Deep learning grading of Schistosoma japonicum-associated liver fibrosis from ultrasound images: a weighted soft-voting ensemble of convolutional and transformer networks

Background: Schistosomiasis morbidity still persists in China despite extensive progress towards elimination of new and active infections. Diagnosis of liver fibrosis related to Schistosoma japonicum remains challenging due to the subjective nature of ultrasound grading and the shortage of experienced sonographers in endemic counties. Methods: We developed a deep learning model that grades schistosomal liver fibrosis from a single B-mode ultrasound image without manual delineation of a region of interest. We used 2,921 images from 2,921 adults (aged 18-88 years, median age 54 years; 46.4% fema

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