<p>Performance of different deep learning models.</p>

Female genital schistosomiasis (FGS) is a sequela of infection with a waterborne parasite prevalent in sub-Saharan Africa and is associated with increased HIV risk. Diagnosis of FGS involves visual colposcopic identification of lesions on the cervix or vaginal walls. Previous studies have utilized digital image processing methods with statistical validation, and more recently, an artificial intelligence (AI)-based approach has also been explored. In this work, we sought to evaluate the performance of an AI model for identifying the presence of FGS from cervical photographs. Colposcopy images were obtained from 340 subjects in Zambia. Ground truth for presence or absence of FGS was determined by trained expert human examiners using visual assessment of images. Examiners also provided a FGS severity score between 0–8 for each image based on the number of lesions and the cervical quadrants affected, where 8 denotes highest severity and 0 denotes no FGS. The images were pre-processed with specular reflection artifact removal and image cropping to focus on the regions corresponding to the cervix and the transformation zone. The preprocessed dataset was randomly divided into training (FGS = 71, no FGS = 71) and testing (FGS = 21, no FGS = 177) cohorts. Image representations in the latent space were obtained using an ensemble of pre-trained machine learning models to further classify the image into FGS and no FGS. The best performance in the testing dataset was obtained at subject-level with area under the curve (AUC) =0.70 (95% Confidence interval: 0.58 - 0.82), Specificity = 0.68, and Sensitivity = 0.71, against the ground truth. Subjects with higher FGS severity scores (between 5–8) had high prediction rate by the machine classifier compared to those with lower severity scores (between 1–4). Machine learning shows promise in detecting FGS from limited colposcopy images. Early, accurate diagnosis may enhance reproductive health, and reduce HIV transmission risks, safeguarding maternal and child health.