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Applied Sciences
Volume 16
Issue 1
10.3390/app16010087
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Deep Learning for Automated Detection of Periportal Fibrosis in Ultrasound Imaging: Improving Diagnostic Accuracy in Schistosoma mansoni Infection

by
Alex Mutebe
1,2,*,
Bakhtiyar Ahmed
3,
Agnes Natukunda
1,
Emily Webb
1,4,
Andrew Abaasa
1,
Simon Mpooya
5,
Moses Egesa
1,6,7,
Ayoub Kakande
1,
Alison M. Elliott
1 and
Samuel O. Danso
8
1
Medical Research Council/Uganda Virus Research Institute and London School of Hygiene and Tropical Medicine, Uganda Research Unit, Entebbe P.O. Box 49, Uganda
2
Department of Computing, University of Essex, Colchester CO4 3SQ, UK
3
Department of Computer Science, Kingston University, London KT1 2EE, UK
4
Department of Infectious Disease Epidemiology and International Health, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
5
Division of Vector Borne and Neglected Tropical Diseases, Ministry of Health, Kampala P.O. Box 7272, Uganda
6
Department of Infection Biology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
7
Uganda Virus Research Institute, Entebbe P.O. Box 49, Uganda
8
School of Computer Science and Engineering, University of Sunderland, St Peters Campus, Sunderland SR6 0DD, UK
*
Author to whom correspondence should be addressed.
Appl. Sci. 2026, 16(1), 87; https://doi.org/10.3390/app16010087 (registering DOI)
Submission received: 4 November 2025 / Revised: 27 November 2025 / Accepted: 3 December 2025 / Published: 21 December 2025
Review Reports Versions Notes

Abstract

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.
Keywords: chronic liver disease; convolutional neural networks; deep learning; diagnostic accuracy; medical imaging; periportal fibrosis; Schistosoma mansoni; ultrasound chronic liver disease; convolutional neural networks; deep learning; diagnostic accuracy; medical imaging; periportal fibrosis; Schistosoma mansoni; ultrasound

Share and Cite

MDPI and ACS Style

Mutebe, A.; Ahmed, B.; Natukunda, A.; Webb, E.; Abaasa, A.; Mpooya, S.; Egesa, M.; Kakande, A.; Elliott, A.M.; Danso, S.O. Deep Learning for Automated Detection of Periportal Fibrosis in Ultrasound Imaging: Improving Diagnostic Accuracy in Schistosoma mansoni Infection. Appl. Sci. 2026, 16, 87. https://doi.org/10.3390/app16010087

AMA Style

Mutebe A, Ahmed B, Natukunda A, Webb E, Abaasa A, Mpooya S, Egesa M, Kakande A, Elliott AM, Danso SO. Deep Learning for Automated Detection of Periportal Fibrosis in Ultrasound Imaging: Improving Diagnostic Accuracy in Schistosoma mansoni Infection. Applied Sciences. 2026; 16(1):87. https://doi.org/10.3390/app16010087

Chicago/Turabian Style

Mutebe, Alex, Bakhtiyar Ahmed, Agnes Natukunda, Emily Webb, Andrew Abaasa, Simon Mpooya, Moses Egesa, Ayoub Kakande, Alison M. Elliott, and Samuel O. Danso. 2026. "Deep Learning for Automated Detection of Periportal Fibrosis in Ultrasound Imaging: Improving Diagnostic Accuracy in Schistosoma mansoni Infection" Applied Sciences 16, no. 1: 87. https://doi.org/10.3390/app16010087

APA Style

Mutebe, A., Ahmed, B., Natukunda, A., Webb, E., Abaasa, A., Mpooya, S., Egesa, M., Kakande, A., Elliott, A. M., & Danso, S. O. (2026). Deep Learning for Automated Detection of Periportal Fibrosis in Ultrasound Imaging: Improving Diagnostic Accuracy in Schistosoma mansoni Infection. Applied Sciences, 16(1), 87. https://doi.org/10.3390/app16010087

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