Abstract
Female genital schistosomiasis (FGS) is a chronically disabling gynaecological condition, impacting up to 56 million women and girls, mostly in sub-Saharan Africa. In lieu of a gold standard laboratory test, it is possible to diagnose FGS visually. Visual diagnosis is performed through inspection of the cervix and surrounding tissue to identify signs of Schistosoma egg deposition, associated inflammation and granuloma formation. The change related to egg deposition can be very subtle and heterogeneous and is often seen in the context of other altered cervical morphology. Visual diagnostics for FGS are therefore currently highly subjective and lack specificity, with low consistency of grading between trained expert reviewers. Computer vision, driven by artificial intelligence, is an enticing prospect to overcome these issues due to the potential to accurately detect and classify the subtle changes and patterns that are indiscernible to human graders. Computer vision also offers the opportunity to support resource-constrained regions with few staff trained on visual diagnostics. However, several challenges stand in the way of progressing and successfully implementing computer vision tools for FGS. These challenges are particularly related to the variation in the appearance of the cervix (with or without disease) and FGS lesions, as well as the difficulty with accurately labelling cervical images. Exploring alternative annotation methods and model architectures is likely to improve the performance of FGS computer vision tools. This paper will explore the challenges of FGS computer vision and provide suggestions on how to overcome these barriers to enhance visual diagnostics for FGS.