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.

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.