{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T08:56:44Z","timestamp":1766653004345,"version":"3.41.2"},"reference-count":19,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T00:00:00Z","timestamp":1683676800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000133","name":"Agency for Healthcare Research and Quality","doi-asserted-by":"publisher","award":["1R01HS026486"],"award-info":[{"award-number":["1R01HS026486"]}],"id":[{"id":"10.13039\/100000133","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Automation and computer assistance can support quality assurance tasks in radiotherapy. Retrospective image review requires significant human resources, and automation of image review remains a noteworthy missing element in previous work. Here, we present initial findings from a proof\u2010of\u2010concept clinical implementation of an AI\u2010assisted review of CBCT registrations used for patient setup.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>An automated pipeline was developed and executed nightly, utilizing python scripts to interact with the clinical database through DICOM networking protocol and automate data retrieval and analysis. A previously developed artificial intelligence (AI) algorithm scored CBCT setup registrations based on misalignment likelihood, using a scale from 0 (most unlikely) through 1 (most likely). Over a 45\u2010day period, 1357 pre\u2010treatment CBCT registrations from 197 patients were retrieved and analyzed by the pipeline. Daily summary reports of the previous day's registrations were produced. Initial action levels targeted 10% of cases to highlight for in\u2010depth physics review. A validation subset of 100 cases was scored by three independent observers to characterize AI\u2010model performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Following an ROC analysis, a global threshold for model predictions of 0.87 was determined, with a sensitivity of 100% and specificity of 82%. Inspecting the observer scores for the stratified validation dataset showed a statistically significant correlation between observer scores and model predictions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>In this work, we describe the implementation of an automated AI\u2010analysis pipeline for daily quantitative analysis of CBCT\u2010guided patient setup registrations. The AI\u2010model was validated against independent expert observers, and appropriate action levels were determined to minimize false positives without sacrificing sensitivity. Case studies demonstrate the potential benefits of such a pipeline to bolster quality and safety programs in radiotherapy. To the authors\u2019 knowledge, there are no previous works performing AI\u2010assisted assessment of pre\u2010treatment CBCT\u2010based patient alignment.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.14016","type":"journal-article","created":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T06:48:04Z","timestamp":1683787684000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Proof\u2010of\u2010concept study of artificial intelligence\u2010assisted review of CBCT image guidance"],"prefix":"10.1002","volume":"24","author":[{"given":"Jack","family":"Neylon","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, David Geffen School of Medicine University of California  Los Angeles California USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dishane C.","family":"Luximon","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, David Geffen School of 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