{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T11:34:46Z","timestamp":1786534486678,"version":"build-2736575974"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Lower limb computed tomography venography (CTV) has low success rates for deep vein thrombosis (DVT) diagnosis. This study applied deep learning to improve DVT identification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Our study enrolled 119 positive DVT and 40 negative DVT, 111 positive pulmonary embolism (PE) patients and 20 negative PE. Two algorithms were evaluated: Faster R-CNN trained directly on CTV images, and YOLO11-nano pre-trained on computed tomographic pulmonary angiography (CTPA) then optimized on CTV via transfer learning. Three radiologists (3-5 years' experience) independently interpreted CTV images. Diagnostic performance was compared.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The YOLO11-nano model and Faster R-CNN achieved over 97% accuracy and sensitivity in diagnosing PE. Compared to the three radiologists in diagnosing DVT, YOLO also had an excellent accuracy (92.1%) and sensitivity (93.9%), superior to Faster R-CNN (66% accuracy, 68% sensitivity). The YOLO model exceeded 77% accuracy in pelvic and femoral-popliteal segments and 66.7% in detecting calf segment thrombi.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The CTPA-based transfer learning model significantly improved the feasibility of this method in routine CTV diagnostic performance, offering a promising approach for simultaneous PE and DVT detection.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1890645","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T10:56:50Z","timestamp":1786532210000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning algorithm enables lower limb venous thrombosis detection with CT venography"],"prefix":"10.3389","volume":"13","author":[{"given":"Shanshan","family":"Shen","sequence":"first","affiliation":[{"name":"Department of Radiology, Hebei General Hospital","place":["Shijiazhuang, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haixiao","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The First Hospital of Hebei Medical University","place":["Shijiazhuang, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengchao","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hebei General Hospital","place":["Shijiazhuang, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hebei General Hospital","place":["Shijiazhuang, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiahao","family":"Zhen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Hebei General Hospital","place":["Shijiazhuang, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Economics and Management , Hebei University of Science and Technology","place":["Shijiazhuang, Hebei, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.jdmv.2022.04.003","article-title":"European society for vascular surgery (ESVS) 2022 clinical practice guidelines on the management of chronic venous disease of the lower 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