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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The urgency to accelerate PE management and minimize patient risk has driven the development of artificial intelligence (AI) algorithms designed to provide a swift and accurate diagnosis in dedicated chest imaging (computed tomography pulmonary angiogram; CTPA) for suspected PE; however, the accuracy of AI algorithms in the detection of incidental PE in non-dedicated CT imaging studies remains unclear and untested. This study explores the potential for a commercial AI algorithm to identify incidental PE in non-dedicated contrast-enhanced CT chest imaging studies. The Viz PE algorithm was deployed to identify the presence of PE on 130 dedicated and 63 non-dedicated contrast-enhanced CT chest exams. The predictions for non-dedicated contrast-enhanced chest CT imaging studies were 90.48% accurate, with a sensitivity of 0.14 and specificity of 1.00. Our findings reflect that the Viz PE algorithm demonstrated an overall accuracy of 90.16%, with a specificity of 96% and a sensitivity of 41%. Although the high specificity is promising for ruling in PE, the low sensitivity highlights a limitation, as it indicates the algorithm may miss a substantial number of true-positive incidental PEs. This study demonstrates that commercial AI detection tools hold promise as integral support for detecting PE, particularly when there is a strong clinical indication for their use; however, current limitations in sensitivity, especially for incidental cases, underscore the need for ongoing radiologist oversight.<\/jats:p>","DOI":"10.1007\/s10278-025-01552-0","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T11:48:35Z","timestamp":1750852115000},"page":"1195-1201","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Efficacy of an Automated Pulmonary Embolism (PE) Detection Algorithm on Routine Contrast-Enhanced Chest CT Imaging for Non-PE Studies"],"prefix":"10.1007","volume":"39","author":[{"given":"Hayden R.","family":"Troutt","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenneth N.","family":"Huynh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aditya","family":"Joshi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin","family":"Ling","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9148-042X","authenticated-orcid":false,"given":"Scott","family":"Refugio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Scott","family":"Cramer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasmine","family":"Lopez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katherine","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir","family":"Imanzadeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel S.","family":"Chow","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"1552_CR1","doi-asserted-by":"publisher","unstructured":"Grenier PA, Ayobi A, Quenet S, et al. Deep Learning-Based Algorithm for Automatic Detection of Pulmonary Embolism in Chest CT Angiograms. Diagnostics. 2023;13(7):1324. https:\/\/doi.org\/10.3390\/diagnostics13071324","DOI":"10.3390\/diagnostics13071324"},{"key":"1552_CR2","unstructured":"General (US) O of the S, National Heart L. SECTION I: Deep Vein Thrombosis and Pulmonary Embolism as Major Public Health Problems. In: The Surgeon General\u2019s Call to Action to Prevent Deep Vein Thrombosis and Pulmonary Embolism. Office of the Surgeon General (US); 2008. Accessed April 12, 2024. https:\/\/www.ncbi.nlm.nih.gov\/books\/NBK44181\/"},{"key":"1552_CR3","doi-asserted-by":"publisher","unstructured":"Puchades R, Tung-Chen Y, Salgueiro G, Lorenzo A, Sancho T, Fern\u00e1ndez Capit\u00e1n C. Artificial intelligence for predicting pulmonary embolism: A review of machine learning approaches and performance evaluation. 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