A Review of Computer-Aided Diagnostic Algorithms for Cervical Neoplasia and an Assessment of Their Applicability to Female Genital Schistosomiasis

Abstract

Female genital schistosomiasis (FGS) affects an estimated 56 million women and girls in Africa. Nevertheless, this neglected tropical disease remains largely understudied and underdiagnosed. In this literature review, we examine the effectiveness of published computer-aided diagnostic (CAD) algorithms for cervical cancer that use colposcopy images and assess their applicability to the design of an automated image diagnostic algorithm for FGS. We searched 2 databases (Embase and MEDLINE) from database inception to June 10, 2022. We identified 393 studies, of which 13 were relevant for FGS diagnosis. These 13 studies were analyzed for their key image analysis model components and compared with the features that would be beneficial in an FGS diagnostic image analysis system.