{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T17:14:30Z","timestamp":1771607670241,"version":"3.50.1"},"reference-count":16,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education and the National Research Foundation of Korea","award":["GLOCAL-202504570001"],"award-info":[{"award-number":["GLOCAL-202504570001"]}]},{"name":"Ministry of Education and the National Research Foundation of Korea","award":["NRF-2022R1C1C1010643"],"award-info":[{"award-number":["NRF-2022R1C1C1010643"]}]},{"name":"Korean government (MSIT)","award":["GLOCAL-202504570001"],"award-info":[{"award-number":["GLOCAL-202504570001"]}]},{"name":"Korean government (MSIT)","award":["NRF-2022R1C1C1010643"],"award-info":[{"award-number":["NRF-2022R1C1C1010643"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Tomography"],"abstract":"<jats:p>Background\/Objectives: To develop and validate deep learning-based models for detecting significant steno-occlusion (SSO)\u2014defined as luminal narrowing greater than 50%\u2014of the femoropopliteal arteries using maximum intensity projection (MIP) images from lower extremity CT angiography (CTA). Methods: This retrospective study utilized MIP images of lower extremity CTA performed between January 2021 and December 2023 for internal model development. Deep learning-based models were developed sequentially to diagnose SSO: screening with single anteroposterior image, followed by four-segment rotational analysis that divided each femoropopliteal artery into four segments and incorporated multi-angle images. Given the cropped images and the shape of stenosis, models were trained to classify the presence of SSO. A temporal validation dataset comprised MIP images from lower extremity CTA performed between January and June 2024. Results: In total, 56,496 segment images from 642 patients (mean age: 68.2 \u00b1 13.5 years; 472 men) were included in the internal dataset. In the single-image analysis, RDNet achieved the highest mean AUC of 0.886 for SSO detection. In the four-segment rotational analysis, RDNet also demonstrated the highest mean AUC, reaching 0.964 in both half-set and full-set approaches. While RDNet recorded the highest mean AUC, all other models showed improved AUCs as the number of input images increased (p &lt; 0.05). In the temporal validation dataset, RDNet again achieved the highest mean AUC (0.959) in the half-set analysis. Conclusions: The deep learning-based model, particularly RDNet, demonstrated excellent performance in detecting SSO of peripheral arteries on MIP images from lower extremity CTA.<\/jats:p>","DOI":"10.3390\/tomography11090104","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T11:51:12Z","timestamp":1757332272000},"page":"104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning-Based Diagnosis of Femoropopliteal Artery Steno-Occlusion Using Maximum Intensity Projection Images of CT Angiography"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6891-5483","authenticated-orcid":false,"given":"Wonju","family":"Hong","sequence":"first","affiliation":[{"name":"Department of Radiology, Hallym University Sacred Heart Hospital, College of Medicine, Hallym University, Anyang 14068, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7194-5920","authenticated-orcid":false,"given":"Jaewoong","family":"Kang","sequence":"additional","affiliation":[{"name":"Medical Artificial Intelligence Center, Hallym University Medical Center, Anyang 14068, Republic of Korea"},{"name":"Department of Biomedical Informatics, Hallym University Chuncheon Sacred Heart Hospital, Chuncheon 24253, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"So Eui","family":"Kim","sequence":"additional","affiliation":[{"name":"Medical Artificial Intelligence Center, Hallym University Medical Center, Anyang 14068, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taikyeong","family":"Jeong","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence Convergence, Hallym University, Chuncheon 24252, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang Jin","family":"Yoon","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"In Jae","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hallym University Sacred Heart Hospital, College of Medicine, Hallym University, Anyang 14068, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5611-2480","authenticated-orcid":false,"given":"Lyo Min","family":"Kwon","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hallym University Sacred Heart Hospital, College of Medicine, Hallym University, Anyang 14068, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bum-Joo","family":"Cho","sequence":"additional","affiliation":[{"name":"Medical Artificial Intelligence Center, Hallym University Medical Center, Anyang 14068, Republic of Korea"},{"name":"Department of Ophthalmology, Hallym University Sacred Heart Hospital, College of Medicine, Hallym University, Anyang 14068, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1016\/j.jacc.2005.10.009","article-title":"ACC\/AHA 2005 guidelines for the management of patients with peripheral arterial disease (lower extremity, renal, mesenteric, and abdominal aortic): Executive summary. 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Medicina, 58.","DOI":"10.3390\/medicina58101435"}],"container-title":["Tomography"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2379-139X\/11\/9\/104\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:41:57Z","timestamp":1760035317000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2379-139X\/11\/9\/104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,8]]},"references-count":16,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["tomography11090104"],"URL":"https:\/\/doi.org\/10.3390\/tomography11090104","relation":{},"ISSN":["2379-139X"],"issn-type":[{"value":"2379-139X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,8]]}}}