{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T02:23:19Z","timestamp":1774664599544,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T00:00:00Z","timestamp":1757894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Ascending aortic (AA) dilation (diameter \u2265 4.0 cm) is a significant risk factor for aortic dissection, yet it often goes unnoticed in routine chest CT scans performed for other indications. This study aimed to develop and evaluate a deep learning pipeline for automated AA segmentation using non-ECG-gated chest CT scans. Methods: We designed a two-stage pipeline integrating a convolutional neural network (CNN) for focus-slice classification and a U-Net-based segmentation model to extract the aortic region. The model was trained and validated on a dataset of 500 non-ECG-gated chest CT scans, encompassing over 50,000 individual slices. Results: On the held-out test set (10%), the model achieved a Dice similarity coefficient (DSC) score of 99.21%, an Intersection over Union (IoU) of 98.45%, and a focus-slice classification accuracy of 98.18%. Compared with traditional rule-based and prior CNN-based methods, the proposed approach achieved markedly higher overlap metrics while maintaining low computational overhead. Conclusions: A lightweight CNN+U-Net deep learning model can enhance diagnostic accuracy, reduce radiologist workload, and enable opportunistic detection of AA dilation in routine chest CT imaging.<\/jats:p>","DOI":"10.3390\/diagnostics15182336","type":"journal-article","created":{"date-parts":[[2025,9,15]],"date-time":"2025-09-15T16:27:33Z","timestamp":1757953653000},"page":"2336","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automated Detection and Segmentation of Ascending Aorta Dilation on a Non-ECG-Gated Chest CT Using Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Fargana","family":"Aghayeva","sequence":"first","affiliation":[{"name":"Department of Radiology, Brigham and Women\u2019s Hospital, Mass General Brigham, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yusuf","family":"Abdi","sequence":"additional","affiliation":[{"name":"Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3703-0836","authenticated-orcid":false,"given":"Ahmad","family":"Uzair","sequence":"additional","affiliation":[{"name":"College of Computing and Information Technology, University of Doha for Science and Technology, Doha P.O. Box 24449, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayaz","family":"Aghayev","sequence":"additional","affiliation":[{"name":"Department of Radiology, Brigham and Women\u2019s Hospital, Mass General Brigham, Boston, MA 02115, USA"},{"name":"Harvard Medical School, Boston, MA 02115, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A Survey on Deep Learning in Medical Image Analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102680","DOI":"10.1016\/j.media.2022.102680","article-title":"The Liver Tumor Segmentation Benchmark (LiTS)","volume":"84","author":"Bilic","year":"2023","journal-title":"Med. 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