ArticleNature communications2021
Ensembled deep learning model outperforms human experts in diagnosing biliary atresia from sonographic gallbladder images.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 3 of them syntheses that pooled it.
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Who cites it
63 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The diagnostic performance of ultrasound features for biliary atresia: a systematic review and updated meta-analysis.Pediatric surgery international · 2025Pooled it
- Comparison for the diagnostic performance of early diagnostic methods for biliary atresia: a systematic review and network meta-analysis.Pediatric surgery international · 2024Pooled it
- The diagnostic performance of ultrasound elastography for biliary atresia: A meta-analysis.Frontiers in public health · 2022Pooled it
- Diagnostic and management guidelines for biliary atresia in 2025.Hepatobiliary surgery and nutrition · 2026Review
- 3D Large-Scale Subwavelength-Resolution Sound Sheet Tomography Based on an Active and Programmable Circular Meta-Array.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Deep learning model for automated identification of ventrally positioned right hepatic artery in contrast-enhanced computed tomography of pediatric congenital biliary dilatation: development and clinical application.Pediatric radiology · 2026Article
- Applications of artificial intelligence in pediatric general surgery: a systematic review.Translational pediatrics · 2026Review
- GBCapsNet: A calibrated capsule network for automated gallbladder disease diagnosis via ultrasound imaging.PloS one · 2026Article
- Development and validation of a screening model for early diagnosis of biliary atresia in neonates with cholestasis.Pediatric surgery international · 2025Article
- Application of machine learning in constructing a diagnostic model for neonatal biliary atresia.Pediatric investigation · 2025Article
- Development and validation of a minimally invasive diagnostic model for biliary atresia using artificial intelligence.World journal of pediatrics : WJP · 2025Article
- Ensemble deep learning model for accurate assessment of renal fibrosis in chronic kidney disease using two-dimensional shear wave elastography images.BMC medical imaging · 2025Article
- A multimodal uncertainty-aware AI system optimizes ovarian cancer risk assessment workflow.NPJ digital medicine · 2025Article
- Comprehensive images and serum biomarkers for biliary atresia and other cholestasis in pediatrics.Scientific data · 2025Article
- MSFE-GallNet-X: a multi-scale feature extraction-based CNN Model for gallbladder disease analysis with enhanced explainability.BMC medical imaging · 2025Article
- Multimodal model for the diagnosis of biliary atresia based on sonographic images and clinical parameters.NPJ digital medicine · 2025Article
- Ultrasound of the Gallbladder-An Update on Measurements, Reference Values, Variants and Frequent Pathologies: A Scoping Review.Life (Basel, Switzerland) · 2025Review
- MMRNet: Ensemble deep learning models for predicting mismatch repair deficiency in endometrial cancer from histopathological images.Cell reports. Medicine · 2025Article
- Beyond human perception: challenges in AI interpretability of orangutan artwork.Primates; journal of primatology · 2025Article
- Transfer learning method for prenatal ultrasound diagnosis of biliary atresia.NPJ digital medicine · 2025Article
3 more citing papers are in PubMed but not listed here.
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Authors and funding
17 authors.
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Abstract
It is still challenging to make accurate diagnosis of biliary atresia (BA) with sonographic gallbladder images particularly in rural area without relevant expertise. To help diagnose BA based on sonographic gallbladder images, an ensembled deep learning model is developed. The model yields a patient-level sensitivity 93.1% and specificity 93.9% [with areas under the receiver operating characteristic curve of 0.956 (95% confidence interval: 0.928-0.977)] on the multi-center external validation dataset, superior to that of human experts. With the help of the model, the performances of human experts with various levels are improved. Moreover, the diagnosis based on smartphone photos of sonographic gallbladder images through a smartphone app and based on video sequences by the model still yields expert-level performances. The ensembled deep learning model in this study provides a solution to help radiologists improve the diagnosis of BA in various clinical application scenarios, particularly in rural and undeveloped regions with limited expertise.
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