# AID-FGS: Artificial intelligence-enabled diagnosis of female genital schistosomiasis: Preliminary findings

## TL;DR

This study explores using AI to diagnose a parasitic disease affecting women in Africa, which could improve healthcare access and reduce HIV risks.

## Contribution

The study introduces an AI model for diagnosing female genital schistosomiasis from cervical images, showing promising accuracy.

## Key findings

- The AI model achieved an AUC of 0.70 in detecting FGS from cervical images.
- Higher FGS severity scores correlated with better prediction accuracy by the model.
- Machine learning shows potential for improving FGS diagnosis in resource-limited settings.

## Abstract

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.

Female genital schistosomiasis is a disease caused by a waterborne parasite Schistosoma haematobium, affecting millions of women in sub-Saharan Africa. It can lead to serious health problems and increases the risk of contracting HIV. Diagnosing this condition currently depends on a detailed visual exam of the cervix, performed by highly trained specialists. But these experts are scarce, and even among them, there can be disagreement when identifying the disease. In our study, we explored whether a computer-based system could help solve this problem. We used photos taken during medical exams to recognize patterns linked to the disease. This type of learning based on patterns in images is similar to how humans learn to recognize faces or objects. We found that the computer model was able to identify the disease with a level of accuracy that is encouraging, especially when the disease was more severe. This shows that technology can play a meaningful role in bringing better healthcare to places where expert diagnosis isn’t always available. Our hope is that this approach will lead to quicker, more reliable detection, helping protect the health of women and reduce their risk of HIV.