This study investigates advanced deep learning methods to improve the detection of periportal fibrosis (PPF) in medical imaging. Schistosoma mansoni infection affects over 54 million individuals globally, predominantly in sub-Saharan Africa, with around 20 million experiencing chronic complications. PPF, present in up to 42% of these cases, is a leading outcome of chronic liver disease, significantly contributing to morbidity and mortality. Early and accurate detection is critical for timely intervention, yet conventional ultrasound diagnosis remains highly operator-dependent. We adapted and trained a convolutional neural network (CNN) using ultrasound images to automatically identify and classify PPF severity. The proposed approach achieved a diagnostic accuracy of 80%. Sensitivity and specificity reached 84% and 76%, respectively, demonstrating robust generalisability across varying image qualities and acquisition settings. These findings highlight the potential of deep learning to reduce diagnostic subjectivity and support scalable screening programmes. Future work will focus on validation with larger datasets and multi-class fibrosis grading to enhance clinical utility.

Background and Aim: Despite the high prevalence of mental disorders in these people and the important role of resilience in mental health, resilience in prisoners has not received much attention. The present study was conducted with the aim of evaluating the prevalence of mental disorders and its relationship with resilience among ISIS prisoners of war in Iraq between 2014- 2021. Methods: This research was conducted using the SCL questionnaire and Connor and Davidson's resilience. The statistical population of this research is based on the official statistics of the Iraqi Ministry of Migrants and Displaced Persons Affairs and there are 465 thousand people, after making the necessary coordination with the officials of the POW camps among 383 people (based on the Cochran formula) questionnaires were distributed by random sampling method. became Pearson's correlation coefficient was used to analyze the data. Results: The findings of the present study showed that the participants in the study suffered from significant mental disorders on average, and all dimensions of the morbid symptoms index were reported at the level of significant mental disorders. Also, based on the results of people's resilience, it was also reported at an unfavorable level. Resilience dimensions include perception of individual competence, trust in individual differences, tolerance of negative emotions, positive acceptance of change and secure relationships, and spiritual influences at an unfavorable level and control at a relatively favorable level. Conclusion: The results of this research indicated that resilience can predict the index variable of morbid symptoms in an inverse way. In this study, it was observed that people who had less resilience are more prone to mental disorders.

This study investigates advanced deep learning methods to improve the detection 1 of periportal fibrosis (PPF) in medical imaging. Schistosoma mansoni infection affects over 2 54 million individuals globally, predominantly in sub-Saharan Africa, with around 20 3 million experiencing chronic complications. PPF, present in up to 42% of these cases, is 4 a leading outcome of chronic liver disease, significantly contributing to morbidity and 5 mortality. Early and accurate detection is critical for timely intervention, yet conventional 6 ultrasound diagnosis remains highly operator-dependent. We developed a convolutional 7 neural network (CNN) model trained on non-invasive ultrasound images to automatically 8 identify and classify PPF severity. The proposed approach achieved a diagnostic accuracy 9 of 80%. Sensitivity and specificity reached 80% and 84%, respectively, demonstrating robust 10 generalisability across varying image qualities and acquisition settings. These findings 11 highlight the potential of deep learning to reduce diagnostic subjectivity and support 12 scalable screening programs. Future work will focus on validation with larger datasets and 13 multi-class fibrosis grading to enhance clinical utility.