Deep learning for precision grading of Schistosoma japonicum-induced liver fibrosis in ultrasound images.
Authors
Affiliations (6)
Affiliations (6)
- Department of Mathematics, The Chinese University of Hong Kong, Hong Kong, China.
- Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Institute of Parasitic Diseases, Wuxi, China.
- School of Science, Nanjing University of Posts and Telecommunications, Nanjing, China.
- Jiangsu Provincial Key Laboratory on Parasite and Vector Control Technology, National Health Commission Key Laboratory of Parasitic Disease Control and Prevention, Jiangsu Institute of Parasitic Diseases, Wuxi, China. [email protected].
- Institute for Advanced Study, Beijing Normal-Hong Kong Baptist University, Zhuhai, China. [email protected].
- School of Mathematics and Statistics, Guangzhou Nanfang College, Guangzhou, China. [email protected].
Abstract
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.