SADXAI: Predicting social anxiety disorder using multiple interpretable artificial intelligence techniques.

Social anxiety disorder (SAD), also known as social phobia, is a psychological condition in which a person has a persistent and overwhelming fear of being negatively judged or observed by other individuals. This fear can affect them at work, in relationships and other social activities. The intricate combination of several environmental and biological factors is the reason for the onset of this mental condition. SAD is diagnosed using a test called the "Diagnostic and Statistical Manual of Mental Health Disorders (DSM-5), which is based on several physical, emotional and demographic symptoms. Artificial Intelligence has been a boon for medicine and is regularly used to diagnose various health conditions and diseases. Hence, this study used demographic, emotional, and physical symptoms and multiple machine learning (ML) techniques to diagnose SAD. A thorough descriptive and statistical analysis has been conducted before using the classifiers. Among all the models, the AdaBoost and logistic regression obtained the highest accuracy of 88 % each. Four eXplainable artificial techniques (XAI) techniques are utilized to make the predictions interpretable, transparent and understandable. According to XAI, the "Liebowitz Social Anxiety Scale questionnaire" and "The fear of speaking in public" are the most critical attributes in the diagnosis of SAD. This clinical decision support system framework could be utilized in various suitable locations such as schools, hospitals and workplaces to identify SAD in people.

Modified DeeplabV3+ with multi-level context attention mechanism for colonoscopy polyp segmentation.

The development of automated methods for analyzing medical images of colon cancer is one of the main research fields. A colonoscopy is a medical treatment that enables a doctor to look for any abnormalities like polyps, cancer, or inflammatory tissue inside the colon and rectum. It falls under the category of gastrointestinal illnesses, and it claims the lives of almost two million people worldwide. Video endoscopy is an advanced medical imaging approach to diagnose gastrointestinal disorders such as inflammatory bowel, ulcerative colitis, esophagitis, and polyps. Medical video endoscopy generates several images, which must be reviewed by specialists. The difficulty of manual diagnosis has sparked research towards computer-aided techniques that can quickly and reliably diagnose all generated images. The proposed methodology establishes a framework for diagnosing coloscopy diseases. Endoscopists can lower the risk of polyps turning into cancer during colonoscopies by using more accurate computer-assisted polyp detection and segmentation. With the aim of creating a model that can automatically distinguish polyps from images, we presented a modified DeeplabV3+ model in this study to carry out segmentation tasks successfully and efficiently. The framework's encoder uses a pre-trained dilated convolutional residual network for optimal feature map resolution. The robustness of the modified model is tested against state-of-the-art segmentation approaches. In this work, we employed two publicly available datasets, CVC-Clinic DB and Kvasir-SEG, and obtained Dice similarity coefficients of 0.97 and 0.95, respectively. The results show that the improved DeeplabV3+ model improves segmentation efficiency and effectiveness in both software and hardware with only minor changes.

Surgical Residents' Perspective on Online Education during COVID-19 Pandemic.

<b> Introduction: </b> The COVID-19 pandemic is an exceptional situation which has rigorously affected surgical education and training worldwide. This current situation has carved innovative ways like online teaching to counter the challenges of the pandemic faced by a surgical resident. However, online teaching is not complimentary to bedside teaching which is a traditional practice. Therefore, we designed this study to assess the residents' perceptive towards online learning as a mode of education which is extensively implemented during the COVID-19 pandemic at our centre. </br></br> <b> Methods:</b> This study was a cross-sectional survey. An online Google survey was circulated among junior residents in the department of General Surgery. This survey included residents' demographic profile, effect on working hours, their perception with online teaching, and effect on their surgical training during the COVID 19 pandemic. </br></br> <b>Results:</b> A total of 95 junior residents participated in this study. Surgical training and teaching was rigorously affected according to most of them and they also believe they have lost crucial training time in their tenure as a surgical resident. A majority found the communication with the faculty during online teaching adequate; however, the main drawback of online classes was the lack of clinical exposure and practical experience. Only 4.2% preferred 100% online teaching in future. </br></br> <b> Conclusion:</b> COVID-19 pandemic has affected surgical training much more as compared to other medical fields. However, we believe online education is still a potential instrument during the COVID-19 pandemic. Online learning platforms can be used in future as a supplement to time-honoured classroom teaching and didactic lectures.