{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T16:43:26Z","timestamp":1791650606562,"version":"4.3.4"},"reference-count":28,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T00:00:00Z","timestamp":1735084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CHANG GUNG MEDICAL FOUNDATION, TAIWAN","award":["CORPVVM0061"],"award-info":[{"award-number":["CORPVVM0061"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: To develop and validate a model system using deep learning algorithms for the automatic detection of type A aortic dissection (AD), and differentiate it from normal and type B AD patients. Methods: In this retrospective study, a deep learning model is developed, based on aortic computed tomography angiography (CTA) scans of 498 patients using training, validation and test sets of 398, 50 and 50 patients, respectively. An independent test set of 316 patients is used to validate and evaluate its performance. Results: Our model comprises two components. The first one is an objection detection model, which can identify the aorta from CTA. The second one is a dissection classification model, which can automatically detect the presence of aortic dissection and determine its type based on Stanford classification. Overall, the sensitivity and specificity for Type A AD were 0.969 and 0.982, for Type B AD were 0.946 and 0.996 and for normal cases were 0.988 and 1.000, respectively. The average processing time per CTA scan was 7.9 \u00b1 2.8 s. (mean \u00b1 standard deviation). Conclusions: This deep learning automatic model can accurately and quickly detect type A AD patients, and could serve as an imaging triage in an emergency setting and facilitate early intervention and surgery to decrease the mortality rates of type A AD patients.<\/jats:p>","DOI":"10.3390\/diagnostics15010012","type":"journal-article","created":{"date-parts":[[2024,12,25]],"date-time":"2024-12-25T19:19:32Z","timestamp":1735154372000},"page":"12","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Automated Detection and Differentiation of Stanford Type A and Type B Aortic Dissections in CTA Scans Using Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7507-8644","authenticated-orcid":false,"given":"Hung-Hsien","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Medical Imaging and Intervention, New Taipei City Municipal Tucheng Hospital, New Taipei City 236043, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-Bi","family":"Chang","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Sa","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging and Intervention, New Taipei City Municipal Tucheng Hospital, New Taipei City 236043, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9770-5730","authenticated-orcid":false,"given":"Chang-Fu","family":"Kuo","sequence":"additional","affiliation":[{"name":"Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-Yu","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Medicine, College of Medicine, Chang Gung University, Taoyuan City 333323, Taiwan"},{"name":"Department of Cardiothoracic and Vascular Surgery, New Taipei City Municipal Tucheng Hospital, New Taipei City 236043, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9589-6177","authenticated-orcid":false,"given":"Cheng-Yu","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Chang Gung University, Taoyuan City 333323, Taiwan"},{"name":"Artificial Intelligence Research Center, Chang Gung University, Taoyuan City 333323, Taiwan"},{"name":"Division of Rheumatology, Allergy and Immunology, Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2263-4835","authenticated-orcid":false,"given":"Li-Jen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Taoyuan City 333423, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1093\/ejcts\/ezw235","article-title":"The incidence and mortality of acute thoracic aortic dissection: Results from a whole nation study","volume":"50","author":"Melvinsdottir","year":"2016","journal-title":"Eur. 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