosis. Current research support received from the National Multiple Sclerosis Society, the Canadian Institutes of Health Research, the Multiple Sclerosis Society of Canada and the Multiple Sclerosis Scientific Research Foundation. In addition, in the last 5 years, has received research support from the UK MS Trust; travel expenses to present at CME conferences from the Consortium of MS Centres (2018), the National MS Society , ECTRIMS/ACTRIMS (2015–2020), American Academy of Neurology . Speaker honoraria are either declined or donated to an MS charity or to an unrestricted grant for use by HT's research group. Unrelated to this study NM receives funding from the Charles H. Hood Foundation.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Over 1250 data points from the Devonian Duvernay shale were utilized to create and validate the model. These datasets were obtained from three wells; the first was used to train the models, while the data sets from the other two wells were utilized to test and validate them. Support vector machine (SVM), random forest (RF), and decision tree (DT) were the ML approaches tested, and their predictions were contrasted with three empirical correlations. Various AI methods' parameters were tested to assure the best possible accuracy in terms of correlation coefficient (R) and average absolute percentage error (AAPE) between the actual and predicted TOC. The three ML methods yielded good matches; however, the RF-based model has the best performance. The RF model was able to predict the TOC for the different datasets with R values range between 0.93 and 0.99 and AAPE values less than 14%. In terms of average error, the ML-based models outperformed the other three empirical correlations. This study shows the capability and robustness of ML models to predict the total organic carbon from readily available logging data without the need for core analysis or additional well interventions.Unconventional resources have recently gained a lot of attention, and as a consequence, there has been an increase in research interest in predicting total organic carbon (TOC) as a crucial quality indicator. TOC is commonly measured experimentally; however, due to sampling restrictions, obtaining continuous data on TOC is difficult. Therefore, different empirical correlations for TOC have been presented. However, there are concerns about the generalization and accuracy of these correlations. In this paper, different machine learning (ML) techniques were utilized to develop models that predict TOC from well logs, including formation resistivity (FR), spontaneous potential (SP), sonic transit time (Δ Due to the continuous oil and gas exploitation, conventional hydrocarbon reserves are gradually depleted, and the production rates of the current reservoirs are significantly declining.Conventional hydrocarbon reserves are gradually depleting due to the continuous oil and gas exploitation and the production rates of the current reservoirs dramatically dropped , 2. SourTotal organic carbon (TOC), which has been widely considered as a quantification of the hydrocarbon generation potentials –10, is oSeveral authors developed empirical correlations to determine TOC, and these models were developed based on the experimental TOC measures on drilling cuttings or core samples and the corresponding well logs. Then, these developed correlations are proposed to be applied to determine the TOC for different wells –19. Thest), bulk density (RHOB), neutron porosity (CNP), gamma ray (GR), and spectrum logs of thorium (Th), potassium (K), and uranium (Ur).Artificial intelligence (AI) has been used in different industries . AI techTOC is a vital parameter to characterize the unconventional resources. Experimental analysis can be used to measure the TOC, but it is expensive, time-consuming, and does not give a continuous profile for the total organic carbon with depth. Empirical correlations can be used to estimate TOC. However, there are concerns about the generalization and accuracy of these correlations. In this paper, the application of different AI techniques in TOC prediction in Devonian shale formation from the well logs will be tested. These well logs include sonic transient time, resistivity, bulk gamma ray, bulk density, and spectral GR logs of Th, Ur, and K and neutron log porosity. The next sections in the paper describe the methodology that was used to build the ML models, followed by building the model. The models were then tested and validated with different data sets. A sensitivity analysis was conducted to investigate the importance of input logging parameters in the predicted TOC values.In this study, three machine learning tools were used to estimate the TOC as a function of eight well logs records. Experimental data for TOC from three different wells have been collected together with their corresponding well logs. 891, 291, and 82 data points from Well-A, Well-B, and Well-C were used to train, test, and validate the AI models all, respectively. All wells are in Devonian Duvernay shale, which is an organic liquid-rich source rock. The sedimentary basin is located in Alberta, Canada, with 61.7 billion barrels and 443 trillion cubic feet of oil and gas reserves, respectively [Rock-Eval 6 was employed to estimate the actual TOC values of drilling cuttings from different wells. Tests procedures are shown in Prior to the AI model's training, outliers, incomplete, or unrealistic data points were eliminated from the data used to construct the model. Data points that contain any value that is away from the mean of the data with three times the standard deviation were considered as an outlier. The statistical characteristics of Well-A's dataset are illustrated in The AI model was trained and optimized in this work using Well-A dataset that contains 891 data points with wide ranges of TOC and well logs values. The effect of various parameters inside the AI algorithms was tested to optimize the models, by running the AI tools inside a mutable for loops in MATLAB.In SVM models, different kernel functions, values for kernel options, epsilon, and regularization were tested while in RF, different sets of number of trees, maximum number of level in each tree, and maximum number of features were used. In DT, various values for maximum tree depth, minimum sample split, and maximum number of features were tested. The correlation coefficient between the known and predicted TOC and the average absolute percentage error (AAPE) were used as evaluation criteria.In addition, different sets of inputs were used to evaluate the significance of each well log parameter in TOC prediction. Seven sets were considered, the least one includes four parameters, and the most comprehensive consists of all eight parameters, as shown in The accuracies of the generated models were tested and validated using 291 and 82 data points from Well-B and Well-C, respectively. The two wells are in the same field as Well-A. The performance of AI models was also compared with that of currently existing correlations, such as the Schmoker model, the logR approach, and the Zhao et al. model.t, CNL, FR, GR, and spectral GR. The training dataset consisted of 891 data points from Well-A, while the testing dataset contains 291 data points from Well-C. This section presents the results obtained using each method.The AI models were trained for TOC estimation based on eight well log data of RHOB, ΔUsing data set from Well-A and Well-B, different trials have been applied using support SVM with changing some tuning parameters inside the algorithm, such as kernel function and regularization. The best results were achieved using the Gaussian kernel function. Good results have been achieved in the training dataset with a 7.1% average error; however, the accuracy in the testing datasets was relatively low with an average error of 19.7%. The correlation coefficients were 0.974 and 0.856 for training and testing, respectively. R value which equals 0.864 and which reduces the ability to generalize the model. In This technique resulted in perfect fitting in the training dataset with a 0.994 correlation coefficient as shown in R which equals 0.989. In comparison with DT, RF performed better in testing with a 0.931 correlation coefficient, and its performance with the training data set was very close with From the previous analysis, RF was chosen to perform the sensitivity on the inputs. Seven different sets of the well logs information are tested and reported in R, and Zhao et al. correlations.As shown in the previous results, good matching accuracies for Devonian shale's TOC have been achieved by the two methods in training and testing datasets. As an additional validation, eighty-two data points from Well-C were kept hidden from the AI tool during model construction utilized later to ensure the generalization of the new models. This well is located in the vicinity of the Well-I and Well-II that has been used in model building. The validation data points fall in 140 ft depth range. The accuracy of the two AI techniques was compared with that of three of the available models for TOC estimation, namely, the Schmoker, ΔlogR of 0.94. DT and SVM models were less accurate than RF; however, in terms of AAPE, both were better than the other three correlations with AAPE values less than 16.4%. Zhao et al. [R predictions with AAPE values range between 20% and 25% and R values between 083 and 0.83 while the least favorable results in the validation dataset were from the Schmoker model with AAPE above 48%.The TOC values obtained from different empirical correlations and AI-based models against the actual values of Well-C dataset are presented in o et al.  correlatt logs and level of maturity (LOM) (ΔlogR method) or bulk gamma ray, FR, and Δt or RHOB logs  or a combination of FR and Δo et al.  correlatt, FR, K, Th, and Ur logs, which helps to obtain a continuous profile for TOC with depth without the need for core analysis or additional well interventions. Similar to any developed model, we recommend employing the developed models using input parameters within the same model's inputs ranges to ensure reliable results. For future work, more data will be collected to validate the developed models and other ML techniques will be applied.The developed models were able to accurately predict the TOC from the convention well log including CNP, RHOB, GR, ΔR values of 0.99 and 0.93, respectively, and AAPE values of 5.3% and 13.8% in the same order.In training and testing datasets, the three AI algorithms produced good matches; however, the RF-based model has the best accuracy. The RF model was able to predict the TOC for the training and testing datasets, with Data from a different well were hidden entirely from the AI tools and used to validate the built model. In this dataset, the RF model produced a 0.94 correlation coefficient and a 14% AAPE.The AI-based models' predictions were compared with three other empirical correlations. The AI models yielded more accurate results contrasted to the other models which resulted in AAPEs greater than 20%.This study established three models for TOC prediction in Devonian shale from conventional well logs and spectral GR logs using different machine learning techniques and approximately 1250 data points from three wells. The employed ML techniques were support vector machine (SVM), decision tree (DT), and random forest (RF). A summary of the findings reported in this paper is as follows :In train


Recent research has shown that the prevalence of stroke incidents and the number of survivors in developing countries surpass those from developed countries. This study aimed to enumerate the prevalence of post-stroke psychiatric and cognitive symptoms among stroke survivors from West and South Asia and Africa through a systematic review and meta-analysis. Data from each country was systematically acquired from five major databases ). Meta-analytic techniques were then used to estimate the prevalence of various post-stoke psychiatric and cognitive symptoms. A total of 36 articles were accrued from 11 countries, of which 25 were evaluated as part of the meta-analysis. The pooled prevalence of post-stroke depression as per the Hospital Anxiety and Depression Scale (HADS), Hamilton Depression Rating Scale, Patient Health Questionnaire, Schedules for Clinical Assessment in Neuropsychiatry (SCAN), Geriatric Depression Scale, and the Montgomery–Asberg Depression Rating Scale ranged from 28.00 to 50.24%. Pooled prevalence of post-stroke anxiety based on the HADS and SCAN was 44.19% and 10.96%, respectively. The pooled prevalence of post-stroke cognitive impairment as per the Mini-Mental Status Examination was 16.76%. This present review has suggested that both psychiatric and cognitive symptoms are common among stroke survivors. Concerted efforts are needed to institute robust studies using culturally sensitive measures to contemplate mechanisms that address the unmet needs of this vulnerable population. On one hand, while environment-related and infectious diseases continue to pose challenges, these developing countries are concurrently witnessing a spike in non-communicable diseases including neurological events that lead to the intransigent and debilitating sequelae of focal  disturbance in cerebral function. In this regard, stroke has been widely established to compromise the integrity of emotional and cognitive functioning, often going on to disrupt an individual’s premorbid self-directedness, quality of life, and meaningful existence. Stroke has also been recorded as being the second leading cause of death and dependency worldwide [Recently, in the part of the world where approximately 80% of the global population resides—sometimes labeled as the ‘Global South’, ‘emerging economies’, ‘low- and middle-income countries’ or simply ‘developing’ or ‘non-Western countries’—there has been a surge of health problems resonating with the metaphor of a ‘orldwide ,2 and acorldwide .Research from many industrialized countries of North America, Western Europe and the pocket of countries in the Pacific Rim have shown that the most common risk factors contributing to the stroke epidemic include transient ischemic attacks, high blood pressure, tobacco smoking, end-stage kidney disease, atrial fibrillation disease, obesity, hyperlipidemia, and diabetes mellitus . HoweverAs a result, stroke survivors in developing countries have been documented to be marked with intransigent and debilitating disabilities . To combIn the past decades, an increasing number of impressionistic and anecdotal reports along with a significant number of observational studies have reported the prevalence rates and types of emotional and cognitive symptoms persistent among stroke survivors in developing countries. In this regard, such studies must be applied in a wider context so that evidence-based quantification, prevention, and mitigation can be contemplated upon. To date, while there have been narrative reviews reported from developing countries ,13, therWhile there is an abundance of research on cognitive and psychiatric symptoms secondary to stroke from many industrialized countries ,15,16, lThis study aimed to calculate the prevalence of psychiatric and cognitive symptoms among stroke survivors from West and South Asia and Africa through a systematic review and meta-analysis.The following systematic review was conducted via a properly established protocol, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines  and inclThe process of identifying articles based on the inclusion criteria began with a complete screening of the major databases: PsycINFO, Web of Science, Scopus, and PubMed/Medline. A final search of up to 14 pages on Google Scholar was also done to ensure the inclusion of grey literature and any remaining articles that may have been missed out. This chosen strategy did not include a search based on any specific timestamp, implying that all articles published before 6 May 2021 were included in the search.As shown in All studies included in this meta-analysis (1) were cross-sectional/cohort/case-control studies examining the prevalence of post-stroke secondary conditions in the general population; (2) used validated assessment tools; (3) had proper numerical information about prevalence and sample size; (4) were written in English. Articles were excluded if they were reviews, case reports, duplicated studies, non-human studies, or studies that did not include the examination of the prevalence of secondary post-stroke conditions as one of its aims. Additionally, studies were excluded if they scored below the 75% cut-off point on the evaluations while the JBI guidelines were being used. Articles were also excluded if they did not provide a proper prevalence measure of any of the secondary mental disorders as classified by the DSM-or ICD ,22. WhilThree authors  independently extracted relevant information from identified studies, including the name of the first author, the year of publication, the country in which the study was conducted, the year(s) in which the study was conducted, sampling methods, the mean, median, and the standard deviation of the age of participants, along with the age-range, the sample characteristic , total sample size, the gender distribution of the sample, the assessment tools, tool reliability, disorder screened, the total number of diagnosed cases, associated factors, and the amount of time elapsed between the stroke incident and the administration of the neuropsychological test (post-stroke duration).Independent rating of the title, abstract, methods, results, discussion, and other parts of each included study according to the standard items listed in the Joanna Briggs Institute guidelines was carried out by the three individual reviewers . The ran2 and Q statistics were used to assess heterogeneity across articles [2 statistic was greater than 50% and the Q statistic was < 0.1, a random-effects model was used to interpret the results; otherwise, we used the fixed-effects model [Acquired data were analyzed using the MedCalc 12 statistical software. In this review, three main secondary conditions  were identified for the PS patients. Some of the mental disorder outcomes were assessed by different tools. In the meta-analysis, the estimated pooled prevalence for each disorder outcome was calculated by using different assessment tools . I2 and articles . For thets model ,53.Although the initial search included 83 countries, the 11 countries that were finally included in this study were: Saudi Arabia, Qatar, Jordan, Iran, Egypt, Nigeria, Senegal, Uganda, India, Sri Lanka, and Bangladesh, with a total of 36 studies . The hig2 = 98.92%, Q = 276.91, p < 0.001).The estimated prevalence of PSD assessed by the HADS for four studies is shown in 2 = 93.53%, Q = 92.79, p < 0.001).The estimated prevalence of PSD assessed by the HDRS for seven studies is shown in 2 = 99.21%, Q = 379.61, p < 0.001). Since Litton et al. [2 = 77.56%, Q = 8.91, p = 0.012).The estimated prevalence of PSD assessed by the PHQ-9 for four studies is shown in n et al.  providedn et al. ) is show2 = 85.98%, Q = 14.27, p < 0.001).The estimated prevalence of PSD assessed by the SCAN for three studies is shown in 2 = 92.19%, Q = 12.81, p < 0.001).The estimated prevalence of PSD assessed by the GDS-15/30 for two studies is shown in 2 = 86.14%, Q = 14.43, p < 0.001).The estimated prevalence of PSD assessed by the MADS for three studies is shown in 2 = 98.88%, Q = 178.70, p < 0.001).The estimated prevalence of PSA assessed by the HADS for three studies is shown in 2 < 0.01%, Q = 0.22, p = 0.882).The estimated prevalence of PSA assessed by the SCAN for two studies is shown in 2 = 92.11%, Q = 38.01, p < 0.001).The estimated prevalence of PS Dementia assessed by the MMSE for four studies is shown in The current meta-analysis was done using a subgroup analysis based on the tool used for the assessment of prevalence. It is important to note that, while most of the studies tap only into subthreshold symptoms or probable ‘case-ness’ for both psychiatric and cognitive symptoms, there are some exceptions. In the current review, three studies utilized the Schedules for Clinical Assessment in Neuropsychiatry (SCAN) to extrapolate post-stroke data ,40,42. TMoreover, it was also not possible to group certain high-quality studies into any of the tool-based categories, since each study covered a single unique disorder by itself . Additionally, studies were also excluded even if they did reliably measure any of the disorders included in the current meta-analysis but utilized a unique tool that was not used by at least one other study. Rehabilitation 2030: A call for action [While the industrialized countries of North America and Western Europe have mobilized multiple strategies for the prevention of cardiovascular diseases and the mitigation of its sequelae, i.e., post-stroke disability and quality of life, the trend appears, in general, to be reversed in the current countries under scrutiny . The prer action , the WHOr action . Within The articles that fulfill the present inclusion criteria were those from West Asia , the Indian subcontinent , and Africa . A majority of these articles were observational studies that reported mental disorders and cognitive impairment and employed common questionnaires such as the Hospital Anxiety and Depressive Scale (HADS), Hamilton Depression Rating Scale (HDRS), Patient Health Questionnaire (PHQ-9), Geriatric Depression Scale (GDS15/30), and the Montgomery–Asberg Depression Rating Scale (MADS). These questionnaires, which are sometimes known to be administered as a part of structured interviews, are known to reveal probable diagnosis or subthreshold psychiatric symptoms. However, three studies utilized the more robust semi-structured interview process . Some studies tapping into cognitive symptoms did not employ conventional neuropsychological batteries but rather used ‘bedside measures’ such as the Mini-Mental Status Examination (MMSE), which are only equipped to indicate the presence of cognitive decline. In this study, post-stroke duration referred to the amount of time following the stroke incident after which the participants of each study were investigated for secondary conditions. In the three accrued articles, it was found that post-stroke duration was extremely varied, ranging from 1 week up to 10 years ,46. In tn = 20293) and yielding a pooled prevalence of 29%. In their systematic review that included 61 prospective observational studies up to May 2013 , Hackett and Pickles [n = 780). Seven studies (n = 658) utilized the Hamilton Depression Rating Scale (HDRS), yielding a pooled prevalence of 50.09%. With a pooled prevalence rate of 45.55%, four studies (n = 544) used the Patient Health Questionnaire (PHQ-9). The Geriatric Depression Scale (GDS-15/30) was used to assess depressive symptoms post-stroke in two studies (n = 306), giving a pooled prevalence of 35.25%. Three other studies (n = 237) used the Montgomery–Asberg Depression Rating Scale (MADS), yielding a pooled prevalence rate of 50.24%. Finally, only three remaining studies utilized semi-structured interviews, or Schedules for Clinical Assessment in Neuropsychiatry (SCAN) (n = 263), to tap into PSD. The pooled prevalence rate was 30.43%. It appears that the utilization of questionnaires tends to give spurious results compared to the semi-structured interview, often viewed as the gold standard for diagnosing depression. Overall, the present region of interest suggests that the rate of depressive symptoms ranged from 30.43 to 50.24%, which appears to be in the same range as that reported in previous meta-analyses, i.e., 29–31% [It has been widely recognized that the presence of depressive symptoms in post-stroke survivors tends to adversely affect the road to recovery, attenuating self-directedness and quality of life and heightening health service utilization and the risk of early death ,69,70,71 Pickles  reportedcale GDS-/30 was u, 29–31% . Previou, 29–31% . The pre, 29–31% . n = 5760) with stroke survivors fulfilled the study criteria. The pooled estimate of anxiety disorders stood at 18%. Rafsten, Danielsson, and Sunnerhagen [n = 13,756). The pooled prevalence of anxiety disorders was 29.3%. Knapp et al. [n = 22,262) of anxiety after stroke. Two types of the pooled estimates emerged: the semi-structured interview revealed 18.7% to be marked with anxiety disorder while the use of a symptom checklist yielded a prevalence of 24.2%. In the present review, both symptom checklists (HADS) and semi-structured interviews (SCAN) were used. A pooled prevalence of 44.19% for HADS was calculated from three studies (n = 582). Given that semi-structured interviews are more robust in identifying anxiety disorders, as often articulated in the literature, the pooled estimate derived from the two studies included in the present review (n = 153) was only 10.96%.One hypothesis is that anxiety constitutes a risk for stroke by approximately 24% , but thenerhagen  conductep et al.  have repn = 878) with a pooled estimate of 16.76%. This rate appears to be in the lower range of the global trend ranging from 22 to 47%.Cognitive impairment, characterized by impaired attention and concentration, problems with learning and remembering, executive functioning, the efficiency of speech and language and visual–spatial ability , has beeIn addition to the limitations often associated with systematic reviews and meta-analyses, the studies that have emerged from West and South Asia and Africa on post-stroke psychiatric and cognitive symptoms have various limitations of their own that need to be highlighted. Such limitations could be used as part of the background to strengthen the approach to the field in these aforementioned regions. Firstly, in the literature, there has been a widespread interest in cognition and psychiatry-specific instruments. It is not clear whether the diverse instruments employed in the present region of interest have been subjected to evaluation based on local psychometric properties. Relevant to this in the context of cognitive functioning, it appears that the MMSE has been widely used in the present region of interest. However, there are indications that this instrument is fairly weak in terms of sensitivity to minor cognitive impairment among the post-stroke population ,90. ThisSecondly, in some of the articles considered for the present review, there were many instances where it was not explicitly clear whether the quantification of post-stroke symptoms was done via self-reported instruments or through other means. Related to this, it was also not apparent whether the clinician-based interviews were conducted under the guidance of the established gold-standard interview protocol or simply under the clinical impression of the researcher/clinicians. Some of the assessment measures such as the Hamilton Depression Rating Scale have been widely reported as being part of the semi-structured interview process in a clinician-administered depression assessment scale, but there is a dissenting view in the literature . Future Future studies should also examine the feasibility of developing disease-specific instruments to identify post-stroke psychiatric and cognitive complications. Societies belonging to the presently defined region are diverse in terms of ethnicity, language, and culture. It is not clear whether the evaluation of post-stroke psychiatric and cognitive symptoms was performed while keeping the local idioms of distress in mind, since many of them were developed in industrialized countries. Third, it is likely that some of the studies that emerged from these vastly diverse regions have been published in languages other than English. This critical appraisal has overlooked such articles, and this should certainly be considered an important confounder. Fourth, it is commendable that research from the presently considered regions has been conducted using proper strategies for documenting post-stroke psychiatric and cognitive symptoms given the circumstances. However, it was noted that the quality and quantity of these research papers needed to be improved and increased. For instance, concerted efforts should be made to chart out premorbid functioning, pre-existing cognitive and psychiatric symptoms, single or multiple strokes, subtypes of strokes, as well as issues pertinent to localization and lateralization. It may also be worthwhile to explore the relationship between motor/functional conditions and psychiatric and cognitive conditions secondary to stroke. Fifth, while it has been widely suggested that post-stroke survivors tend to manifest various psychiatric and neuropsychiatric symptoms, in the present region of interest, most of the research focused predominantly on symptoms of anxiety and depression. Further studies quantifying other types of neurobehavioral impairments  are warranted ,93.The present region of interest that is often alternatively labelled the ‘global south’ or simply ‘developing countries’ has been widely described as being plagued with two health threats. One of them includes the traditional enemies of health such as malnutrition, child mortality, and environment-related and infectious diseases, while the other includes non-communicable diseases that entail, for example, neurological events that lead to the debilitating sequelae of focal  disturbance in cerebral function. Around 80% of the global population resides in these regions and while existing rudimental emergency services and critical care often lead to high death rates among the majority of those sustaining stroke, a significant number of them have gone on to survive, though with severely debilitating conditions. The present review has suggested that both psychiatric and cognitive symptoms are common among stroke survivors in the regions of West and South Asia and Africa, and form part of the unmet needs of such survivors. Concerted efforts are needed to institute more robust studies using culturally sensitive measures to contemplate mechanisms that would address the needs of these vulnerable populations.


Objective: The study aims to estimate the prevalence of disability and the association of disability with socio-demographic correlates and health outcomes among the Singapore population. Methods: Face-to-face interviews were conducted with a representative sample of Singapore’s population. Using the Washington Group’s questionnaire, disability was defined using both the ‘standard’ (a lot of difficulty or higher in one or more domains) and the ‘wider’ threshold (of ‘some’ or greater difficulty). Data on socio-demographic correlates, self-reported lifestyle, physical activity, chronic conditions, and health-related quality of life were also collected. Results: The prevalence of any disability using the standard threshold was 3.1% (95% CI: 2.4–4.1). When separated by disability type, mobility (1.8%) was the most prevalent, followed by vision (0.8%), cognition (0.5%), hearing (0.3%), and self-care (0.2). In the adjusted regression analysis, lower education and unemployed and economically inactive status (versus employed) were significantly associated with disability. Conclusions: Although this prevalence is lower than other countries, it is a significant finding in terms of actual numbers and impact at both the individual and the societal levels. Our findings also highlight the need to strengthen health services and preventive interventions targeting older adults and those who are physically inactive to reduce the burden of disability in these groups. The International Classification of Functioning, Disability and Health (ICF) defines disability as ‘impairments in body functions and structures, activity limitations, and participation restriction’. Disability is seen as the outcome of the interaction between a person  and that person’s contextual factors  . The UniThe Behavioral Risk Factor Surveillance System (BRFSS) survey that collects state data about United States (U.S.) residents regarding their health and health behaviours, also assesses disability. The survey includes questions about six disability types—hearing, vision, cognition, mobility, self-care, and independent living. Respondents who answer ‘yes’ to any of the questions are identified as having a disability. According to the 2016 BRFSS survey, 25.7% of U.S. adults reported some form of disability. Disability in mobility (13.7%) and cognition (10.8%) were most frequently reported, while disability in self-care (3.7%) was least frequent. The study identified several socio-demographic correlates of disability. Women had a higher prevalence of any disability  than men, as did those in older age groups compared with younger ones. Respondents with higher household income levels reported a lower prevalence of any disability .Persons with disabilities face several challenges that restrict their effective participation on an equal basis with others in society. Access to basic services like transport, education, employment and built spaces are often inadequate, thus placing them at risk of losing out on development and subject to rising inequality . At the Singapore is a highly developed Southeast Asian country with one of the highest per capita GDP in the world. Despite being a small country with limited natural resources, Singapore focuses heavily on developing its human capital and resources . At the While these studies have reported the prevalence of disability using different scales and ascertained some of the challenges PWD face, there remains a need to establish the prevalence using a validated tool consistent with the ICF framework that can enable comparisons across countries. In addition, there exists a knowledge gap in the understanding of disability distribution across socio-demographic profiles and comorbidities in Singapore. Such data is essential for creating awareness and early identification of these conditions, along with strengthening services across healthcare and social welfare systems. Lastly, valid population disability data are necessary for evaluating the outcomes of programs and policies for PWD. The aim of the current study was thus to estimate the prevalence of disability, both overall and by functional disability types, and the association of disability with socio-demographic correlates and health outcomes among the Singapore population using the Washington Disability Questionnaire ,12.This cross-sectional study utilised data from a nationwide study that aimed to examine the knowledge, attitudes, and practices (KAP) regarding diabetes in the general population of Singapore . ConcurrThe study population comprised Singapore citizens and permanent residents aged 18 years and above who could understand and speak in one of the official languages of Singapore—English, Chinese, Malay, or Tamil. The study excluded those who were uncontactable due to incomplete or incorrect addresses, those living outside of the country, participants who were assessed to have difficulties in understanding the questionnaire due to cognitive difficulties as well as those with severe physical or mental disorders who were unable to answer the questionnaire on their own. Trained lay interviewers conducted the survey, and data were collected via computer-assisted personal interviews (CAPI) using handheld tablets. Participants were recruited between February 2019 to September 2020. Based on data from an earlier study that examined knowledge of diabetes and risk factors , a targeEthical approval for the study was obtained from the Institute of Mental Health’s Institutional Research Review Committee and the National Healthcare Group’s Domain Specific Review Board (NHG DSRB Ref 2018/00430). All participants gave written informed consent, and parental consent was sought for those aged 18 to 20 years as the official age of majority in Singapore is 21 years and above. The study used a set of five questions from the short set of six questions of the Washington Questionnaire. This questionnaire was developed by the Washington Group on Disability Statistics and is consistent with the conception and principles of the definition of disability as articulated by the ICF ,12. The Standard Threshold—those who reported ‘a lot of difficulty’ and ‘cannot do at all’ were considered as having a disability, while those reporting ‘no difficulty’ and ‘some difficulty’ were not regarded as having a disability, as recommended by the developers .Wider Threshold- those reporting ‘some difficulty’, ‘a lot of difficulty’, and ‘cannot do at all’ were considered as having a disability .Each of these questions had four ordinal responses of ‘no difficulty, some difficulty, a lot of difficulty, or cannot do at all’. Disability was assessed based on the responses of the participants in two ways:While the first definition ensures comparability of our results with other international studies, the second, less conservative definition prevents missing respondents who may under-report the extent of disability fearing stigma or discrimination .Information about chronic physical conditions was obtained using a checklist that has been used previously in Singapore-based studies . Based o®, Quality Metric Incorporated, Johnston, RI, USA) is a 12-item self-report instrument that assesses health-related quality of life (HRQOL) [The Short Form Health Survey version 2 (SF-12v2 (HRQOL) . The SF- (HRQOL) . The Global Physical Activity Questionnaire (GPAQ) is a 16-item instrument developed by the World Health Organization to measure physical activity . The freParticipants were asked to state how healthy their lifestyles were by choosing one of the four statements—(i) I have a very healthy lifestyle, (ii) I have a fairly healthy lifestyle, (iii) I think my lifestyle can be improved, (iv) I think my current lifestyle is not healthy. Those endorsing (i) or (ii) were classified as ‘having a healthy lifestyle’, and those agreeing with statements (iii) or (iv) were classified as ‘having an unhealthy lifestyle’.A structured questionnaire was used to obtain socio-demographic information regarding age, gender, ethnicity, marital status, personal income, educational and employment status, and self-reported diagnosis of diabetes. The prevalence of any form of disability and the specific types of disabilities were expressed as weighted percentages with 95% confidence intervals (CI). In addition, socio-demographic characteristics of the population  stratified based on disability were also reported. The weighted mean and standard error were calculated for continuous variables, while unweighted frequencies and weighted percentages were presented for categorical variables. The associations between disability and socio-demographic characteristics were examined using binary logistic regressions, where disability served as the dependent variable, and socio-demographic characteristics served as independent variables. We also investigated the relationship between disability, lifestyle, and physical activity by using binary logistic regressions, analysing lifestyle and physical activity as the dependent variables and disability as an independent variable. An analysis of multinomial logistic regression was also conducted using the number of chronic conditions as the dependent variable and disability as the independent variable. Linear regressions were used to examine the association between PCS and MCS with disabilities, with PCS and MCS as the dependent variables and disability as the independent variables. Odds ratios (OR) and 95% confidence intervals (CI) were presented for binary logistic regressions. The multinomial logistic regression yielded prevalence ratios (PR) and 95% CI. For linear regressions, the beta-coefficient and 95% CI were reported.All regression analyses were adjusted for socio-demographic characteristics. Standard errors were calculated using the Taylor series linearization method to account for complex survey design. All statistical analyses were performed using Stata/SE 15.0 , with two-sided tests assuming a 5% significance level. Missing data were handled via listwise deletion.A total of 2895 participants were included in the analysis. Using the standard threshold, the prevalence of any disability was 3.1% (95% CI: 2.4–4.1). When separated by disability type, mobility  was the most prevalent, followed by vision  and cognition  . Table 2The prevalence of any disability using the wider threshold was 31.0% (95% CI: 28.6–33.4). When separated by disability type, cognition  was the most prevalent, followed by mobility  and vision  . Table SDisability, as defined by the standard threshold, was significantly associated with physical activity, PCS and MCS after controlling for socio-demographic variables . The oddThose with a disability, as defined by the wider threshold, were more likely to report having an unhealthy lifestyle than those with no disability . The odds of being insufficiently active (MET < 600) was 1.8 times (95% CI: 1.3–2.4) higher for PWD as compared to individuals with no disability. The prevalence of having two or more chronic conditions was higher for PWDs than individuals with no disability . The PCS of PWDs was 4.8 units lower (95% CI: −5.6 to −4.0), whereas their MCS was 4.2 units lower (95% CI: −5.2 to −3.2) .The study established the overall prevalence of disability as 3.1% in the adult Singapore population using the standard threshold recommended by the developers. The three most prevalent disability types were mobility, vision and cognition, while the least prevalent disability was self-care. Despite using the same disability criteria (at least one severe or extreme difficulty with bodily functions), the prevalence may vary across studies depending on the number of domains assessed and the population’s age structure. Mitra and Sambamoorthi  analysedUsing a wider threshold resulted in a 10-fold increase in the overall disability to 31.5% in the Singapore population. This increase was similarly seen in Mactaggart et al.  study whThe prevalence of disability varied significantly across several socio-demographic groups in the univariate analysis. As expected, disability, as assessed by the standard threshold, increased with increasing age, with 8.4% of those aged 65 years and above reporting a disability versus 1.4% in those aged 18–34 years. The increasing prevalence of disability by age can be explained by increasing frailty, decreasing muscle strength, and reduced cardiopulmonary fitness ,27, evenEducational and employment status were associated with disability, according to the regression analysis that used disability defined by the standard threshold. The likelihood of disability was higher for those with lower education status as compared to those with a degree and higher education. The United Nations Convention on the Rights of Persons with Disabilities specifies that countries must ensure that PWD have access to an ‘inclusive, quality and free primary and secondary education’. However, data suggests that children and adolescents with a disability are less likely to complete primary education than their non-disabled peers and that these disparities increase at higher levels of education . The undDisability, as determined by the standard threshold, was significantly associated with insufficient physical activity and PCS and MCS even after controlling for significant confounders. Studies suggest that both self-efficacy and physical activity are important determinants of HRQOL ,38,39. TOn the other hand, disability, as determined by the wider threshold, was associated with age, lower education, and lower-income. In addition, those with a disability as assessed by the wider threshold were more likely to report having an unhealthy lifestyle, being insufficiently active, having two or more chronic conditions, and lower HRQOL. Hence, including the ‘some’ category in the definition of disability can identify individuals with lower functioning who would benefit from support. However, the 10-fold increase in prevalence does pose challenges in terms of resource allocation and planning. Mactaggart et al.  have sugOur findings are subject to several limitations. Firstly, the study had a response rate of 66%; thus, there may be a non-response bias. In addition, given the perceived burden of the entire survey, those with severe disabilities might have chosen not to participate in the survey. Secondly, all participants had to provide written informed consent to participate in the study, thereby excluding those with severe cognitive disabilities. Thirdly, there is an element of stigma related to having a disability, and thus respondents may tend to under-report it. Lastly, the study did not include those living in institutional settings (prisons and long-term nursing home residents), which might exclude persons with disabilities since persons residing in these settings might be more likely to have a disability. Thus, our findings are likely to be a conservative estimate of the disability prevalence in Singapore. Regardless, this is one of the few studies in Singapore that established the prevalence of overall and specific types of disability in the population. In addition, the study is among the first to examine the association of physical activity, chronic conditions, and HRQOL with disability in a national sample.The study established the overall prevalence of disability in Singapore to be 3.1%. Although this prevalence is lower than other countries, it is a significant finding in terms of actual numbers and impact at both the individual and the societal levels. The 3rd Enabling Masterplan in Singapore has identified four key thrust areas: improving the quality of life of persons with disabilities, supporting caregivers, building the community, and building an inclusive society . This Ma


The purpose is to explore the feature recognition, diagnosis, and forecasting performances of Semi-Supervised Support Vector Machines (S3VMs) for brain image fusion Digital Twins (DTs). Both unlabeled and labeled data are used regarding many unlabeled data in brain images, and semi supervised support vector machine (SVM) is proposed. Meantime, the AlexNet model is improved, and the brain images in real space are mapped to virtual space by using digital twins. Moreover, a diagnosis and prediction model of brain image fusion digital twins based on semi supervised SVM and improved AlexNet is constructed. Magnetic Resonance Imaging (MRI) data from the Brain Tumor Department of a Hospital are collected to test the performance of the constructed model through simulation experiments. Some state-of-art models are included for performance comparison: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), AlexNet, and Multi-Layer Perceptron (MLP). Results demonstrate that the proposed model can provide a feature recognition and extraction accuracy of 92.52%, at least an improvement of 2.76% compared to other models. Its training lasts for about 100 s, and the test takes about 0.68 s. The Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the proposed model are 4.91 and 5.59%, respectively. Regarding the assessment indicators of brain image segmentation and fusion, the proposed model can provide a 79.55% Jaccard coefficient, a 90.43% Positive Predictive Value (PPV), a 73.09% Sensitivity, and a 75.58% Dice Similarity Coefficient (DSC), remarkably better than other models. Acceleration efficiency analysis suggests that the improved AlexNet model is suitable for processing massive brain image data with a higher speedup indicator. To sum up, the constructed model can provide high accuracy, good acceleration efficiency, and excellent segmentation and recognition performances while ensuring low errors, which can provide an experimental basis for brain image feature recognition and digital diagnosis. Now that science and technology advances quickly, Artificial Intelligence (AI), Big Data (BD), the Internet of Things (IoT), and Deep Learning (DL) have been accepted in all walks of life. Health is vital to the happy life of people. Medical intelligentization has improved health monitoring techniques notably. Brain tumors have severely harmed people’s health.Statistics suggest that the annual mortality rate of brain cancer in China is second only to gastric cancer and lung cancer. About 80,000 people die from brain cancer each year, accounting for about 22.5% of the world’s total number of brain cancers . In onlyMedical imaging techniques play a vital role in clinical diagnosis with the successive introduction of medical imaging equipment and the continuous development of computer science and technology. Results of brain disease diagnosis depend on brain image acquisition and accurate interpretation. However, due to the large number of patient groups, the number of brain and other medical images keeps increasing dramatically . Manual Image segmentation is one of the necessary steps of brain image fusion, so it is necessary to segment and extract the features of the image. There are many approaches to extract brain image features; currently trendy ones include Region Growing (RG), Level Set, Fuzzy Clustering, and Machine Learning (ML) . HoweverTo sum up, AI applications in medical fields, such as brain image processing, can help clinicians choose surgical treatment or radiotherapy and chemotherapy treatment options for patients while avoiding the steps of direct surgical resection of all tumors, which greatly reduces the risk of treatment. The innovative points are (1) the Semi-Supervised Support Vector Machines (S3VMs) proposed regarding the massive volume of unlabeled brain image data, (2) the improved AlexNet, and (3) the brain image fusion DTs diagnosis and forecasting model constructed based on S3VMs and improved AlexNet, in an effort to provide theoretical support for brain image feature recognition and digital diagnosis.Now that complex data grow explosively, big data mining and analysis functions, especially DL, are demanded increasingly, which has been explored by many scholars. The above works reveal that most ML algorithms applied in the medical field collect image data from historical cases. In contrast, DTs are mappings from virtual space to real cases. There are very few combinations of DTs and ML in the medical field. Hence, ML approaches and DTs are introduced for brain image fusion in the present work, which is of great significance to the accurate identification and diagnosis of brain images.At present, although many achievements have been made in the fusion of medical images such as brain tumors, the results are still unsatisfactory, and the application effect in clinical medicine is extremely limited. As one of the effective methods of brain image fusion, image segmentation technology has great practical value and research significance in the diagnosis, prediction, and feature extraction of brain tumor and other medical images.With the aging of the population, the number of patients is increasing, and the number of medical images is growing. It has become a new direction to diagnose and treat diseases by using computer technology such as digital twins to assist the recognition and prediction of brain images. Regarding brain image processing, excellent brain image segmentation results are of great significance for further target feature extraction and target recognition and analysis. Brain image segmentation differs from ordinary image segmentation in image complexity and diversity . Brain iTo improve the effect of brain image segmentation diagnosis, both unlabeled and labeled data are adopted to put forward an S3VMs regarding the massive volume of unlabeled brain image data based on the above approaches of brain image segmentation diagnosis. S3VMs combine supervised learning and unsupervised learning, which can utilize labeled data and unlabeled data simultaneously to improve the model’s generalization ability . CombiniSemi-supervised learning combines supervised learning and unsupervised learning. It utilizes both labeled data and unlabeled data for learning and mine the data distribution hidden in unlabeled samples such as brain images, thereby guiding the training of the classifier and improving its performance.A graph-based semi-supervised learning algorithm is adopted to express the relationships between the entire training data during brain image feature extraction. Graph nodes represent training samples, including labeled samples and unlabeled samples. There are edges between nodes; these edges are given weights to represent the similarity between data nodes and can be obtained using various distance metrics. Overall, the greater the weight, the greater the similarity. No connection between nodes means zero similarity between them. Similar vertices are given the same category labels as much as possible so that the graph distribution is as smooth as possible.The mathematical model of standard SVM can be formalized as a convex quadratic programming problem; then, a linear classification machine can be obtained through hard interval maximization learning . If the T = {,⋯,} is given, where  refers to the sample point, xi ∈ Rn, and yi ∈ { + 1,−1},i = 1,2,⋯,n. The red and the black circles represent two types of training samples, respectively. At this point, there is an infinite number of interfaces that can correctly separate the two types of samples. Hence, an optimal classification hyperplane is required, which should not only ensure the minimum empirical risk and correctly divide the two types of samples but also minimize the actual risk and meet the maximum classification interval. The core idea of linear separable SVM employs the maximum classification interval to find the optimal hyperplane, at which the solution is unique. In As shown in w represents the normal vector of the hyperplane, and b denotes the bias. The following conditions must be met to classify samples accurately and ensure the maximum interval simultaneously:In Eq. 1, The classification interval can be obtained as 2/1I y} II. Solving the optimal hyperplane of the training sample set T can be transformed into a problem described by the following equation:Equation (3) is a quadratic programming problem, which can be solved as a dual Lagrangian problem. Introducing the Lagrangian function can obtain:i refers to the Lagrangian multiplier. The partial derivative of the Lagrangian function can be obtained. According to the extreme conditions:In Eq. 4, αSubstituting equation 5 into 4 can get the dual problem about the original problem:w∗ and b∗ can obtain the optimal classification function:Solving Eq. 6 can get the optimal solution xi,yi) corresponding to xi ∈ Rn at According to Eq. 7, the optimal classification plane of SVM only depends on the sample point  refers to a graph, V represents the node set, and E describes the edge set. The weight matrix W of g is symmetric, which can be described as:A training set including labeled data and unlabeled data e = w(e) = wij = wji signifies the similarity between nodes xi,xj, which can be calculated using K Nearest Neighbor (KNN) graph. Each vertex in the KNN graph is only connected to its k nearest neighbors under a particular distance (such as Euclidean distance). Notably, a vertex xi that is the k nearest neighbor of xj does not necessarily indicate that xj is the k nearest neighbor of xi. While constructing the KNN graph, if xi and xj are k-nearest neighbors of each other, xi and xj will be connected by an edge, and the corresponding weights will be calculated using the Radial Basis Function (RBF) kernel:The weight of the edge xi = xj, the weight wij = 1; when ∣∣xi−xj∣∣ approaches infinite, the weight wij = 0.In Eq. 9, σ refers to the kernel function that controls the weight reduction speed. When To extract and forecast the obtained brain image features, CNN is introduced into the brain image feature extraction and diagnosis system. The proposed model can minimize the preprocessing workload and directly extract the most expressive features from the original data input without manual feature designation . Figure Convolutional Neural Network pooling layer performs down-sampling operations on the input feature maps in length and width dimensions. It can reduce the model parameters by down-sampling the input feature data. According to the number of parameters, the data complexity of different brain images can be reduced, diminishing the over-fitting degree and the probability of local minimum. Moreover, the pooling layer can make the model more robust to translation and distortion in the image.Data in CNN go through multiple convolutional layers and pooling layers; then, they are connected via one or more fully connected layers. All neurons in the current layer are connected to those in the last layer. Usually, this layer depends on two 1D network layers. Local information of the convolutional layer or the pooling layer is grouped together. The activation function used by all neurons is often the Rectified Linear Unit (ReLU).AlexNet , a deep Digital Twins  can foreIn this diagnosis and forecasting model, different penalty coefficients are provided considering the different contribution degrees of the labeled and unlabeled data samples in the brain images to the hinge loss. The objective function can be written as the following equation:s signifies a similarity matrix between samples, Ds ∈ Rn×n refers to a diagonal element, the diagonal matrix of In Eq. 9, 3 = λ4 and s are initialized:In this algorithm flow, λkNk(xi) refers to the k samples nearest to xi. Ft), thereby normalizing the vector. To reduce the computational complexity, the output data volume is reduced to 28 through μ companding conversion, that is, μ = 255, thereby improving the model’s forecasting efficiency.Essentially, SoftMax can map an The proposed model is trained through learning rate updating using the polynomial decay approach (Poly) . The equinit_lr is 0.0005 (or 5e–4), and power is set to 0.9.In Eq. 23, the initial learning rate zk describes the unnormalized logarithmic probability of pixel x in the k-th category under the given network parameter θ. In that case, the SoftMax function pk is defined as:The Weighted Cross-Entropy (WCE) is accepted as a cost function to optimize model training. Suppose that K represents the total number of image categories. During forecasting, once Eq. 24 reaches the maximum, pixel x will be labeled as the k-th category, namely k∗ = argmax{Pk}. A semantic segmentation task needs to sum the pixel data loss in each input mini-batch. Here, N denotes the total number of pixels in the training batch of image data, yi refers to the real semantic annotation of the pixel xi, and pk describes the forecasted probability of pixel xi belonging to the k-th semantic category, that is, the log-normalized probability, abbreviated as pik. Hence, the training process aims to find the optimal network parameter θ∗ by minimizing the WCE loss function ℓ, denoted as In Eq. 24, strategy  is adoptIn Eq. 25, η ∈  describes the symbolic function, which will take 1 if the condition is met and 0 otherwise. The weighted loss function for brain image fusion is defined as:qik = q(yi = k|xi) denotes the true label distribution of the k-th category of the pixel xi, wik refers to the weighting coefficient. The following strategy is employed during training:In Eq. 26, c is an additional hyperparameter, set to 1.10 based on experience during simulation experiments.In Eq. 27, In the present work, MATLAB is adopted for simulation to validate the performance of the proposed model. Brain image data in experiments come from MRI records of the Brain Tumor Department of a Hospital. It contains 20 groups of clinical data, and each group of data includes four kinds of multi sequence MRI images: T1, T2, Tlc, and FLAIR. The selected image data are processed by registration, skull peeling, contrast enhancement and other operations. Each case of MRI image contains the standard segmentation results given by artificial experts. Data collected are divided into a training dataset and a test dataset in 7:3; the ratio of each data type in the two datasets shall be consistent. Hyperparameters of AlexNet are set as follows: 120 iterations, 2,000 seconds of simulation, and 128 Batch Size. Some state-of-art models are included for performance comparison, including LSTM , CNN, RNThe similarity between the experimental segmentation result and the expert segmentation result can measure the model’s segmentation quality . Common T represents the expert segmentation result, and P denotes the segmentation result of the proposed model based on S3VMs and the improved AlexNet.In Eqs. 28–31), To explore the forecasting performance of the proposed model, LSTM, CNN, RNN, AlexNet, and MLP are included for comparison. Values of Accuracy, Precision, Recall, and F1 of these models are compared through simulation experiments, and the results are illustrated in As shown in According to In both the training and test sets, errors reduce gradually with iterations. MLP provides the longest time delay, 564.15 ms and 693.06 ms, respectively. In contrast, the proposed model provides a time delay approaching zero, the smallest among all tested models. Comparisons of RMSE and MAE values are summarized in According to The Jaccard coefficients, DSCs, PPVs, and Sensitivity of the proposed model, LSTM, CNN, RNN, AlexNet, and MLP are compared. The results are demonstrated in The higher the indicator value, the more accurate the segmentation result. As shown in The acceleration efficiency of the proposed model is analyzed through simulation experiments. The time required and speedup indicator of different models under different data volumes are presented in The proposed model is less sensitive to brain image data growth than other models. Hence, it is suitable to process massive data. Simultaneously, the larger the data volume, the higher the speedup indicator. To sum up, the proposed model can provide better data recognition and segmentation results than other models.A diagnosis and prediction model of brain image fusion digital twins based on semi supervised SVM and improved AlexNet is proposed to improve the accuracy of traditional CNN segmentation of MRI brain tumor images. The model mainly includes the preprocessing of brain image data based on semi supervised SVM. Then, the improved AlexNet network algorithm is used to extract and analyze the brain image features. Finally, through the comparative test and verification of simulation experiments, the model algorithm constructed can acquire more accurate segmentation results and prediction accuracy, and can effectively solve a series of problems in the process of MRI brain tumor image fusion, such as under-segmentation and over-segmentation, and meet the needs of clinical pathological diagnosis.Brain tumor images have complicated edge structures, artifacts, offset fields, and other defects that affect image segmentation. Extracting features from multi-sequence MRI brain tumor images becomes particularly vital. Both unlabeled and labeled data are utilized to construct the S3VMs regarding the massive number of unlabeled brain image data. Afterward, AlexNet is improved, and a brain image fusion DTs diagnosis and forecasting model is built based on S3VMs and improved AlexNet. Simulation experiments are performed to analyze the proposed model’s performance. Results demonstrate a 92.52% feature recognition and extraction accuracy, a 79.55% Jaccard coefficient, a 90.43% PPV, a 73.09% Sensitivity, and a 75.58% DSC of the proposed model, providing an experimental basis for brain image feature recognition and digital diagnosis. There are some weaknesses in the present work. The constructed model does not grade brain tumors. In the future, the texture feature, shape feature, position information, and volume of the multi-sequence MRI image will be extracted based on brain tumor segmentation results before being classified by ML and DL algorithms, achieving the key steps of precise brain tumor treatment and truly meeting the clinical needs, which will provide remarkable significance for the subsequent clinical diagnosis and treatment of brain tumors.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.ZW was responsible for the writing and implementation of the full text. YD was responsible for the implementation of the experiment. ZY was responsible for the collection of data and the writing of related research. HL was responsible for the implementation and data analysis of the experiment. ZL was responsible for the design of the experiment and the inspection of the results.YD was employed by Qingdao Haily Measuring Technologies Co., Ltd., The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.