ArticleBMC medicine2024
Interpretable artificial intelligence-based app assists inexperienced radiologists in diagnosing biliary atresia from sonographic gallbladder images.
Article in BMC medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Explainable artificial intelligence in medical ultrasound: a WoSCC-based bibliometric analysis, evidence map, and Scopus concordance assessment.Frontiers in medicine · 2026Pooled 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
- Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review.Cancers · 2026Review
- Applications of artificial intelligence in pediatric general surgery: a systematic review.Translational pediatrics · 2026Review
- Development of a lightweight deep learning model for accurate assessment of liver fibrosis in biliary atresia.Pediatric surgery international · 2026Article
- Application of machine learning in constructing a diagnostic model for neonatal biliary atresia.Pediatric investigation · 2025Article
- Multimodal model for the diagnosis of biliary atresia based on sonographic images and clinical parameters.NPJ digital medicine · 2025Article
- Effect of computer aided detection system on esophageal neoplasm diagnosis in varied levels of endoscopists.NPJ digital medicine · 2025Article
- Development of an artificial intelligence-based multimodal diagnostic system for early detection of biliary atresia.BMC medicine · 2025Article
- Detection of Gallbladder Disease Types Using a Feature Engineering-Based Developed CBIR System.Diagnostics (Basel, Switzerland) · 2025Article
- Strengthening collaboration within Europe: biliary atresia and the rise of the European Reference Networks.World journal of pediatric surgery · 2025Review
- Deep learning-driven ultrasound-assisted diagnosis: optimizing GallScopeNet for precise identification of biliary atresia.Frontiers in medicine · 2024Article
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Authors and funding
14 authors.
Funding
Abstract
backgroundA previously trained deep learning-based smartphone app provides an artificial intelligence solution to help diagnose biliary atresia from sonographic gallbladder images, but it might be impractical to launch it in real clinical settings. This study aimed to redevelop a new model using original sonographic images and their derived smartphone photos and then test the new model's performance in assisting radiologists with different experiences to detect biliary atresia in real-world mimic settings.
methodsA new model was first trained retrospectively using 3659 original sonographic gallbladder images and their derived 51,226 smartphone photos and tested on 11,410 external validation smartphone photos. Afterward, the new model was tested in 333 prospectively collected sonographic gallbladder videos from 207 infants by 14 inexperienced radiologists (9 juniors and 5 seniors) and 4 experienced pediatric radiologists in real-world mimic settings. Diagnostic performance was expressed as the area under the receiver operating characteristic curve (AUC).
resultsThe new model outperformed the previously published model in diagnosing BA on the external validation set (AUC 0.924 vs 0.908, P = 0.004) with higher consistency (kappa value 0.708 vs 0.609). When tested in real-world mimic settings using 333 sonographic gallbladder videos, the new model performed comparable to experienced pediatric radiologists (average AUC 0.860 vs 0.876) and outperformed junior radiologists (average AUC 0.838 vs 0.773) and senior radiologists (average AUC 0.829 vs 0.749). Furthermore, the new model could aid both junior and senior radiologists to improve their diagnostic performances, with the average AUC increasing from 0.773 to 0.835 for junior radiologists and from 0.749 to 0.805 for senior radiologists.
conclusionsThe interpretable app-based model showed robust and satisfactory performance in diagnosing biliary atresia, and it could aid radiologists with limited experiences to improve their diagnostic performances in real-world mimic settings.
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