{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T22:17:19Z","timestamp":1790115439799,"version":"4.0.1"},"reference-count":18,"publisher":"Wiley","issue":"7-8","license":[{"start":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T00:00:00Z","timestamp":1711324800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["ANZ Journal of Surgery"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Prompt diagnosis of choledocholithiasis is crucial for reducing disease severity, preventing complications and minimizing length of stay. Magnetic resonance cholangiopancreatography (MRCP) is commonly used to evaluate patients with suspected choledocholithiasis but is expensive and may delay definitive intervention. To optimize patient care and resource utilization, we have developed five machine learning models that predict a patients' risk of choledocholithiasis based on clinical presentation and pre\u2010MRCP investigation results.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Inpatients admitted to the Royal Hobart Hospital from 2018 to 2023 with a suspicion of choledocholithiasis were included. Exclusion criteria included prior hepatobiliary surgery, known hepatobiliary disease, or incomplete records. Variables related to clinical presentation, laboratory testing, and sonographic or CT imaging were collected. Four machine learning techniques were employed: logistic regression, XGBoost, random forest, and K\u2010nearest neighbours. The three best performing models were combined to create an ensemble model. Model performance was compared against the American Society for Gastrointestinal Endoscopy (ASGE) choledocholithiasis risk stratification guidelines.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Of the 222 patients included, 113 (50.9%) had choledocholithiasis. The most successful models were the random forest (accuracy: 0.79, AUROC: 0.83) and ensemble (accuracy and AUROC: 0.81). Every model outperformed the ASGE guidelines. Key variables influencing the models' predictions included common bile duct diameter, lipase, imaging evidence of cholelithiasis, and liver function tests.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Machine learning models can accurately assess a patient's risk of choledocholithiasis and could assist in identifying patients who could forgo an MRCP and proceed directly to intervention. Ongoing validation on prospective data is necessary to refine their accuracy and clinical utility.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ans.18950","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T10:26:31Z","timestamp":1711362391000},"page":"1260-1265","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Using artificial intelligence to predict choledocholithiasis: can machine learning models abate the use of <scp>MRCP<\/scp> in patients with biliary dysfunction?"],"prefix":"10.1111","volume":"94","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5910-3034","authenticated-orcid":false,"given":"Joshua","family":"Blum","sequence":"first","affiliation":[{"name":"Department of General Surgery Royal Hobart Hospital  Hobart Tasmania Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sam","family":"Hunn","sequence":"additional","affiliation":[{"name":"Department of General Surgery Royal Hobart Hospital  Hobart Tasmania Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jules","family":"Smith","sequence":"additional","affiliation":[{"name":"Department of General Surgery Royal Hobart Hospital  Hobart Tasmania Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fa Yu","family":"Chan","sequence":"additional","affiliation":[{"name":"Tasmanian School of Medicine University of Tasmania  Hobart Tasmania Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Richard","family":"Turner","sequence":"additional","affiliation":[{"name":"Department of General Surgery Royal Hobart Hospital  Hobart Tasmania Australia"},{"name":"Tasmanian School of Medicine University of Tasmania  Hobart Tasmania Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,3,25]]},"reference":[{"key":"e_1_2_8_2_1","article-title":"Unnecessary MRCP prior to ERCP in patients with choledocholithiasis: the role of on\u2010site ERCP","volume":"115","author":"Sagvand BTea","year":"2020","journal-title":"Am. J. Gastroenterol."},{"key":"e_1_2_8_3_1","unstructured":"Australia Co.Availability and accessibility of diagnostic imaging equipment around Australia.2018."},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/5132052"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2018.10.001"},{"key":"e_1_2_8_6_1","first-page":"85","article-title":"ASGE guidelines result in cost\u2010saving in the management of choledocholithiasis","volume":"29","author":"Singhvi G","year":"2016","journal-title":"Ann. Gastroenterol."},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.1000368"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.hbpd.2019.06.005"},{"key":"e_1_2_8_9_1","unstructured":"HunnS BlumJ SmithJ ChanFY TurnerR 2023 Patterns of MRCP utilisation in the diagnosis and management of choledocholithiasis: a quantitative retrospective assessment at a regional centre (unpublished local audit data). Royal Hobart Hospital 2023."},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.3348\/jksr.2018.78.3.147"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ajg.2021.05.010"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2019.03.264"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gie.2021.03.972"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1097\/MPG.0000000000003219"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.livres.2021.10.001"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00464-023-10087-w"},{"key":"e_1_2_8_17_1","first-page":"448","article-title":"A machine learning pipeline for accurate COVID\u201019 health outcome prediction using longitudinal electronic health records","volume":"2021","author":"Feng A","year":"2022","journal-title":"AMIA Annu. Symp. Proc."},{"key":"e_1_2_8_18_1","first-page":"99","article-title":"Hi\u2010BEHRT: hierarchical transformer\u2010based model for accurate prediction of clinical events using multimodal longitudinal electronic health records","volume":"27","author":"Li Y","year":"2022","journal-title":"IEEE J. Biomed. 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