ArticleNature communications2026
Deep learning for precision grading of Schistosoma japonicum-induced liver fibrosis in ultrasound images.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Deep learning for precision grading of Schistosoma japonicum-induced liver fibrosis in ultrasound images.Nature communications · 2026Article
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Authors and funding
6 authors.
Funding
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.
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