{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T23:08:18Z","timestamp":1789686498923,"version":"4.0.1"},"reference-count":40,"publisher":"Georg Thieme Verlag KG","funder":[{"name":"Soonchunhyang University Research Fund","id":[{"id":"https:\/\/ror.org\/03qjsrb10","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Semin Thromb Hemost"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Machine learning (ML) and deep learning (DL) are increasingly applied across the clinical spectrum of deep vein thrombosis (DVT), from AI-guided ultrasound diagnosis to perioperative and inpatient risk prediction, but methodological progress has outpaced clinical translation. We aimed to narratively synthesize ML and DL applications in DVT across eight clinical domains and assess their performance and translational readiness. We performed a structured literature search of PubMed, Web of Science, and Scopus (January 2019\u2013April 2026) using predefined disease and technology term blocks restricted to Title\/Abstract fields. Of 492 records identified, 250 eligible publications were retained as the synthesis pool. Representative studies were purposively selected for detailed appraisal, prioritizing those with external validation, prospective design, or clear linkage to clinical workflows. AI applications span AI-guided ultrasound diagnosis, CT\/MRI-based imaging, perioperative risk prediction, medical\/ICU risk prediction, cancer-associated DVT, post-thrombotic syndrome and pulmonary embolism outcome prediction, and natural language processing (NLP)\/large language model\u2013based surveillance or decision support. Reported AUROC values are frequently high; however, external validation is uncommon, calibration metrics and decision-curve analysis are rarely reported, and prospective implementation studies are scarce. A randomized trial in medical inpatients demonstrated reduced hospital-acquired venous thromboembolism with AI-driven decision support, and AI-guided ultrasound pathways have shown near-expert diagnostic performance when used by non-expert operators. AI shows substantial promise for improving DVT diagnosis, risk stratification, and outcome prediction, but most models remain insufficiently validated for routine use. Prospective multicenter evaluation, standardized reporting of calibration and clinical utility, and structured oversight frameworks will be essential for safe deployment.<\/jats:p>","DOI":"10.1055\/a-2954-8867","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T22:55:48Z","timestamp":1789685748000},"source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning and Deep Learning in Deep Vein Thrombosis: A Narrative Review of Clinical Applications for Diagnosis, Risk Prediction, and Outcome Management"],"prefix":"10.1055","author":[{"given":"Hyun Yong","family":"Lee","sequence":"additional","affiliation":[{"name":"Soonchunhyang University Hospital Cheonan, Chungcheongnam-do, South Korea","place":["Cheonan-si"],"department":["Department of Transplantation and Vascular Surgery"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6321-4319","authenticated-orcid":false,"given":"Sangchul","family":"Yun","sequence":"additional","affiliation":[{"name":"Soonchunhyang University Hospital, Seoul, South Korea","place":["Yongsan-Gu"],"department":["Department of Transplantation and Vascular Surgery"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eol","family":"Choi","sequence":"additional","affiliation":[{"name":"Soonchunhyang University Hospital Bucheon, Gyeonggi-do, South Korea","place":["Bucheon-si"],"department":["Department of Transplantation and Vascular Sugery"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hana","family":"Jin","sequence":"additional","affiliation":[{"name":"Soonchunhyang University Hospital Bucheon, Gyeonggi-do, South Korea","place":["Bucheon-si"],"department":["Department of Transplantation and Vascular Sugery"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heangjin","family":"Ohe","sequence":"additional","affiliation":[{"name":"The Catholic University of Korea Seoul St Mary's Hospital, South Korea","place":["Seoul"],"department":["Department of Transplantation and Vascular Surgery"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungwoo","family":"Cho","sequence":"additional","affiliation":[{"name":"CHA Gangnam Medical Center, South Korea","place":["Seoul"],"department":["Department of Surgery"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moon Gu","family":"Lee","sequence":"additional","affiliation":[{"name":"Ajou University, South Korea","place":["Suwon-si"],"department":["Department of Mechanical Engineering"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"1738139","DOI":"10.3389\/fcvm.2025.1738139","article-title":"A comprehensive review of venous thromboembolism risk assessment models for hospitalized medical patients: comparative evidence, implementation challenges, and future directions","volume":"12","author":"L Alfehaid","year":"2026","journal-title":"Front Cardiovasc Med"},{"issue":"06","key":"ref2","first-page":"475","article-title":"Artificial intelligence in the diagnosis of acute deep vein thrombosis","volume":"66","author":"A Yfantis","year":"2025","journal-title":"J Cardiovasc Surg (Torino)"},{"issue":"04","key":"ref3","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1515\/cclm-2025-1450","article-title":"Artificial intelligence and machine learning in thrombosis and hemostasis: a scoping review of clinical and laboratory applications, challenges, and future directions","volume":"64","author":"M A Altememi","year":"2025","journal-title":"Clin Chem Lab Med"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"109121","DOI":"10.1016\/j.thromres.2024.109121","article-title":"Venous thromboembolism in the era of machine learning and artificial intelligence in medicine","volume":"242","author":"M Reyes Gil","year":"2024","journal-title":"Thromb Res"},{"issue":"01","key":"ref5","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1038\/s41746-021-00503-7","article-title":"Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning","volume":"4","author":"B Kainz","year":"2021","journal-title":"NPJ Digit Med"},{"issue":"04","key":"ref6","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1016\/j.ejvs.2025.04.070","article-title":"Novel artificial intelligence guided non-expert compression ultrasound deep vein thrombosis diagnostic pathway may reduce vascular laboratory venous testing","volume":"70","author":"E Avgerinos","year":"2025","journal-title":"Eur J Vasc Endovasc Surg"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.avsg.2023.08.022","article-title":"Remote expert DVT triaging of novice-user compression sonography with AI-guidance","volume":"99","author":"J Oppenheimer","year":"2024","journal-title":"Ann Vasc Surg"},{"issue":"01","key":"ref8","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1038\/s41746-025-01518-0","article-title":"Value of clinical review for AI-guided deep vein thrombosis diagnosis with ultrasound imaging by non-expert operators","volume":"8","author":"G Speranza","year":"2025","journal-title":"NPJ Digit Med"},{"issue":"01","key":"ref9","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.ultrasmedbio.2025.09.019","article-title":"Deep learning approaches for thrombosis detection and risk assessment via ultrasound imaging: a scoping review","volume":"52","author":"M Didaskalou","year":"2026","journal-title":"Ultrasound Med Biol"},{"issue":"04","key":"ref10","doi-asserted-by":"crossref","first-page":"BJGPO.2024.0057","DOI":"10.3399\/BJGPO.2024.0057","article-title":"Evaluating the benefits of machine learning for diagnosing deep vein thrombosis compared with gold standard ultrasound: a feasibility study","volume":"8","author":"K Nothnagel","year":"2025","journal-title":"BJGP Open"},{"key":"ref11","doi-asserted-by":"crossref","first-page":"4989297","DOI":"10.1155\/2021\/4989297","article-title":"Deep learning for accurate segmentation of venous thrombus from black-blood magnetic resonance images: a multicenter study","volume":"2021","author":"C Sun","year":"2021","journal-title":"BioMed Res Int"},{"issue":"02","key":"ref12","doi-asserted-by":"crossref","first-page":"274","DOI":"10.3390\/diagnostics12020274","article-title":"Comparison between deep learning and conventional machine learning in classifying iliofemoral deep venous thrombosis upon CT venography","volume":"12","author":"J H Hwang","year":"2022","journal-title":"Diagnostics (Basel)"},{"issue":"01","key":"ref13","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1038\/s41598-022-25849-0","article-title":"Artificial intelligence-based iliofemoral deep venous thrombosis detection using a clinical approach","volume":"13","author":"J W Seo","year":"2023","journal-title":"Sci Rep"},{"issue":"01","key":"ref14","doi-asserted-by":"crossref","first-page":"2197","DOI":"10.1038\/s41598-022-26032-1","article-title":"Using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplasty","volume":"13","author":"N Shohat","year":"2023","journal-title":"Sci Rep"},{"issue":"04","key":"ref15","doi-asserted-by":"crossref","first-page":"104238","DOI":"10.1016\/j.otsr.2025.104238","article-title":"Machine learning models based on a national-scale cohort accurately identify patients at high risk of deep vein thrombosis following primary total hip arthroplasty","volume":"111","author":"Z Huang","year":"2025","journal-title":"Orthop Traumatol Surg Res"},{"issue":"04","key":"ref16","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1097\/SLA.0000000000002706","article-title":"MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery","volume":"269","author":"A Bihorac","year":"2019","journal-title":"Ann Surg"},{"issue":"02","key":"ref17","doi-asserted-by":"crossref","first-page":"214","DOI":"10.21037\/jss-24-8","article-title":"Machine learning approach to predict venous thromboembolism among patients undergoing multi-level spinal posterior instrumented fusion","volume":"10","author":"K Y Heo","year":"2024","journal-title":"J Spine Surg"},{"issue":"03","key":"ref18","doi-asserted-by":"crossref","first-page":"221","DOI":"10.4103\/jcvjs.jcvjs_69_23","article-title":"Using machine learning and big data for the 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Hemost"},{"issue":"01","key":"ref21","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1186\/s13054-023-04683-4","article-title":"Interpretable machine learning models for predicting venous thromboembolism in the intensive care unit: an analysis based on data from 207 centers","volume":"27","author":"C Guan","year":"2023","journal-title":"Crit Care"},{"issue":"04","key":"ref22","doi-asserted-by":"crossref","first-page":"e002267","DOI":"10.1136\/bmjoq-2023-002267","article-title":"Effectiveness of an artificial intelligence clinical assistant decision support system to improve the incidence of hospital-associated venous thromboembolism: a prospective, randomised controlled study","volume":"12","author":"X Huang","year":"2023","journal-title":"BMJ Open Qual"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"3279","DOI":"10.2147\/IJGM.S467374","article-title":"Development and validation of an ICU-venous thromboembolism prediction model using machine learning approaches: a 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prospective study","volume":"16","author":"Y Li","year":"2025","journal-title":"Diagnostics (Basel)"},{"issue":"13","key":"ref33","doi-asserted-by":"crossref","first-page":"7132","DOI":"10.3390\/ijms23137132","article-title":"Outcome prediction in critically-ill patients with venous thromboembolism and\/or cancer using machine learning algorithms: external validation and comparison with scoring systems","volume":"23","author":"V Danilatou","year":"2022","journal-title":"Int J Mol Sci"},{"issue":"02","key":"ref34","doi-asserted-by":"crossref","first-page":"421","DOI":"10.3390\/medicina62020421","article-title":"Predicting mortality in pulmonary embolism: a machine learning approach with external validation in COVID-19 patients","volume":"62","author":"D A M\u00ee\u00c8u","year":"2026","journal-title":"Medicina (Kaunas)"},{"issue":"12","key":"ref35","doi-asserted-by":"crossref","first-page":"2991","DOI":"10.1182\/bloodadvances.2023012200","article-title":"Machine learning natural language processing for identifying venous thromboembolism: systematic review and meta-analysis","volume":"8","author":"B D Lam","year":"2024","journal-title":"Blood Adv"},{"issue":"03","key":"ref36","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1016\/j.jtha.2025.09.004","article-title":"Large language models vs thrombosis experts: a comparative study on patient education and clinical decision-making in venous thromboembolism","volume":"24","author":"N Vladic","year":"2026","journal-title":"J Thromb Haemost"},{"issue":"01","key":"ref37","doi-asserted-by":"crossref","first-page":"e36877","DOI":"10.2196\/36877","article-title":"Exploring the applicability of using natural language processing to support nationwide venous thromboembolism surveillance: model evaluation study","volume":"3","author":"A Wendelboe","year":"2022","journal-title":"JMIR Bioinform Biotechnol"},{"issue":"01","key":"ref38","doi-asserted-by":"crossref","first-page":"11333","DOI":"10.1038\/s41598-026-36432-2","article-title":"S V. Privacy-aware deep vein thrombosis segmentation using a multi-model federated learning framework with the federated averaging algorithm","volume":"16","author":"B PL,","year":"2026","journal-title":"Sci Rep"},{"issue":"36","key":"ref39","doi-asserted-by":"crossref","first-page":"14560","DOI":"10.1021\/acs.analchem.4c02973","article-title":"Novel strategy for human deep vein thrombosis diagnosis based on metabolomics and stacking machine learning","volume":"96","author":"J Cao","year":"2024","journal-title":"Anal Chem"},{"issue":"06","key":"ref40","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1111\/ejh.14110","article-title":"Artificial intelligence in the prediction of venous thromboembolism: a systematic review and pooled analysis","volume":"111","author":"T Chiasakul","year":"2023","journal-title":"Eur J Haematol"}],"container-title":["Seminars in Thrombosis and Hemostasis"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/www.thieme-connect.de\/products\/ejournals\/pdf\/10.1055\/a-2954-8867.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T22:55:53Z","timestamp":1789685753000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.thieme-connect.de\/DOI\/DOI?10.1055\/a-2954-8867"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,17]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1055\/a-2954-8867","archive":["Portico","CLOCKSS"],"relation":{},"ISSN":["0094-6176","1098-9064"],"issn-type":[{"value":"0094-6176","type":"print"},{"value":"1098-9064","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,17]]}}}