{"title":"Artificial intelligence in the detection of choledocholithiasis: a systematic review.","authors":"Joshua Blum, Lewis Wood, Richard Turner","doi":"10.1016/j.hpb.2024.09.009","DOIUrl":null,"url":null,"abstract":"<p><strong>Importance: </strong>Choledocholithiasis is a potentially life-threatening manifestation of acute biliary dysfunction (ABD) often requiring magnetic resonance cholangiopancreatography (MRCP) for diagnosis when standard investigation findings are inconclusive. Machine learning models (MLMs) may offer alternatives to diagnose choledocholithiasis.</p><p><strong>Objective: </strong>This systematic review seeks to evaluate the performance of MLMs in predicting choledocholithiasis and to compare this performance with the American Society of Gastrointestinal Endoscopy (ASGE) guidelines.</p><p><strong>Review: </strong>This review adhered to PRISMA guidelines. Four databases were searched for relevant records published between January 2000 and April 2024. Two researchers appraised records. MLM performance and ASGE guideline efficacy were compared, and the clinical utility of MLMs was assessed.</p><p><strong>Findings: </strong>408 records were screened; eight were eligible. Model accuracy ranged from 19 % to 97 %. Several records demonstrated a moderate-to-high risk of bias; of those featuring low risk of bias, peak accuracies ranged from 70 % to 85 %. Most MLMs outperformed ASGE guidelines. Important predictor variables included age, total bilirubin, and common bile duct diameter.</p><p><strong>Conclusions: </strong>MLMs outperform ASGE guidelines in predicting choledocholithiasis. Nonetheless, biases in study design and reporting limit their prospective applicability. Current MLMs do not yet rival MRCP in detecting choledocholithiasis. Future guideline development should consider MLM-driven insights for better risk prediction.</p>","PeriodicalId":13229,"journal":{"name":"Hpb","volume":" ","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Hpb","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.hpb.2024.09.009","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Importance: Choledocholithiasis is a potentially life-threatening manifestation of acute biliary dysfunction (ABD) often requiring magnetic resonance cholangiopancreatography (MRCP) for diagnosis when standard investigation findings are inconclusive. Machine learning models (MLMs) may offer alternatives to diagnose choledocholithiasis.
Objective: This systematic review seeks to evaluate the performance of MLMs in predicting choledocholithiasis and to compare this performance with the American Society of Gastrointestinal Endoscopy (ASGE) guidelines.
Review: This review adhered to PRISMA guidelines. Four databases were searched for relevant records published between January 2000 and April 2024. Two researchers appraised records. MLM performance and ASGE guideline efficacy were compared, and the clinical utility of MLMs was assessed.
Findings: 408 records were screened; eight were eligible. Model accuracy ranged from 19 % to 97 %. Several records demonstrated a moderate-to-high risk of bias; of those featuring low risk of bias, peak accuracies ranged from 70 % to 85 %. Most MLMs outperformed ASGE guidelines. Important predictor variables included age, total bilirubin, and common bile duct diameter.
Conclusions: MLMs outperform ASGE guidelines in predicting choledocholithiasis. Nonetheless, biases in study design and reporting limit their prospective applicability. Current MLMs do not yet rival MRCP in detecting choledocholithiasis. Future guideline development should consider MLM-driven insights for better risk prediction.
期刊介绍：
HPB is an international forum for clinical, scientific and educational communication.
Twelve issues a year bring the reader leading articles, expert reviews, original articles, images, editorials, and reader correspondence encompassing all aspects of benign and malignant hepatobiliary disease and its management. HPB features relevant aspects of clinical and translational research and practice.
Specific areas of interest include HPB diseases encountered globally by clinical practitioners in this specialist field of gastrointestinal surgery. The journal addresses the challenges faced in the management of cancer involving the liver, biliary system and pancreas. While surgical oncology represents a large part of HPB practice, submission of manuscripts relating to liver and pancreas transplantation, the treatment of benign conditions such as acute and chronic pancreatitis, and those relating to hepatobiliary infection and inflammation are also welcomed. There will be a focus on developing a multidisciplinary approach to diagnosis and treatment with endoscopic and laparoscopic approaches, radiological interventions and surgical techniques being strongly represented. HPB welcomes submission of manuscripts in all these areas and in scientific focused research that has clear clinical relevance to HPB surgical practice.
HPB aims to help its readers - surgeons, physicians, radiologists and basic scientists - to develop their knowledge and practice. HPB will be of interest to specialists involved in the management of hepatobiliary and pancreatic disease however will also inform those working in related fields.
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HPB is owned by the International Hepato-Pancreato-Biliary Association (IHPBA) and is also the official Journal of the American Hepato-Pancreato-Biliary Association (AHPBA), the Asian-Pacific Hepato Pancreatic Biliary Association (A-PHPBA) and the European-African Hepato-Pancreatic Biliary Association (E-AHPBA).
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