Deep learning for precision grading of Schistosoma japonicum-induced liver fibrosis in ultrasound images

Liver fibrosis caused by schistosomiasis is a major health problem in endemic regions. Ultrasonography is widely used for screening, but grading remains subjective and dependent on specialist expertise. Here we show that a deep learning system can provide automated, fine-grained assessment of liver fibrosis caused by Schistosoma japonicum. We developed and evaluated the system using a multicentre dataset of 167,702 ultrasound images labelled on a 36-level scale from 0.0 to 3.5, designed to capture gradual disease progression and map onto four clinical grades. Of these images, 16,811 were reserved for the independent test set. In this test set, 93.9% of predictions were within 0.5 grades of expert labels, with a mean absolute error of 0.116. We further developed a deployable version that ran on Windows and Android devices, processing each image in less than 400 milliseconds. These results support scalable and more consistent ultrasound screening and follow-up in endemic regions. Researchers developed a deep learning system that accurately grades schistosomiasis-related liver fibrosis from ultrasound, and deployed it on Windows and Android devices to support consistent screening and follow-up in endemic regions.