Artificial Intelligence Diagnostics for Neglected Tropical Diseases

As leaders in the fight against schistosomiasis, Merck KGaA, Darmstadt, Germany, is engaged in advancing innovations to address Female Genital Schistosomiasis (FGS), which affects an estimated 56 million women and girls in Africa. In our collaboration with Oxford University, we assessed the applicability of existing Cervical Cancer Artificial Intelligence diagnostic tools for FGS.

Article Highlights

• In this review, we identified 13 published computer-aided diagnostic (CAD) algorithms for cervical cancer as potentially relevant for adaptation to automated image diagnosis for FGS.
  
• Important features for selection of CAD algorithms for FGS include high sensitivity performance, ability to identify symptomatic areas, and efficient model design that can function in low-resource settings.
  
• Improvements are needed in the following areas to accelerate the development of FGS CAD algorithms: standardization of data collection and ground truth labels, data set quality and availability, consistency in reporting of published algorithms, and publicly available codes.