{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T19:50:11Z","timestamp":1792612211848,"version":"4.5.0"},"reference-count":32,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 DK 131685"],"award-info":[{"award-number":["R01 DK 131685"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Neurourology and Urodynamics"],"published-print":{"date-parts":[[2026,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Interpretation of urodynamic studies (UDS) is central to diagnosing lower urinary tract dysfunction, but it is often complex, time\u2010consuming, and subject to interobserver variability. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have enabled automated analysis of multichannel pressure\u2013flow data and may improve objectivity and efficiency in UDS interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To critically review current applications of ML and DL for interpretation of multichannel UDS data and to evaluate their performance, clinical targets, and limitations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A literature search of PubMed, Scopus, and Web of Science was performed through September 2, 2025, without language or date restrictions. A narrative review was conducted of peer\u2010reviewed studies applying ML or DL techniques to invasive UDS signals, including vesical, abdominal, and detrusor pressures as well as urinary flow. Studies limited to uroflowmetry or non\u2010UDS inputs were excluded. Model design, input features, validation strategies, and clinical outcomes were systematically assessed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 12 studies met inclusion criteria. AI models were applied to a range of UDS tasks, including detection of detrusor overactivity, classification of bladder outlet obstruction and detrusor underactivity, multi\u2010feature UDS pattern recognition, severity grading, and real\u2010time event detection. Reported diagnostic performance was generally high, with many models achieving accuracies or AUCs between 80% and 95% for primary outcomes. However, study designs were heterogeneous, most datasets were retrospective and single\u2010center, and external validation was limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>ML and DL approaches can extract clinically meaningful information from multichannel UDS recordings and demonstrate strong technical performance across several diagnostic tasks. However, variability in methodology, limited generalizability, and lack of prospective validation currently limit clinical adoption. AI\u2010based UDS interpretation remains an emerging research area requiring standardized data, robust validation, and integration with clinical workflows.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/nau.70308","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T11:41:44Z","timestamp":1778586104000},"page":"1597-1606","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Applications of Artificial Intelligence in Urodynamic Data Interpretation: A Narrative Review"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9684-1466","authenticated-orcid":false,"given":"Farzad","family":"Pourghazi","sequence":"first","affiliation":[{"name":"Physiology and Biomedical Engineering Department, Mayo Clinic College of Medicine Mayo Clinic Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pradeep Kumar","family":"Chaudhary","sequence":"additional","affiliation":[{"name":"Physiology and Biomedical Engineering Department, Mayo Clinic College of Medicine Mayo Clinic Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4674-2598","authenticated-orcid":false,"given":"Brian J.","family":"Linder","sequence":"additional","affiliation":[{"name":"Department of Urology Mayo Clinic College of Medicine and Science Rochester Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7658-1572","authenticated-orcid":false,"given":"Azra","family":"Alizad","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic College of Medicine Mayo Clinic Rochester Minnesota 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