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Background <p>Gallbladder and bile duct stones (cholelithiasis and choledocholithiasis) represent a major global health burden. Conventional imaging modalities have well-recognised limitations in sensitivity, operator dependence, and accessibility. Artificial intelligence (AI) has emerged as a potential adjunct to improve diagnostic accuracy, yet the evidence base remains unsynthesised.</p> Methods <p>A systematic review was conducted in accordance with PRISMA 2020 guidelines. Seven major databases were searched without language or date restrictions. Studies evaluating any AI algorithm for the detection, segmentation, or classification of gallbladder or bile duct stones on medical images were included. Risk of bias was assessed with QUADAS-2 and evidence certainty with GRADE and a Qualitative synthesis was performed.</p> Results <p>Thirteen studies (<i>n</i> &gt; 7,700 patients, &gt; 130,000 images) met inclusion criteria. Owing to substantial clinical and methodological heterogeneity, meta-analysis was not performed and findings were synthesized qualitatively. AI models, predominantly convolutional neural networks applied to ultrasound reported accuracies of 71.5–99.63% and AUC values of 0.79–0.99. Performance was highest for multi-class gallbladder disease classification and choledocholithiasis detection on MRCP. Twelve of 13 studies (92%) carried high risk of bias; overall certainty of evidence was rated very low.</p> Conclusion <p>AI demonstrates promising diagnostic performance for biliary stones; however, methodological limitations and lack of robust external validation preclude routine clinical adoption. Prospective, multicenter studies with real-world validation are urgently required.</p>

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Artificial intelligence in the imaging diagnosis of gallbladder and bile duct stones: a systematic review

  • 期刊论文

  • 发表日期：

    2026年6月29日

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  • Ali Hajihashemi,
  • Mohammadsadegh Kalantary,
  • Mohammadreza Elhaie,
  • Abolfazl Koozari

摘要

Background

Gallbladder and bile duct stones (cholelithiasis and choledocholithiasis) represent a major global health burden. Conventional imaging modalities have well-recognised limitations in sensitivity, operator dependence, and accessibility. Artificial intelligence (AI) has emerged as a potential adjunct to improve diagnostic accuracy, yet the evidence base remains unsynthesised.

Methods

A systematic review was conducted in accordance with PRISMA 2020 guidelines. Seven major databases were searched without language or date restrictions. Studies evaluating any AI algorithm for the detection, segmentation, or classification of gallbladder or bile duct stones on medical images were included. Risk of bias was assessed with QUADAS-2 and evidence certainty with GRADE and a Qualitative synthesis was performed.

Results

Thirteen studies (n > 7,700 patients, > 130,000 images) met inclusion criteria. Owing to substantial clinical and methodological heterogeneity, meta-analysis was not performed and findings were synthesized qualitatively. AI models, predominantly convolutional neural networks applied to ultrasound reported accuracies of 71.5–99.63% and AUC values of 0.79–0.99. Performance was highest for multi-class gallbladder disease classification and choledocholithiasis detection on MRCP. Twelve of 13 studies (92%) carried high risk of bias; overall certainty of evidence was rated very low.

Conclusion

AI demonstrates promising diagnostic performance for biliary stones; however, methodological limitations and lack of robust external validation preclude routine clinical adoption. Prospective, multicenter studies with real-world validation are urgently required.