{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T17:06:45Z","timestamp":1789492005816,"version":"build-2803163510"},"reference-count":27,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,10]],"date-time":"2026-05-10T00:00:00Z","timestamp":1778371200000},"content-version":"vor","delay-in-days":9,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Aortic dissection (AD) is a life\u2010threatening cardiovascular emergency. For Type B AD (TBAD), rapid and accurate identification of the true lumen (TL), false lumen (FL), and false lumen thrombus (FLT) from CTA is critical for risk stratification and treatment planning. However, existing deep learning solutions often lack real\u2010time capability and fail to address the detection of FLT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop a real\u2010time, high\u2010precision deep learning framework for the simultaneous detection of all three key AD components to support emergency triage.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We propose AD\u2010YOLO11, an enhanced YOLOv11 model integrating three key innovations: (1) a Recursive Information Distillation Network (RIDNet) for CTA noise suppression, (2) a Triplet Attention Mechanism for spatial and channel feature enhancement, and (3) the MPDIoU loss function for optimized bounding box regression. The model was trained and internally validated on a dataset of 25\u00a0176 slices from 106 TBAD patients and externally validated on 18\u00a0238 slices from 71 independent patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>On internal validation, AD\u2010YOLO11 achieved a precision of 0.991\u00a0\u00b1\u00a00.004, recall of 0.936\u00a0\u00b1\u00a00.006, mAP@0.5 of 0.951\u00a0\u00b1\u00a00.007, and mAP@0.5:0.95 of 0.883\u00a0\u00b1\u00a00.008. It maintained high performance on the external test set, demonstrating strong generalizability. The inference speed was 3.18\u00a0\u00b1\u00a00.23\u00a0ms per slice on GPU, and it remained clinically feasible on CPU (53.15\u00a0\u00b1\u00a02.76\u00a0ms per slice).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>AD\u2010YOLO11 achieves millisecond\u2010level, high\u2010accuracy detection of all three critical Type B aortic dissection components from CTA images. Its efficient inference on both GPU and CPU makes it a promising frontline tool for rapid triage in emergency and resource\u2010limited settings, effectively complementing time\u2010consuming 3D segmentation for aortic dissection assessment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70461","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T02:32:12Z","timestamp":1778466732000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Real\u2010time, high\u2010precision detection of aortic dissection key components for emergency triage using an enhanced deep learning model"],"prefix":"10.1002","volume":"53","author":[{"given":"Houde","family":"Wu","sequence":"first","affiliation":[{"name":"School of Medical Technology Tianjin Medical University  Tianjin 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