ArticlePediatric surgery international2026
Development of a lightweight deep learning model for accurate assessment of liver fibrosis in biliary atresia.
Article in Pediatric surgery international, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundGiven the lack of research on deep learning for evaluating liver fibrosis in biliary atresia (BA), and the challenge of balancing accuracy and efficiency in lightweight models, this study aims to develop a liver fibrosis assessment model utilizing Dynamic SpatialLite-EfficientNet (DSL-Net), based on the BA-specific staging system.
methodsA total of 530 HE whole slide images scanned from liver biopsies at Kasai portoenterostomy were used for the train (80%), validation (10%), and test (10%) of DSL-Net model. In the test cohort, the quantitative performance of DSL-Net was compared with that of existing lightweight models. Additionally, the model’s performance in liver fibrosis assessment was compared with that of six pathologists, and its prognostic predictive ability was further validated against traditional liver fibrosis scoring systems.
resultsThe quantitative performance of DSL-Net model was 6.9 M (Params), 0.5G (FLOPs), and 86.4 ± 0.7% (average Dice coefficient). It achieved an AUC of 0.906 for liver fibrosis assessment (≥ stage 3), comparable to two senior pediatric pathologists. With DSL-Net’s assistance, the AUCs for two junior pathologists and one attending pediatric pathologist improved from 0.685, 0.867, and 0.837 to 0.916 (P = 0.008), 0.907 (P = 0.005), and 0.948 (P = 0.021), respectively. Compared to the Metavir and Ishark scoring systems, DSL-Net model also demonstrated promising potential in predicting native liver survival (NLS), with 2-year and 5-year NLS AUCs of 0.8232 (P = 0.013; P = 0.046) and 0.7845 (P = 0.144; P = 0.656).
conclusionBased on the results of this study, the DSL-Net model demonstrates relatively higher accuracy in liver fibrosis assessment (≥ stage 3) and short-term prognostic prediction of BA, with both parameter count and computational cost comparable to the mainstream lightweight architecture. This positions DSL-Net as a promising, lightweight, and efficient tool for liver fibrosis assessment in BA, thereby better guiding clinical decision-making for BA patients.
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