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Updated: Sep 9, 2025
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Artificial Intelligence Image-Diagnosis for Female Genital Schistosomiasis
Jiayuan Zhu1, J Alison Noble1, Mireille Gomes2
1Department of Engineering Science, University of Oxford, UK.
Objective:
To introduce a novel, artificial Intelligence (AI), deep learning-based application for automated diagnosis of female genital schistosomiasis (FGS), a disease estimated to affect around 56 million women and girls in sub-Saharan Africa.
Patients And Methods:
This study focused on cervical images collected from a high endemic FGS area in Cameroon, from August 1, 2020 to August 31, 2021. We applied the You Only Look Once deep learning model and employed a 5-fold cross-validation approach, accompanied by sensitivity analysis, to optimize model performance.
Results:
The model achieved a sensitivity of 0.96 (76/78) and an accuracy of 0.78 (97/125), demonstrating improved performance over an existing, non-AI-based, computerized image diagnostic method, which has a sensitivity of 0.94 (73/78) but an accuracy of 0.58 (73/125) on the same dataset. In addition, the AI model significantly reduced processing time, decreasing from 47 minutes to under 90 seconds for testing 250 images.
Conclusion:
This study highlights the potential of deep learning-based models for automated diagnosis for FGS while reducing the reliance on specialized clinical expertise. It also underscores the need for further work to address current limitations of such AI-based methods for FGS diagnosis.
