In the production process of steel products, it is very important to find defects, which can not only reduce the failure rate of industrial production but also can reduce economic losses. All deep learning-based methods need many labeled samples for training. However, in the industrial field, there is a lack of sufficient training samples, especially in steel surface defects. It is almost impossible to collect enough samples that can be used for training. To solve this kind of problem, different from traditional data enhancement methods, this paper constructed a data enhancement model dependent on GAN, using our designed EDCGAN to generate abundant samples that can be used for training. Finally, we mixed different proportions of the generated samples with the original samples and tested them through the MobileNet V2 classification model. The test results showed that if we added the samples generated by EDCGAN to the original samples, the classification results would gradually improve. When the ratio reaches 80%, the overall classification result reaches the highest, achieving an accuracy rate of more than 99%. The experimental process proves the effectiveness of this method and can improve the quality of steel processing. In industrial production, defect classification is an essential step in the steel manufacturing process. However, this step is usually performed manually and mainly relies on the human eyes to observe defects on the steel surface. Therefore, production efficiency and product quality are generally low. The manual detection method is difficult to guarantee the detection effect. Even using human eyes to find defects for a long time may cause damage to human health. So, automatically detecting defects to replace manual labor in industrial manufacturing has become an important research direction ,2.In the industrial manufacturing environment, there are many difficulties in steel surface defect detection, such as slight differences, low contrast, too many noises, etc. . The metRecently, deep learning technology has exhibited excellent performance in many fields of AI. This method builds multiple layers of neural network models, to continuously adjust model parameters in the training process, and finally fits the task . There aAlthough these methods design various deep learning models, their results mainly rely on the supervised training process. For the shortage of large, labeled defect samples, they have to use networks with few layers, which are usually not deep enough and have no solid, expressive ability. The insufficient labeled training samples also lead to the risk of overfitting, resulting in this trained network model seeming to be less robust when testing in the natural environment. The deep learning network with insufficient labeled training samples is a problem in training deep networks and may seriously affect the expressiveness of the model. Therefore, for the task of supervised learning, a sufficient training dataset is essential . The training of such kinds of network models needs a large enough labeled dataset. Yet unlike life scenes, the defect samples in the industrial field are very few. Therefore, using data augmentation to expand the training data set is a strategy to solve the insufficient training dataset ,19. We dThe basic principle of a generative adversarial network (GAN) is that a generative model (G) generates fake samples through the input of random noises, then input these generated samples into a discriminant model (D) and uses the discriminant model to judge the origin of samples. By adversarial training of G and D, the samples generated based on the generative model are closer and closer to real samples, and the ability of the discriminator is also getting stronger and stronger. The network model of standard GAN can be seen in D to maximize the probability of calculating the correct label for the input samples, and train G to minimize the value of GAN uses the value function of Formula (1) to play a two-player minimax game. G and the discriminator (D) are optimized simultaneously. For the G, it is hoped that the image it generates is as closer to the real image as possible. The optimization function of the G is shown as Formula (2); the smaller its value is, the closer the generated image is to the real sample.During this process, the D can correctly distinguish the true or false of the input images; its optimization function is shown in Formula (3). The value is positive correlated to the discriminator model property.At the same time, it is hoped that the These above two functions are optimized repeatedly and alternately. Finally, the performance of the generator and discriminator reaches a balanced state that the generator cannot generate more realistic samples and the discriminator cannot correctly identify different samples.The advantage of the classifier based on deep learning is that it can efficiently learn more abstract features in the training process; without the need to design targeted artificial features. The classification accuracy is obviously improved. Convolutional neural network (CNN) is widely used in the field of surface classification due to their various performance advantages. CNN needs to be guided in the training process. It shows the characteristics of local connection, weight sharing, etc., which greatly reduces the number of parameters and the difficulty of model training. It can efficiently learn local data features and has good stability after various linear transformations of the image. In this process, the deep network model is mainly used to convolve; for nonlinear activation function mapping, and as a pooling layer to obtain the mapping relationship between variables. The structure of the CNN model is mainly composed of multiple convolutional layers and pooling layers with different functions. In the field of image classification, the classification network model using CNN has been widely used. Common classification algorithms are VGG , AlexNetCNN has been applied in the task of target classification; to pursue better accuracy, the depth of these models is getting deeper and deeper, and the models are getting more and more complex. However, when these models are used in real production, problems often happen. These models require powerful equipment and network support, but it is usually unavailable in the industrial field. Therefore, some network models redesign the structure to optimize the model in terms of speed and complexity. MobileNet V2 is one of these representatives; it is a lightweight deep neural network model built using depthwise separable convolutions that can effectively extract data space and channel features by equivalenting the general convolution method into two newly defined convolution operations. Additionally, two global hyperparameters are referenced in MobileNet V2, namely width multiplier: Compared with traditional high-performance network models, the number of parameters of MobileNet V2 has been greatly optimized, while the model’s performance is not significantly degraded. Hence, it is a lightweight network model that can ensure high accuracy. Moreover, it can be easily and quickly deployed on the mobile terminal and can even perform inference without network communication, so it is suitable for industrial fields with complex environments.For an input feature map, its size is, where the feature size is For the general DCGAN (deep convolution GAN) is a network model with deep convolutional layers, which adds the CNN layers to the GAN network. It is an improvement in the network architecture of the original GAN model. In DCGAN, both the generator and discriminator use CNN architecture; and can be properly optimized according to application requirements. The discriminator of DCGAN retains the architecture of CNN, while the generator applies fractional step convolution, which can better meet the performance-related requirements. Compared with other GAN models, DCGAN can flexibly choose the activation function and loss function, and different choices can directly affect the training results of the network model. Without the activation function, the output value can only be a linear expression of the input value, and the school performance of this model is very poor. The introduction of a nonlinear activation function can significantly improve the performance of the DNN network. It can accurately approximate any function, which is of great significance in improving the performance of the model. Activation function can directly affect the high-dimensional mapping ability from noises to meaningful images, so different activation functions can greatly affect the quality of generated images. In the original DCGAN model, batch normalization is applied after the activation function layer. However, batch normalization needs to calculate the variance and offset of the data distribution. As the network layers get deeper and deeper, more and more batch normalizations need to be performed, which leads to an increase in calculation during the training process. To enable the DCGAN model to generate better quality images, this paper constructed an EDCGAN model that uses the ELU function to replace the ReLU activation function in the original DCGAN. ELU activation function can not only generate a nonlinear relationship between the input and output but also can automatically normalize the input neuron data, eliminating the need for adding batch normalization in the DCGAN structure and reducing the computational complexity of neural network training. ELU is an improved version of ReLU; unlike the sparsity of the ReLU activation function, ELU can provide richer features as it preserves computations with less than 0 inputs; its activation function is shown in Formula (5). ELU also has the property of self-normalization, which can keep the output of this neuron self-normalized. Normalization of parameters is essential for neural networks. If there is no normalization process, the data distribution in a certain network layer will likely have a certain offset phenomenon. As the number of network layers increases, this problem will aggravate, leading to increased difficulty in model optimization; or even impossible optimization.In the EDCGAN we proposed, only the ELU activation function is introduced in the discriminative layer, while that in the generation layer remains unchanged. Because ELU retains the calculation of input less than 0, the calculation time will be slightly longer during forward propagation and backpropagation. Suppose it is used in large quantities in the discriminant network and the generation network at the same time. In that case, the difficulty of network optimization will further be increased. The discriminative network usually dominates the DCGAN training, and the generative network updates itself according to the discriminative network. Through adversarial training, the change in the high-dimensional mapping ability of the discriminative network will also be transmitted and adjusted to the generative network. Therefore, to obtain richer features without increasing the computational complexity, this paper only uses the ELU function in the discriminant network. Its structure of EDCGAN can be seen in flow Compared with the DCGAN model, the EDCGAN based on ELU has the following characteristics: (1) It makes full use of the self-normalization characteristics of ELU, omits the batch normalization layer, and reduces the special normalization calculation of parameters. (2) Only use ELU in the discriminant network and use ELU to calculate and extract richer high-dimensional features for data less than 0. (3) To cooperate with the characteristics of the self-normalized activation function, the normalization feature is used in the network, which not only prevents the over-fitting problem associated with the initial training of the discriminant but also speeds up the update speed of the network parameters. In the process of model training, using DropOut can discard some neural units, thereby reducing training complexity.. For different batches of input samples, because the discarded neural units are random each time, it is equivalent to training a different network for each batch. Using DropOut can reduce the computational parameters during training and overfitting. The principle of DropOut is shown in In the ordinary DCGAN structure, DropOut is not used; because the BN layer is included in those network layers, adding DropOut will complicate the calculation. The EDCGAN does not use the BN layer. The discriminant network and the generation network are mutually influenced, and they keep changing dynamically and alternately during the training process; there is no fixed number of iterations. Therefore, the introduction of DropOut can not only alleviate the over-fitting issue associated with the easy training of the discriminant network but also accelerate the speed of network updates.Using DropOut is usually to set an activation value To keep the mean and variance unchanged after adding the parameter After using DropOut, the calculation process formula of the previous item of the hidden layer parameter In Formula (9), The framework we designed mainly considers two practical problems: (1) In the industrial field, the number of data sets of metal surface defects is difficult to obtain, which makes training difficult. (2) In the manufacturing environment, there is usually no powerful computing equipment, so the selection of the classification algorithm is very important. We propose to use GAN models to generate more defect datasets to address the problem of insufficient datasets in the industrial domain. GAN is an adversarial game thinking see , we chosThis part showed the experimental results of our method used for the surface defect classification of steels. We first tested the classification results of MobileNet V2 under different proportions of the original samples and finally compared different classification methods. The hardware platform of all experiments is RTX3080Ti 12G graphics card with the Intel i9-13900K processor. The operating system is Linux, and the Scientific Computing Package Framework is Pytorch (1.13.0).http://faculty.neu.edu.cn/songkechen/zh_CN/zdylm/263270/list/index.htm accessed on 10 September 2022) is a defect dataset provided by Northeastern University [NEU-CLS  [In the experimental part of this paper, we split the NEU-CLS dataset into training and test sets with a ratio of 7: 3. Finally, we obtain a total of 1260 training samples per class and 540 testing samples in total. Compared with other datasets with massive samples, such as CIFAR , Caltechnt (SGD)  algorithFor the generative model, first, we fed a 100-dimensional random noise vector and then reshaped it to the size of In order to obtain more training samples, we use EDCGAN to obtain abundant fake defect samples to train the model. We send a total number of 1260 real defect samples into EDCGAN and, finally, gain 8000 fake defect sample images; all the sizes are AP) to evaluate the experimental results, which is a good compromise between two important detection metrics. These metrics can be expressed as follows:To analyze the influence of the generated samples on classification results, we use different proportions of the generated dataset in the training dataset. According to the different proportions of the original samples, this paper divides the dataset into six types. The proportion of the original samples is from high to low. In the dataset containing raw data samples, up to 100% of the raw samples are included and then decrease initially from 70%, 50%, and 30% to 20%. We also tested a dataset entirely generated by DCGAN that did not contain any original samples. We test the classification accuracy of MobileNet V2 under different proportions of the dataset to obtain the results, which can be seen in TP, FP, and FN refer to the true positives; and false positives/negatives, respectively. In which According to the result in To test the classification results more abundantly, we selected several other classification algorithms for comparative experiments. Specifically, the methods of Zhou  who applFrom the table above, we can find that the fully supervised learning method is highly dependent on the original sample, so when the data does not contain the original sample, the accuracy rate is not the lowest, only 90.51%. As the data set increases, the overall classification effect is improved for the other classification methods. When only 20% of the original samples are included, the accuracy of VGG reaches 99.23%, while when only generated samples are included, the accuracy drops slightly. Using the classification method of MobileNet V2, the highest average accuracy rate reached 99.11%, which is slightly lower than ResNet, but considering that the network structure of MobileNet V2 is simpler, it can save training time. Therefore, this paper can obtain the result that MobileNet V2 is more suitable for classification tasks in industrial scenarios.This study constructed a method that depended on EDCGAN and MobileNet V2 to classify steel surface defects. Considering the lack of original training samples, we propose an EDCGAN method to generate abundant new training samples. The generated samples are not transformations of the original samples, so it can better improve the diversity of the training dataset. To confirm the validity of generated samples, we use the NEU-CLS defect dataset as the experimental object, mix the generated samples with the original samples in different proportions, and then train on the MobileNet V2 model, and use the training results to test and compare the effect of including different proportions of generated samples in the training set on the classification results. Through many experimental comparisons, it can be shown that in the steel surface defect discrimination method, despite the number of original samples being limited, we can still increase the training samples through our method, thereby improving the accuracy. Finally, we compared the evaluation accuracy results of different algorithms. According to the result that both VGG and MobileNet V2 have higher accuracy, and MobileNet V2 has a simpler network structure, it is more suitable for steel defect classification tasks in industrial production.


Background: Effective science-based motor rehabilitation requires high volume of individualized, intense physical training, which can be difficult to achieve exclusively through physical 1-on-1 sessions with a therapist. Home-based training, enhanced by technological solutions, could be a tool to help facilitate the important factors for neuroplastic motor improvements.Objectives: This review aimed to discover how the inclusion of modern information and communications technology in home-based training programs can promote key neuroplastic factors associated with motor learning in neurological disabilities and identify which challenges are still needed to overcome.Methods: We conducted a thorough literature search on technological home-based training solutions and categorized the different fundamental approaches that were used. We then analyzed how these approaches can be used to promote certain key factors of neuroplasticity and which challenges still need to be solved or require external personalized input from a therapist.Conclusions: The technological approaches to home-based training were divided into three categories: sensory stimuli training, digital exchange of information training, and telerehabilitation. Generally, some technologies could be characterized as easily applicable, which gave the opportunity to promote flexible scheduling and a larger overall training volume, but limited options for individualized variation and progression. Other technologies included individualization options through personalized feedback that might increase the training effect, but also increases the workload of the therapist. Further development of easily applicable and intelligent solutions, which can return precise feedback and individualized training suggestions, is needed to fully realize the potential of home-based training in motor learning activities. Neurological disorders are the leading cause of physical disability in the world , 2, and Attention should be directed toward optimal motor learning based on current research, with the purpose of achieving the best functional result. Even though the functional outcome of the individual with a brain lesion is influenced by several factors, such as the location and size of the lesion , adaptatA systematic literature search was performed to identify original research studies through the databases PubMed/Medline, CINAHL, PEDro, and Embase according to the PICO criteria. Both MeSH terms and keywords (sometimes truncated) were used in different combinations. The words used in Population was: “Neurological disorders,” “Cerebral palsy,” “Stroke,” “Multiple sclerosis,” “Spinal cord injury,” “Traumatic brain injury.” The words used in Intervention was: “home-based (training/rehabilitation/exercise/activity/physiotherapy),” “video games,” “exergaming,” “telerehabilitation.” Comparator and outcome were not included in the search string, but instead used in the following screening. We selected studies where neurorehabilitation training was being performed in the home setting, facilitated by information- and communications technology. We were only interested in interventions performed outside of the clinic without the physical presence of a trainer. This meant rejecting studies where the majority of the training was carried out during home visits by a trainer/therapist. Home visits by trainers for introduction or adjustments of the training program were accepted. It also meant rejecting home-applicable training  where the project was performed in a clinic. For comparator group we only included studies, which contained a control group. The control group could be receiving a clinic-based intervention, a different home-based intervention or no intervention at all. The screening was performed by two independent researchers on first abstract/title and subsequently on the full text. Any disagreements were afterwards discussed case-by-case. The literature search identified 634 unique original publications, which were to be screened for eligibility according to this study's criteria. Four hundred twenty-five studies were excluded from the screening of title/abstract, which left 209 studies to assess from full text. A further 181 studies were excluded from the full text assessment, leaving 28 studies to be included. The most common reasons for study exclusion was lack of technological solutions, performance of the study in a laboratory setting or constant physical presence of a trainer. We then grouped the included studies into the following three categories: (1) “Sensory stimuli training,” defined as neurorehabilitation training where the training individual received live sensory input, but no live information was returned. (2) “Digital exchange of information training” where interactivity between sensory stimuli and the recorded actions of the individual was able to personalize the training course independent of input from the trainer. (3) “Telerehabilitation,” where the individual and trainer can communicate directly through the use of technology, closely mimicking the conditions of a clinical visit but without the physical presence.Home-based training provides the opportunity to be less dependent on the schedule and location of the therapist, and instead perform the training at the individual's optimal time of the day, which gives the chance to prescribe a higher training volume. One way to improve home-based training interventions includes technological inputs that involve sensory stimuli using simple and easy-to-use technology such as videos, audio-players or audio metronomes. Other interventions provide elaborate enriched environments of sensory stimuli tailored to the individual . These tActive participation during the practice of the tasks is necessary if the aim is to induce neuroplastic changes and promote learning and function –24. To cIt can be beneficial to improve motor function in familiar and everyday-applicable surroundings and cost-efficient both for the individual not requiring transportation and for the healthcare system if it can rely more on digital automated training delivery and less on physical 1-on-1 consultations. Home-based training provides new opportunities, but it also gives different challenges to solve. The lack of direct physical supervision challenges the training planning in terms of being certain that the training accomplishes optimal rehabilitation results. These include ensuring active participation during the training, providing the appropriate level of progressive challenge and variation, and giving precise feedback at the right time, which are all difficult without the trained eye of an experienced therapist. To increase the potential of home-based training, it is therefore important to consider how to promote the implementation of these principles of neuroplasticity in the home-based training program. Telerehabilitation is an emerging field within health and rehabilitation, and many definitions exist on what defines it. Videoconferencing is a common method and likely the method, which most closely mimics the conditions of a physical session in both strengths and weaknesses. The live attention to the individual makes it possible for the trainer to tailor the progression and variation in exercises to the individual's needs, to continuously set individual goals and give feedback on their achievement, and to facilitate the active participation in the completion of different tasks. One challenge to providing the right exercise variations or progressive challenges, however, is the selection of available equipment or usable objects in the home. One study attempted to improve the task options by providing simple manual objects and tasks  to promote manual dexterity training . This coThe main role of the therapist in relation to rehabilitation of individuals with disabilities due to neurological disorders is providing training that drives neuroplastic and functional changes in the optimal way. The continuous optimization of the training, which is important to make the best progression, currently requires involvement of the therapist. Clear goals accompanied by rewards are indispensable in this situation, but difficult to administer if the therapist is not present during the actual training. It is necessary to find methods that combine (1) high dose of training with (2) influence of the therapist ensuring the optimal performance of the training and motivation (3) time- and cost efficient for both the individual and rehabilitation system. The use of information and communications technology in home-based training is seemingly able to promote the different factors that are important for inducing neuroplastic changes, but each modality comes with its own set of opportunities and challenges. Generally, the training technologies are stuck in a trade-off between high dose/many repetitions and a high degree of therapist feedback and input unless they are highly resourceful in terms of providing personal time and attention. Digital automated feedback can be part of the solution and comes in the form of simple performance scores after an exergaming task or more complex movement analyses using movement sensors and trackers. Training programs involving specific tasks, requiring a low amount of motor skill and benefitting from many repetitions, can already make use of these information- and communications technologies, whereas more technological development might be needed to give the proper feedback in more complex motor tasks. The role of the therapist in this situation is to analyze the needs of the specific population and training tasks and prescribe the optimal training schedule and technological methods, keeping in mind that the best training outcome might results from a combination of different technologies. Also, until the use of information- and communications technology is developed further for use in training, many training programs will still require frequent personal input from the therapist in order to provide feedback, optimize training performance and motivate. Motivation is a critical element of rehabilitation to remember  and is iHome-based training, with the addition of technological solutions, can be of notable value to motor learning and neurorehabilitation by promoting various factors of neuroplasticity. Generally, home-based training can promote a larger training volume and more flexible scheduling according to the needs of the individual. With the addition of technology, important neuroplastic factors such as goal-setting, feedback, rewards, and motivation can be stimulated, enabling better results. Other factors of neuroplasticity can be slightly more challenging to include in home-based training such as progressive challenges, variation and ensuring the highest level of active participation. Some technological training solutions have attempted to solve this by including enriched physical environments or automated progression, but most often, the trainer is required to ensure the optimal inclusion of these factors, making it more expensive and labor-intensive. The general trade-off in the different technological solutions to home-based training therefore seems to be between more flexibility/training volume and optimal progression/variation/feedback. For that reason, identifying the most important factors of the specific motor learning program is key to implementing home-based training.CF wrote the first draft of the manuscript. CF, JN, and JL contributed to the design of the project, critically revised the manuscript, and approved the final version. All authors contributed to the article and approved the submitted version.This study was funded by a personal grant from the Elsass Foundation to Professor Jens Bo Nielsen (Grant number 19-3-1081).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.


Sinus of Valsalva aneurysm is a very uncommon clinical finding and often requires emergency surgery due to its high risk of rupture. This educational text reports the case of a 91-year-old Italian women who was incidentally discovered to have a huge double aneurysm of the sinuses of Valsalva. Sinus of Valsalva aneurysm is a very uncommon finding and often requires emergency surgery due to its high risk of rupture. This educational text reports the case of a 91-year-old Italian woman who was incidentally discovered to have a huge double aneurysm of the sinuses of Valsalva. The patient was admitted to the emergency department due to acute palpitation, chest pain and shortness of breath. Medical history revealed hypertension, dyslipidaemia and no other cardiovascular risk factors or previous event of cardiac arrhythmias. The admission electrocardiogram (ECG) showed the presence of atrial fibrillation with a pulse rate of 130 bpm, which was treated with intravenous infusions of amiodarone (bolus 300 mg/20 min and then continuous infusion of 900 mg/50 mL NaCl 0.9% at an infusion rate of 2 mL/h) that restored the normal sinus rhythm. Due to the anterolateral ST-segment depression  and seruWhat was the cause of the acute onset of atrial fibrillation in a patient with no history of cardiac arrythmias?If the electrocardiogram did not show any signs of myocardial ischemia, would you have performed a coronary angiogram or a CTA?Regarding the treatment options, what would be the surgical challenges?What was the main reason for choosing the medical treatment?The acute onset of atrial fibrillation in such an elderly patient with no previous history of cardiac arrythmias is usually related to high blood pressure rather than atrial enlargement related to mitral valve diseases (Question A).One of the most crucial points of this case was the presence of electrocardiographic signs of myocardial ischemia. If the ECG did not show anterolateral ST-segment depression along with abnormal serum troponin levels, restoration of sinus rhythm after amiodarone infusion probably would have not prompt clinicians to perform a coronary angiogram or CTA scan and the real problem would be missed. Idiopathic atrial fibrillation, indeed, is a common cause of palpitation, chest pain and shortness of breath in elderly patients (Question B).Due to the high risk of rupture, open surgery is usually the gold standard treatment. In this particular case, the patient should undergo a Bentall-De Bono procedure probably with a Cabrol technique to reimplant the coronary arteries. The sinus of Valsalva aneurysm has, in fact, moved the coronary ostia away from their natural position in relation to the aortic root, therefore the only way to reconnect them to the aorta would require the interposition of a prosthetic conduit. Considering the advanced age of the patient and her life expectancy, the procedure would have been very long and with a high risk of intra- and postoperative complications (Question C).The choice to address the patient for medical treatment only was driven by a multifactorial analysis based on the patient's age, the occasional nature of the finding, the high perioperative risk, the absence of signs of imminent rupture and the good blood pressure control at home (Question D)., and is usually an incidental report. When symptomatic, SVA usually present with signs of aortic rupture, angina (due to compression of the coronary artery), cardiac arrhythmias or with symptoms related to the compression of the neighbouring structures. A recent review showed that, as observed in this case, atrial fibrillation was the most reported arrhythmia (9%), while myocardial ischemia was present in only 5 cases among the 53 reported and was related to the high SVA dimensions. However, the reported review showed that the oldest treated patients that could be found in the literature were 84 years old. To the best of our knowledge, this could probably be the oldest reported patient in which a double sinus of Valsalva aneurysm remains asymptomatic until 91 years of age, therefore, considering the good blood pressure control obtained by patients at home with medical therapy only, the Heart Team decision was in favor of periodic follow-up. Unfortunately, we do not know the exact etiology of the double aneurysm, although we think it could be a congenital anomaly or, less likely, a consequence related either to hypertension or to a previous dissection limited to sinuses of Valsalva.Sinus of Valsalva aneurysm (SVA) is an extremely rare finding, affecting about 0.09% of the general populationEven in the absence of signs of myocardial ischemia, atrial fibrillation may be the unusual presentation of evolving aortic root diseases, especially in elderly patients with hypertension.Due to the high risk of rupture, urgent open surgery is usually the gold standard treatment for SVA.SVA is usually an incidental finding and may be missed or undiagnosed in asymptomatic or even symptomatic patients.


Intraoperative accidental extubation on a known difficult-airway patient requires prompt attention. A good understanding of the steps to re-establish the airway is critical, especially when the patient is known to have a difficult airway documented or discovered on induction or acquires a difficult airway secondary to intraoperative events. The situation becomes even more complicated if the case has been handed off to another anesthesiologist, where specific and detailed information may not have been conveyed. This simulation was designed to train first-year clinical anesthesia residents. It was a 50 min encounter that focused on the management of complete loss of an airway during a thyroidectomy on a known difficult-airway patient. The endotracheal tube dislodgement was simulated by deliberate tube manipulation through the cervical access window of the mannequin. Learners received a formative assessment of their performance during the debrief, and most of the residents met the educational objectives. Learners were asked to complete a survey of their experience, and the feedback was positive and constructive. The response rate was 68% (17/25). Our simulation program helped anesthesiology residents develop intraoperative emergency airway management skills in a safe environment, as well as foster communication skills among anesthesiologists and the surgery team. Complete loss of the airway during surgery is very dangerous and may have catastrophic consequences if not addressed promptly. The accidental removal of an endotracheal tube (ETT) may lead to temporary or permanent injury of the vocal cords if the ETT cuff is still inflated. It may also result in laryngospasms. After extubation, copious airway secretions may lead to aspiration and ultimately aspiration pneumonia. Unnoticed extubation may result in inadequate ventilation with possible sequelae of hypoxemia, hypotension, brain damage, cardiac arrest, and death ,2,3,4.2, abnormal mandibular subluxation (SLUX) grade, Mallampati class [III/IV], Cormack Lehane grade [III/IV], abnormal atlantooccipital extension grading, flexion/extension deformity of neck, cervical spine abnormality, protuberant teeth, and mouth opening of less than 3 cm. Additionally, the experience level of the anesthesiologist/laryngoscopist is a risk factor [This scenario has been developed to train the anesthesiology residents to experience causes of intraoperative endotracheal tube dislodgement that led to accidental extubation, steps to re-establish the airway on a known difficult-airway patient and communicate with the surgical team. The incidence of accidental extubation is not well documented in the OR setting, however, studies conducted in the intensive care unit (ICU) range from 0.5 to 35.8% in adults and 1% to 80.8% in the neonatal population ,7,8. Risk factor .When a surgical case is handed off intraoperatively to another anesthesiologist, it presents opportunities for various uncertainties. Even if the handoff was efficiently executed, it may not have been effective where specific and detailed information was not conveyed, such as the difficulty of mask ventilation and intubation, or at what depth the ETT was initially secured. Such uncertainties from the omission of information potentially delay the action to identify and solve the problem. We, therefore, reiterate the importance of effective communication during case hand-offs, including with surgical team members. This scenario was based on the author’s actual clinical experience.The case is presented for facilitators in the Simulation Case file . The SimThe anesthesiology residency program at the Indiana University School of Medicine has 26 residents per cohort. We provide regularly scheduled simulation training of approximately four half-days per year per resident. A small group of three to five residents from the same cohort attend a session that consists of three, one-hour simulation scenarios. The contents range from procedural workshops and perioperative anesthetic management to mock objective structured clinical examination (OSCE) training. The accidental extubation scenario was introduced in 2020 and has been run yearly for each clinical anesthesia-year 1 (CA-1) residency cohort in approximately their ninth week of training. One simulation faculty has served as the primary facilitator for running and iteratively updating this scenario.® or i-gel®), video laryngoscope, stethoscope, and a standard set of simulated anesthesia drugs) were in the OR. A code cart was available outside of the OR. The observation, facilitation, and programming of the scenario were conducted via wireless audio and video feed from the mock OR to a separate control room that was concealed from the learners.We aimed for the highest levels of fidelity and realism. Participants were provided proper surgical attire for each role, such as scrubs, masks, hats, gloves, patient gowns, and surgical gowns. The OR could be either a real (in situ) or mock/simulated laboratory. This simulation occurred in a mock OR setting. The mannequin, operating table, monitors, and equipment , a high-fidelity lung simulator (IngMar ASL 5000 Lung Solution), an interconnected software interface (LLEAP), and a decommissioned yet fully functioning anesthesia machine (Dräger Apollo). An EKG monitor, blood pressure cuff, and pulse oximeter were connected, and the vital sign data were displayed on the simulated patient monitor. The mannequin was positioned supine on the operating table with one peripheral IV in the upper extremity, intubated, and on the anesthesia machine ventilator. A shoulder roll was placed under the mannequin and the neck in full extension . A surgiRole assignments for the simulation were flexible depending on the number of participants. The minimum requirements for this scenario were: one learner anesthesiologist (target learner) who takes over the case, one anesthesiologist  who hands off the case to the learner, and one surgeon EP. Additional residents could be EP surgical scrubs or circulator nurses. Small groups of three to four anesthesiology residents participated in the scenario. One resident was in the “hot seat” as the learner anesthesiologist and the others were assigned EP roles. Faculty members and medical students also served as simulated participants as needed. Simulation operation specialists programmed and operated the mannequin and simulated monitors. The faculty facilitator was also in the control room during the simulation and gave verbal cues and instructions through headsets worn by the EPs.The EP anesthesiologist was prebriefed  and instThe scenario started with the patient intubated, under general anesthesia, as the surgeon worked on the total thyroidectomy. The patient’s history was given as morbidly obese, with a large neck circumference, and a known difficult airway. The EP anesthesiologist told the learner anesthesiologist that “this patient was difficult to mask ventilate due to his beard and big tongue, and the intubation was accomplished by utilizing a video laryngoscope”. The anesthesiologist mentioned a minor ETT leak was heard from the patient’s oral cavity, and the issue was resolved after adding 10 mL of air to the cuff.The facilitator instructed the EP surgeon to mimic accidental extubation by deliberately pushing out the endotracheal tube from the trachea . Kelley The learner anesthesiologist heard the cuff leak sound as the ETT was dislodged by the surgeon. As they tried to troubleshoot, the ventilator alarm sounded, and the bellows completely collapsed . The aneThe scenario ended when the anesthesiologist successfully re-established the airway and communicated with the surgeon to resume the surgery. We found that the simulation took approximately 50 min, including the prebriefing of the participants.The facilitator reviewed the completion of the Critical Actions Checklist . LearnerThe debriefing was held immediately following the conclusion of the scenario and lasted approximately 20 min. All the participants, including observers, relocated to a nearby classroom; alternatively, they could have remained in the mock OR for the debriefing. The debriefing session began with an open-ended question to the learner who was in the “hot seat” about how they felt the scenario went. This is a strong tool to elicit spontaneous reflections from the learner as well as stimulate active discussions from the other participants. The following elements and talking points are recommended during the debriefing with the learners.1Communication between the anesthesiologists handing off and taking over: It is a common practice to sign out an ongoing case to another anesthesiologist. Because the handoff takes place in a busy operating room setting, it is common for there to be frequent interruptions. The learner anesthesiologist should discuss how to perform handoffs efficiently and without omissions. Were there omissions of any critical information? What did the anesthesiologist taking over assume when they were informed of a leak that was resolved by adding 10 mL of air to the ETT cuff? Did both anesthesiologists attempt to discuss or explore the root cause?2Maximum cervical extension: Although the endotracheal tube was initially taped at the correct depth, it was pulled out with cervical extension and the cuff partially herniated out of the vocal cords enough to create an air leak .Adding more cuff air to seal the leak: As more cuff air was added to remedy the leak without repositioning the endotracheal tube, the tube continued to slide out although the air leak seemed to have temporarily resolved. Only the distal end of the endotracheal tube remained beyond the vocal cords while the cuff was inflated with more than 20 mL of air .Surgical manipulation of the neck: The vibration and movement transferred from the surgical manipulation of the neck dislodged the endotracheal tube completely from the vocal cords .The cause of accidental extubation: The facilitator lists the three factors that contributed to the migration of the endotracheal tube and resulted in accidental extubation. The learners discuss these factors:3Recognize accidental extubation: Learners discuss what alarms they received when the integrity of the airway was lost. What steps should the learner take to isolate the problem and discover the accidental extubation?4Communication with the surgical team: Emphasize the importance of notifying the loss of the secure airway and asking for help. Should they immediately activate Code Blue in this situation?5Initiate bag-mask ventilation: Obstacles are secretions, facial hair, large tongue, maximum cervical extension, head distance from the bag and anesthesia machine, and field avoidance.Insert supraglottic airway: Obstacles are unavailability of the device, incorrect device size, and large leak from poor seal due to anatomy and/or size.Draw up drugs required for reintubation: Obstacles are drugs not immediately available in the OR, no one to help draw up drugs.Reintubate: Obstacles are known difficult airway, maximum cervical extension, inexperienced laryngoscopist, unavailability of equipment: video laryngoscope, difficult airway cart, adjunct devices .Re-establishment of the secure airway: Learners discuss the steps and the potential obstacles in re-establishing oxygenation and ventilation on a rapidly de-saturating patient with a known difficult airway.All simulation sessions involved formative learning and assessment to benefit the residents. The residents met the educational objectives and performed very well by promptly recognizing the accidental extubation and communicating with the surgical team. They quickly attempted mask ventilation and once that failed, they utilized a video laryngoscope to reintubate the known difficult-airway patient. The facilitator redirected the learner anesthesiologists by giving verbal cues to the EP surgeon via a microphone and earpiece.Residents were asked to provide feedback at the end of each session. A QR code linked to the online survey was posted on the wall of the debriefing room, and email reminders were sent with the survey link to increase the response rate. The overall feedback from our learners was positive with a survey response rate of 68% (17/25) for AY 21–22 . ConstruThe purpose of the development of this simulation was to enable residents to practice crisis management skills for potential perioperative incidents as well as to foster excellent communication skills with patients and the perioperative team as mature anesthesiologists. We endeavored to provide a psychologically safe learning environment, from scenario design, the prebrief with the basic assumption , scenariAlthough the endotracheal tube was secured at the proper depth immediately after intubation, it may become displaced due to subsequent positional changes, surgical manipulation, or loosening of the tape securing the tube. Since this case was a thyroidectomy, the patient’s neck was maximally extended with a shoulder roll. One report showed that an ETT can be displaced cephalad by approximately 7 to 20 mm with full neck extension . The deg2 and improved oxygen saturation on the patient monitor.Once the learner anesthesiologist realized that the endotracheal tube had been accidentally extubated, they were forced to re-establish ventilation. However, the mannequin was programmed to be very difficult to mask ventilate. With the patient rapidly desaturating, there was not enough time to draw up propofol, muscle relaxants, or prepare a video laryngoscope, and the airway had to be temporarily secured with a supraglottic airway. The SimMan3 mannequin is not suitable for insertion of a supraglottic airway, and this becomes a limitation of this simulation. Even with adequate lubrication, it is difficult to insert the supraglottic airway into this mannequin to the proper depth and achieve an adequate seal. Therefore, when residents attempted to insert the supraglottic airway, even incomplete insertion should be considered as a re-establishment of ventilation. To make it evident, we displayed ETCODue to time constraints, one of the limitations of this simulation was that only one or two residents from each small group were placed in the “hot seat” as the learner anesthesiologist. Although feedback from the other residents indicated that they also learned from the simulation, it remains to be seen if there is a difference in the effectiveness of training between the residents in the “hot seat” and those in the other roles. Exploring these questions and possible scenario adjustments are the subjects of our future research.One of the reasons for the low response rate on the residents’ survey (68%) was that we did not make the residents finish the survey immediately after the completion of the debriefings since they all had to resume their regular clinical anesthesia tasks promptly. It is well known that the debriefing should occur immediately after the simulation since the thoughts and feelings will fade fairly quickly . A simil


Emotions are an essential drive in decision making and may influence consumer preference. In this study we assessed the influence of brands in product preference after tasting 2 soft-drinks (Coca Cola vs. Cola beverage), by using physiological measurements, namely the skin conductance, the respiratory rate and heart rate variability (HRV) extracted using contactless sensors. The experimental protocol enrolled twenty-five young subjects which were asked to taste 2 soft drinks in random order, without knowing the brand (blind condition) and then knowing the brand (not blind condition). After each phase the subject was asked to choose the preferred beverage. Our main hypothesis is that if the subject knows the brand there is an arousal effect, independently from the absolute appreciation of the product. In order to evaluate the emotional components, the skin conductance, respiratory and Video-Photoplethysmographic (PPG) signals were continuously recorded throughout the experiment. The Video-PPG was then processed to extract HRV parameters. We observed that the arousal levels changed among beverages and conditions, going from higher arousal for Coca-Cola in the blind condition, to higher arousal for Cola in the not blind condition. Moreover, 44% of the subjects changed their preference when the brand was uncovered: from blind to not blind conditions, 6 subjects went from Cola to Coca-Cola as preferred drink and 5 went from Coca-Cola to Cola. Opposite results were found for the two beverages when comparing the physiological response when the beverage was/was not preferred. Finally, differences were found also between consumers and not consumers of Coca-Cola and the blind/not blind comparison. We conclude that the brand is a fundamental element in a request for choice and it can affect the first emotional response of a subject. In a world always keener on consuming, companies are increasingly forced to be one step ahead of others, in particular to understand or even predict people needs. Thus, several efforts have been made to understand the various influences behind a customer choice. Since the book by Damasio in 1994, it is commonly recognized that emotion is an inseparable and central component in decision making , evaluatA particular subsection of the emotional component response is linked to the power of brand , 5. McClThe experiment assessed the emotional response elicited by tasting a beverage. In particular, we used the influence of a brand such as Coca-Cola in a request for choice. The study involved human subject research and it was approved by the Politecnico di Milano research ethical committee (comitato etico della ricerca del Politecnico di Milano). Informed written consent was obtained from each participant before starting the experiment. The experiment population comprised of 25 young subjects  with an age of 20.5 ± 2 years (mean ± standard deviation). The participants were asked to test two beverages, a brand product (Coca-Cola) and a similar soft drink (Cola), the first time without knowing the brand  and the second time with the knowledge . The volume of the beverage was controlled (around 7 cl) and the glasses were not transparent. After both tastings, they were asked to indicate which beverage they preferred.The experimental setup comprised an Imaging Source DFK23UM021 camera with 15 mm lens, recording the video of the face of each subject with a framerate of 115 fps and a resolution of 640x480 pixels. The camera was placed in front of the seated subject at a distance of about 1 m .Simultaneously to the video, the SC and respiratory signal were recorded through a FlexComp Infiniti encoder (Thought Technology Inc.). The data were collected using BioGraph Inifiniti software (Thought Technology Inc.) with a sample frequency of 256 Hz and a resolution of 14 bits. The sensors for SC were positioned on the index and ring fingers of the non-dominant hand , to makeIn In order to extract the heart rate and HRV metrics, the camera video was processed following the steps depicted in SS);Average peak-to-peak temporal distance (std_SS);standard deviation of SS intervals (std_dSS);standard deviation of the differences between consecutive SS intervals (pNN50);percentage of successive interval differences greater than 50 ms (RMSSD);the root mean square of successive differences ;the frequency with highest power in the low frequency band ;the frequency with highest power in the high frequency band  to remove slow head movements and ambient noise, the video-photoplethysmographic signal (vPPG) was extracted through the Zero-Phase Component Analysis method . On the In order to assess the changes in cardiac parameters due to the protocol stage, the signals were compared to the individual baseline level through the IBI and std_IBI, respectively) in the baseline and tasting phases were computed and standardized as in Each respiratory peak was found on the respiratory signal as the sample larger than its two neighboring samples, under the condition to be at least 3 seconds from the preceding peak, considering that the normal respiratory rate at rest ranges from 12 to 15 breaths per minute , 18. In avg stands for average of the evaluated metric called Index. The subscripts identify the considered stage of the protocol.The SC signal was decomposed in 2 components, the phasic skin conductance response (SCR) and the tonic skin conductance level (SCL), using the cvxEDA algorithm . In Fig SCLz and SSz are the Z-scored variables of SS and SCL, respectively, and θ is the arctang.The Emotional Index (EI)  was compThe index is based on the circumplex model of affect plan , where tsame beverage group (paired test)when Coca-Cola was preferredwhen Cola was preferredBlind (B) vs not blind (NB)Coca-Cola vs Cola when preferredBeverage preferred vs not preferredConsumers vs not consumers—all beverages and conditionsCoca-Cola vs Cola in consumers and not consumers groups.All signals were visually inspected to discard noisy or low quality segment that could compromise the analysis. Furthermore, the outlier values were identified through the interquartile range method and removed. A two-sided Wilcoxon rank sum test was applied for each comparison. Five main comparisons were carried out:As an exploratory study, a p-value<0.1 was considered statistically significant. Matlab R2019b  was used for all the analyses.Quantitative results obtained from the analysis of physiological signals are reported in Tables In Product preference analysis: Among the 25 subjects, 11 (44%) changed their preference after they tried the branded beverage: six subjects that chose the Cola in the B condition changed their preference to Coca-Cola when the brand was known. Furthermore, 5 subjects that chose Coca-Cola in the first phase, changed their preference to Cola in the NB phase.Blind vs not blind analysis: The parameters related to the SC were higher in the NB phase  and the population that rarely or never drinks the famous soft drink (not consumers). It emerged a lower SCR for consumers together with higher pNN50, lower LF power and higher EI  the significant difference found in the skin conductance parameters between the two preferred beverages in both experimental conditions; 2) the changes of preference observed from the B to the NB condition; 3) the changes in physiological response obtained by comparing the usual consumers of Coca-Cola and the subjects that rarely or never drink the beverage; 4) the differences between the 2 soft drinks observed comparing the physiological response recorded when a beverage was later chosen as preferred or not.SS and std_SS were higher and the SC features lower in the B condition, suggesting a lower emotional load [Regarding the first point, while in the B condition the SCR values were higher for the Coca-Cola and both the LF peak frequency and its power were lower, in the NB condition the SCR was higher for the not famous brand, probably due to a surprise factor, as confirmed by the lower HF power see . The chanal load . Coherennal load . Furthernal load .Regarding the second point, 11 subjects (44%) changed their preference after they tried the beverages knowing the brands. The 6 subjects (24%) that changed preference from Cola to Coca-Cola provide evidence that the knowledge of the brand affects subject’s choice. The 5 subjects that changed from Coca-Cola in B condition to Cola in NB conditions were not habitual consumers of soft drinks in general. A possible explanation of the change resides in the fact that, not being attached to the brand name, they probably wanted to step back from the icon of the most known soft drink.Another finding highlighting the difference between the usual consumers of Coca-Cola and the population that rarely or never consume the soft drink was the different physiological response measured in the 2 cases see : the lowRegarding the last point, it is interesting to note that no index showed a significant difference comparing the trials in which the beverages were later chosen as preferred and the trials in which the beverages were not chosen. However, considering the two beverages separately, we obtained opposite results based on the soft drink considered see : for ColThe main limitation of the study was the low number of subjects, all young Italians of similar socio-economic background and lifestyle. The number of Coca-Cola consumers/not consumers was biased in favor of the first group and the weekly consumption of other soft drink was not investigated. Moreover, in order to assess the effect of brand on product choice and on physiological reactions, different brands should be considered in possibly different fields. In future studies, a larger and more heterogeneous population could be selected, with the possibility to assess consumers behaviors in different age, sex and cultural subgroups.The proposed study focused on the evaluation of consumer preference by means of physiological signals. In particular, a comparison between the well-known Coca-Cola beverage and a similar soft drink was performed in both blind and not blind conditions. The preferred beverage was asked at the end of each test. In summary:We demonstrated that a brand effect is present, as assessed by skin conductance parameters between the 2 preferred beverages in both experimental conditions and the change of preference by 44% of the subjects. Moreover, the effect of the brand was visible by comparing the physiological responses when a beverage was chosen as preferred or not. Indeed, while aggregating both beverages in the analysis no parameter showed a significant difference in the two cases, splitting in accordance with the beverage highlighted an opposite, brand-related behavior.The physiological results and the changes of preference observed between the two phases show that knowing the brand of a popular product can have an immediate emotional response. As a consequence of these findings, future marketing related studies and protocols should be performed taking into consideration that knowing the brands can influence even the first emotional response.Similar studies should be done considering the effects of different brands in different fields, possibly considering also other senses (such as smell and touch). Furthermore, further studies should be done considering a more heterogeneous population.In conclusion, this study paves the way to a better understanding of the brand mechanisms behind brand conditioning and the bias that brands introduce in the consumer’s experience and preference as well as, more in general, to a more quantitative characterization of any subjective behavioral bias from the surrounding physical and social environment.S1 Data(XLSX)Click here for additional data file.


The early detection and management of root fractures pose a challenge to dentists. There are two treatment options in such cases: conservative and endodontic or surgical. Follow-up of such cases involves the continual evaluation of the tissue repair process and the possible occurrence of undesirable events, such as periapical lesions, pulp calcification, and internal or external resorption. This case report describes the finding of a periapex with relatively extensive bone rarefaction resulting from a root fracture in a 64-year-old male who presented with gingival hyperplasia in the right maxillary lateral incisor region sensitivity at the touch of the exploratory probe and mild bleeding with exudate. In this case, the early diagnosis of root fracture enabled the choice of the best therapeutic option with a satisfactory outcome. The early detection and management of root fractures pose a challenge to dentists. Root fractures involve the cementum, dentine, pulp, and periodontal ligament and can occur in the cervical, middle or apical third, with or without mobility and with or without displacement . The preThe two treatment options for root fracture are conservative and endodontic or surgical. Follow-up of such cases involves the continual evaluation of the tissue repair process and the possible occurrence of undesirable events, such as periapical lesions, pulp calcification, and internal or external resorption. Thus, it is essential to maintain patients in clinical and radiographic follow-up . This paA 64-year-old male sought dental care. The intraoral clinical examination showed gingival hyperplasia in the region of the right maxillary lateral incisor Tooth 12). The patient reported sensitivity to the touch of the exploratory probe, and mild bleeding with exudate was observed. The periapical x-ray revealed a radiolucent image of the periapex with relatively extensive bone rarefaction resulting from a root fracture . The pre-anesthetic was 200 mg/g of topical benzocaine , followed by the local anesthetic of phenylephrine lidocaine . The nucleus, crown, and remaining root were surgically removed, followed by curettage irrigated with saline solution. The suture was performed with 4-0 Shalon® silk . The following day, the temporary tooth was placed with an adhesive system for enamel and dentine  using Z-250 polymerizable composite resin .At the six-month follow-up, the periapical x-ray revealed a reduction in bone rarefaction in the region of the right maxillary lateral incisor Figure . Bone foTraumatic dental injuries are recognized as a public dental health problem worldwide, and these injuries are more prevalent in permanent dentition . AccordiTreatment for this type of fracture depends on the location of the fracture line, proximity to the gingival sulcus, tooth mobility, and the pulp tissue condition . In thisRegarding etiologic factors for root fracture, a metal pin concentrates stress on the root due to the high modulus of elasticity, leading to a high incidence of root fractures . This waConsidering that the detection of root fracture is commonly challenging for clinicians, mainly when the results from the typical clinical diagnostic tests are indecisive, the root fractures require the proper radiographic diagnosis to determine the extent and therapeutic options, along with a complete assessment of the history of the case and clinical examination of the tooth structures and bone. Moreover, the follow-up of the patients’ oral hygiene and radiographic control to detect early signs of any disease are essential in trauma cases.In the present case, the precise diagnosis of a periapex with relatively extensive bone rarefaction resulting from a root fracture in a 64-year-old male who presented with gingival hyperplasia, sensitivity at the touch, and mild bleeding with exudate, enabled the choice of the best treatment option, which contributed to successful treatment, as demonstrated during the clinical and radiographic follow-up evaluations. Based on this context, dentists should be adequately trained and aware of the importance of a precise diagnosis in such cases.The report of this clinical case of an elderly patient shows that early diagnosis of root fracture enabled the choice of the best therapeutic option with a satisfactory outcome, as demonstrated at the six-month clinical and radiographic follow-up evaluation. When the typical clinical diagnostic tests are indecisive, the root fractures require the proper radiographic diagnosis to determine the extent and therapeutic options, along with a complete assessment of the history of the case and clinical examination of the tooth structures and bone. Dentists should be adequately trained and aware of the importance of a precise diagnosis in elderly patients with a periapical lesion resulting from a root fracture.


In industrial production, flaws and defects inevitably appear on surfaces, resulting in unqualified products. Therefore, surface defect detection plays a key role in ensuring industrial product quality and maintaining industrial production lines. However, surface defects on different products have different manifestations, so it is difficult to regard all defective products as being within one category that has common characteristics. Defective products are also often rare in industrial production, making it difficult to collect enough samples. Therefore, it is appropriate to view the surface defect detection problem as a semi-supervised anomaly detection problem. In this paper, we propose an anomaly detection method that is based on dual attention and consistency loss to accomplish the task of surface defect detection. At the reconstruction stage, we employed both channel attention and pixel attention so that the network could learn more robust normal image reconstruction, which could in turn help to separate images of defects from defect-free images. Moreover, we proposed a consistency loss function that could exploit the differences between the multiple modalities of the images to improve the performance of the anomaly detection. Our experimental results showed that the proposed method could achieve a superior performance compared to the existing anomaly detection-based methods using the Magnetic Tile and MVTec AD datasets. Over recent years, surface defect detection has attracted attention in various fields, such as transportation ,2,3, agrAnomaly detection has been widely used in various fields, including cyber security ,14, commAlthough both the GAN-based method and AE-based method could solve the problem of the small number of defective samples, the GAN training requires expensive computational resources and the reconstructed images from the AE differ greatly from the input images, resulting in low defect detection accuracy. To overcome these problems, this paper addresses the challenge from three perspectives. First, in order to quickly match the latent vectors that were closest to the normal image distribution, we reconstructed the defect-free images using an encoder–decoder network, thereby avoiding the process of updating the input vectors to capture the normal image manifold. Second, to further enhance the representation ability of the extracted features, a novel attention mechanism that utilizes the parallel fusion of channel attention and pixel attention was added to the encoder network to enhance the detailed information attention of the network. Third, the latent vector that was extracted by the network only contained the features of the normal samples and it was not clear whether the features of abnormal samples were also learned. Therefore, this study proposed a new consistency loss function that was based on the pixel consistency, structural consistency and gradient consistency of the images to further improve the ability of the network to reconstruct normal samples, inhibit abnormal reconstruction and improve the accuracy of the detection of defective samples.An encoder–decoder generative adversarial network is proposed that directly maps image spaces on to latent spaces;A novel dual attention block is proposed within the encoder network;A consistency loss function is proposed to enhance the ability of the network to reconstruct defect-free images.To summarize, the contributions of this paper are threefold:This paper is organized as follows. At present, surface defects on industrial products seriously affect the quality and efficiency of production and a number of industrial enterprises have introduced products that are related to the detection of surface defects. Cognex’s deep learning defect detection tool can learn to find a variety of unacceptable product defects throughout the manufacturing process. This tool inspects the screen, band and back of a smartphone before it is packaged. It is used to detect any combination of dents, scratches and discolorations anywhere on the smartphone. Zeiss proposed SurfMax, which obtains three different modes of captured images  based on deflection measurements using a high-resolution Zeiss optical sensor. It completely captures the relevant surface features and is then combined with machine learning methods to carry out surface defect detection in automotive, aerospace, medical and consumer electronics manufacturing. Creaform designed 3D scanners for the non-destructive inspection of gas pipelines and aerospace surfaces. Therefore, surface defect detection has become a research hot spot for some companies at present. In this section, we summarize the related work within surface defect detection and anomaly detection.According to the extracted features and detection algorithms, the traditional surface defect detection methods within image processing can be divided into three categories: the statistical method , frequenIndustrial surface defect detection can improve the qualified rate and overall quality of products and it is used in a variety of tasks. Therefore, many defect detection algorithms have been proposed ,37,38,39Anomaly detection, also known as outlier detection or novel detection , refers In this section, we first introduce the proposed framework for the detection of industrial surface defects  operation on the feature map The discriminative network consisted of a convolutional layer and a batch normalization layer. The network received the real input image and the corresponding reconstructed image and then output a scalar value. After the Sigmoid function operation, the scalar value range was limited to between 0 and 1. The discriminator output a large scalar value (close to 1) for the real input image and a small scalar value (close to 0) for the reconstructed image. As the reconstructed image became more and more realistic after reaching the Nash equilibrium , the recIn the training phase, the encoder performed feature extraction on the input defect-free image and mapped the image on to the latent space. The decoder then reconstructed the extracted latent feature vectors into a pseudo-image. The discriminator distinguished between the input image and the pseudo-image and output a discriminant score, which eventually caused the pseudo-image that was reconstructed by the decoder to become infinitely closer to the input image.First, the generative network weights and the discriminative network weights were initialized, then the generative network weights were fixed and the discriminative network weights were updated. The discriminative loss adopted the binary classification cross-entropy loss within the classical GAN;After the discriminative network weights were updated, the discriminative network weights were fixed and the generative network weights were updated. Adversarial loss and consistency loss were introduced when updating the generative network weights.The training process of the entire network could be described as follows:The adversarial loss reduced the GAN’s training instability through feature matching. The To enhance the retention of the pixel and detailed information in the input image, the introduced consistency loss considered not only the pixels, but also the structural consistency and gradient consistency between the input image and the reconstructed image. The pixel consistency exploited the differences between the pixels in the input image and the reconstructed image to improve the image reconstruction ability. The structural consistency used SSIM to compare the real input image to the reconstructed image in terms of brightness, contrast and structure. The gradient of the image could reflect the frequency of image changes and improve the reconstruction quality of the high-frequency parts of the image. The consistency loss could be defined as:The adversarial loss and consistency loss were combined to update the total loss of the generative network parameters, which was indicated as:In the testing phase, since the network could only reconstruct defect-free images, normal images were reconstructed from unseen defect images. Therefore, the inputs and outputs of defect images were quite different, especially around the defective areas, so the anomaly score could be obtained through using the discriminative network.Assuming that the trained generative network was good enough to reconstruct defect-free images, we used the absolute value of the pixel-by-pixel difference between the query image and the reconstructed image as the anomaly score. Given the different thresholds for the different datasets, the anomaly score was determined as an anomaly when it was greater than the relevant threshold. The anomaly score was defined as:S, which was restricted to  by feature scaling. The final anomaly score could be expressed as:S, respectively.Using Equation , we wereIn this section, we evaluate the proposed method in terms of the surface defect detection problem. We first present the datasets that were used, followed by a discussion of some of the training details and evaluation metrics that were used in the experiments. Finally, we compare our method to several existing defect detection algorithms. Using the MVTec AD dataset , we compThis experiment used the MVTec AD dataset  and the MVTec AD is a real-world dataset of industrial surface defects with 5354 high-resolution images. The dataset contains 15 different industrial product surfaces, each of which is divided into a training set and a testing set. The training set only contains defect-free images, while the testing set contains both defect-free images and 70 types of defect images. The details of the MVTec AD dataset are shown in The Magnetic Tile dataset has 1344 grayscale images under multiple illumination conditions, including 952 defect-free images. We randomly selected 80% as the training set and the remaining defect-free images and 392 defect images were merged together as the testing set, which included six defect types: blowhole, crack, fray, break, uneven and free. All of the images had pixel-level labels, as shown in To enhance the robustness of the generative network for defect image reconstruction, we used Random Erasing  data enhFor all of the experiments in this paper, we employed five convolutional layers and used a dual attention block after each layer as the encoder network. The latent vectors were reconstructed after five deconvolutional layers. For the training of the generator and discriminator networks, we set the batch size to 64, used Adam  as the oTo evaluate the performance of the proposed method for defect detection, the AUC  value waIn this subsection, we compare several popular reconstruction-based defect detection methods to verify the superiority of the proposed method for the surface defect detection problem.First, we compared the performance of the proposed method to several other defect detection methods: AnoGAN , GANomalTo highlight the superiority of the proposed method, we plotted an AUC line chart for each category of the MVTec AD dataset (as shown in Then, the proposed method was further verified using the Magnetic Tile dataset. Compared to GANomaly and Adgan  (Adgan uThe results of the sample study using the MVTec AD and Magnetic Tile datasets are shown in In this subsection, we present the results from our ablation studies, in which we performed a group of experiments to verify the effectiveness of the individual strategies within our proposed model, mainly from two perspectives: the effectiveness of the dual attention block and the effectiveness of the consistency loss.Struc1. Multi-scale channel attention was introduced into the encoder network to evaluate the effects of channel attention on the defect detection performance, which was named Struc2. Then, the channel attention in the encoder network was replaced with pixel attention to evaluate the impacts of pixel attention on the defect detection, which was named Struc3. Finally, our proposed method was named Struc4. As can be seen from Struc2 and Struc3 had higher AUC values for some categories in the MVTec AD dataset, the encoder network for the parallel fusion of multi-scale channel attention and pixel attention could effectively extract the key information so Struc4 achieved a mean AUC value of 90.2%. Using the Magnetic Tile dataset, we counted the running speed that was needed to train and test each image for the different structures. Although Struc4 took a little longer than the other structures, it achieved the highest result of 84%. As shown in Struc4 had less noise in the defect heat map and could detect defects more accurately.We fixed our proposed consistency loss as the loss function of the generative network by changing the attention in the encoder network and constructing four different structures. First, the dual attention block was removed from the encoder network to evaluate the defect detection performance without the attention mechanism, which was named  to evaluate its impact on the image reconstruction ability. Second, based on the pixel consistency loss function, the structural consistency loss function was added and the two functions were used as the generative network loss function to evaluate the generation effects, namely . Third, we combined pixel consistency, structural consistency and gradient consistency to constitute the consistency loss function, namely . The experimental results are shown in Without changing the dual attention network structure, we considered the loss function in the network in three ways. First, the loss function in the generative network used the pixel consistency loss function We studied the problem of detecting surface defects during the production process of industrial products, i.e., the wide variety of surface defects among different products and the difficulty in collecting defective samples. Therefore, we proposed a semi-supervised anomaly detection method that was based on dual attention and consistency loss to accomplish this task. We used an encoder–decoder structure for the generative network and introduced a dual attention module into the encoder network, which combined multi-scale channel attention and pixel attention. The parallel fusion of the two kinds of attention mechanism improved the performance of key feature extraction and reconstructed higher quality defect-free images. In addition, the consistency loss made use of the differences between the pixels, structures and gradients of the defect images and defect-free images to further improve the performance of the defect detection. Comprehensive experiments using the MVTec AD and Magnetic Tile datasets showed that the proposed method could achieve a superior performance over the existing methods.


The aim of this study is to show the usefulness of collaborative work in the evaluation of prostate cancer from T2-weighted MRI using a dedicated software tool. The variability of annotations on images of the prostate gland  by two independent experts was firstly evaluated, and secondly compared with a consensus between these two experts. Using a prostate MRI database, experts drew regions of interest (ROIs) corresponding to healthy prostate  and cancer. One of the experts then drew the ROI with knowledge of the other expert’s ROI. The surface area of each ROI was used to measure the Hausdorff distance and the Dice coefficient was measured from the respective contours. They were evaluated between the different experiments, taking the annotations of the second expert as the reference. The results showed that the significant differences between the two experts disappeared with collaborative work. To conclude, this study shows that collaborative work with a dedicated tool allows consensus between expertise in the evaluation of prostate cancer from T2-weighted MRI. Prostate cancer (PCa) has shown a substantial decline in the past 5 years, between 5% and 16%, but it continues to be the most common cancer among men . CollaboThe annotation of medical images is subject to an inherent inter-variability between experts, and in some cases, there are also significant differences between the annotations of the same expert (intra-variability) . This diAmong the techniques used to detect PCa, MRI allows the non-invasive analysis of the anatomy and the metabolism in the entire prostate gland. MRI has been established as the best imaging modality for the detection, localization, and staging of PCa on account of its high resolution, excellent spontaneous contrast of soft tissues, and the possibilities of multi-planar and multi-parameter scanning . PreviouThe principal problem encountered in the diagnosis of prostate cancer is the localization of a ROI containing tumour tissue. Normally, experts use different tools to establish the diagnoses using different software and make many annotations in different files . This isIn this paper, we compare the delimitation of different ROIs on images of the prostate gland between an independent evaluation, using collaborative work of different experts. The idea behind this study is to show that collaborative work allows a real consensus between experts and potentially decreases variabilities in their evaluation. To this end, the evaluation procedure, evaluation parameters, and the data analysis discussion of the obtained results are presented in this work.A database containing MRI of both healthy and tumour-bearing prostates was used. The examinations used in our study contained three-dimensional T2-weighted fast spin-echo . We report results derived from the analysis of a small but select sample dataset, which was within reach of only a few clinical cases provided by Hospital of Dijon (France). The included cases fulfilled very specific criteria and may be considered as main impacts, in terms of incidence according to the ground truth. The multi-modal MR approach we employed ensured the precise characterization of each case. To the best of our knowledge, this work is the first to analyze such a sample in detail. For this reason, this study is the first step toward obtaining a reliable ground-truth, without expert variabilities, in which automatic algorithms could be robustly compared.The institutional committee on human research approved the study, with a waiver for the requirement for written consent, because MRI and MRSI were included in the workup procedure for all patients referred for brachytherapy or radiotherapy. As the data were retrospectively collected and untraceable, an ethical approval number, such as an IRB study number, was not needed according to French law. The annotations were performed using our own in-house developed tool .The prostate is composed of a peripheral zone (PZ), a central zone (CZ), a transitional zone (TZ), and anterior fibromuscular tissue (AFT) . Most caManual drawing of the different ROIs of the prostate according to the prostate anatomic regions and tumour lesion was performed on T2WI. Indeed, due to the high volume of information present in the anatomic images, the purpose of the present study was to evaluate the variability between experts concerning medical findings in prostate gland regions using T2WI . The T2WExperts drew ROIs on the prostate zones corresponding to PZ, CZ, and Tum. In our study, TZ was considered a part of the CZ because it was difficult to distinguish the two zones on the T2WI images. The T2WI sequence did, however, provide excellent contrast between PZ and CZ tissues .ur study .t-test . but not so for the other expert, were counted and presented as a percentage of the total number of processed slices by the second expert.The mean and the standard deviation of each parameter for the whole data set were calculated. Again, a two-sample Two examples of PCa analysis are presented in However, not all the prostate studies were evaluated with such good concordance between experiments. An example of discordance is seen in From t-test calculated for the area of the three prostate gland zones are depicted in The correlation coefficient t-test, there is no significant difference between The Bland–Altman test shows a better agreement between The Hausdorff distance and the Dice index between the different annotations are presented in Currently, the ground-truth is often obtained via evaluations from different experts performing their tasks independently, and only afterwards, their results are objectively (or subjectively) merged. Ghose et al. proposed a set of open problems mainly related to the evaluation procedure . This caAlthough interpersonal interactions are difficult to specify and quantify, they are critical to the effective flow of information, which itself is critical to the diagnostic process as it is increasingly performed within and among professional teams . It is wIn our paper, we studied the usefulness of collaborative work, where the ground-truth is obtained by two experts, but with the second expert having prior knowledge of the other expert’s work. Exhaustive evaluations of the medical findings in different regions of the prostate gland from T2WI were performed. We asked two experts to make these drawings independently on several MR examinations, and as a second step, one expert repeated the drawings with the knowledge of the evaluation of the other expert.The novelty in our study is to evaluate the variability between experts concerning medical findings in prostate gland regions. The localization of a lesion in a specific area is crucial and the segmentation process remains a challenge. The differences observed for the delimitation of the different ROIs between independent evaluation or using collaborative work by different users was studied. These segmentations remain difficult, and their delineations are fundamental for assigning a PI-RADS score. Differences in the obtained results  were compared in order to verify whether a significant improvement in consensus had been obtained through collaborative work. The idea behind this study is to show that collaborative work allows a real consensus between experts and potentially decreases variabilities in their evaluation.A main limitation of this work is the small sample size. However, it was a select sample dataset that was within reach of only a few clinical cases. The included cases fulfilled very specific criteria and may be considered as main impacts in terms of incidence according to the ground truth. For instance, this work is a proof of concept and we think that increasing the data set will not considerably alter the results, nor the conclusion, as there is already a significant difference between the approaches even with this small data set. A potential bias in our study could be the fact that the second expert participated in the consensus  shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases has not been demonstrated . Recent In this paper, the interest of collaborative work in the evaluation of cancer issues from MRI is presented. Even if improved results had been expected, this study shows that the evaluation of medical examinations with knowledge of the work of another expert, drastically reduces the differences between processing. In particular, significant differences between experts become non-significant when there is collaborative work. We cannot conclude that the diagnosis was improved, but only that there is improved consensus between the experts . Moreover, an alternative point of view is to affirm that the results from collaborative work are biased. In fact, it is out of the scope of our study because the main objective was to objectively show that collaborative work in current clinical practices can provide a consensus between experts even if there is potentially a bias in the evaluation process.In conclusion, although collaborative work requires more time, it allows the improvement of the management of patients with prostate cancer by providing consensual diagnosis, in particular in complex cases.