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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Presence of intravenous contrast on computed tomography (CT) scans is often unreliably documented, especially in large research datasets. FALCON is an open-access fully automated deep learning model enabling large-scale intravenous contrast detection and body part classification for CT scans of the head and neck (HN), chest, abdomen, and pelvis (AP). This study used six independent datasets consisting of 3138 CT scans of the HN, chest, and AP of 3126 patients from five institutions between 1996 and 2023 to train and validate four CNN models for intravenous contrast detection and body part classification. The ground truth of intravenous contrast presence was verified by a radiologist. We used ResNet9 network architecture and integrated the four models into a graphical user interface. We assessed FALCON\u2019s performance with\n                    <jats:italic>F<\/jats:italic>\n                    1 scores and compared FALCON\u2019s annotation time to manual annotation by human experts. In the external test set containing 1348 scans, the\n                    <jats:italic>F<\/jats:italic>\n                    1 score for intravenous contrast detection was 99.4% (95%CI: 98.8, 99.9) for HN CT, 98.3% (95%CI: 96.9, 99.5) for chest CT, and 98.1% (95%CI: 96.9, 99.1) for AP CT. The\n                    <jats:italic>F<\/jats:italic>\n                    1 score for body part classification alone on unseen data was 100% for HN, chest, and AP CT. Compared to human experts, annotation of a single scan with FALCON required 1.3 s vs. 21 s for HN CT, 1.8 s vs. 33 s for chest CT, and 3.7 s vs. 1.6 s for AP CT. The open-access FALCON model (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/FintelmannLabDevelopmentTeam\/Falcon\" ext-link-type=\"uri\">https:\/\/github.com\/FintelmannLabDevelopmentTeam\/Falcon<\/jats:ext-link>\n                    ) quickly and reliably detects intravenous contrast and classifies body part on CT scans.\n                  <\/jats:p>","DOI":"10.1007\/s10278-026-01865-8","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T21:28:32Z","timestamp":1772141312000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Open-Access Fully Automated Intravenous Contrast Detection and Body Part Classification for Computed Tomography Scans: The FALCON Model"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-6603-3262","authenticated-orcid":false,"given":"Julian A.","family":"Westphal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philipp","family":"Kaess","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lea","family":"Mantz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rashid A.","family":"Barnawi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sami","family":"Saidi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matthias P.","family":"Fabritius","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander","family":"Ziller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georg","family":"Kaissis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Rueckert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian J.","family":"Fintelmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"1865_CR1","doi-asserted-by":"publisher","unstructured":"Gueld MO, Kohnen M, Keysers D, Schubert H, Wein BB, Bredno J, et al. 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The procedures used in this study adhere to the tenets of the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"This study has obtained IRB approval from the Mass General Brigham Institutional Review Board, and the need for informed consent was waived.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"For this type of study, consent for publication is not required.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"Dr. Fintelmann receives research support from the William M. Wood Foundation and serves as a paid consultant for Boston Scientific Corporation, Varian Medical Systems, Tubulis GmbH, and Becton Dickinson for unrelated work. The other authors declare no relevant conflicts of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}