{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T17:46:23Z","timestamp":1791481583395,"version":"4.3.4"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"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>Objective<\/jats:title>\n                    <jats:p>The rapid identification of pulmonary thromboembolism (PTE) on computed tomography pulmonary angiography (CTPA) is vital but labor-intensive, often leading to diagnostic delays. We aimed to construct and evaluate a YOLOv11 object detection algorithm capable of automatically highlighting intraluminal filling defects to expedite emergency radiological workflows.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A retrospective analysis was conducted on CTPA scans from multiple centers. The dataset was divided into a primary internal cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u20091,368) for model derivation and testing, alongside an independent external cohort (\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200998) to assess generalizability. The diagnostic efficacy of the YOLOv11 architecture was quantified using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Additionally, gradient-weighted class activation mapping (Grad-CAM) was applied to map the spatial distribution of the model's focus, ensuring clinical transparency.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>During internal testing, the proposed framework yielded an AUC of 0.777 [95% confidence interval (CI): 0.765\u20130.788], corresponding to a sensitivity of 74.53% and a specificity of 64.26%. When applied to the external cohort, the algorithm's discriminative ability remained consistent with an AUC of 0.778 (95% CI: 0.749\u20130.806). Notably, the external sensitivity reached 86.75% (specificity: 54.46%). Visual assessments via Grad-CAM saliency maps confirmed that the model accurately localized embolic occlusions within the complex pulmonary arterial tree.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Utilizing the YOLOv11 architecture for automated CTPA analysis yields a highly sensitive and visually interpretable screening mechanism. This artificial intelligence-assisted approach holds substantial promise for reducing missed diagnoses and accelerating patient triage in acute clinical settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcvm.2026.1864916","type":"journal-article","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T04:24:43Z","timestamp":1784867083000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A deep learning model for the interpretable identification of pulmonary thromboembolism from computed tomography pulmonary angiography"],"prefix":"10.3389","volume":"13","author":[{"given":"Yemei","family":"Li","sequence":"first","affiliation":[{"name":"Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Nanjing Medical University (Changzhou Second People's Hospital)","place":["Changzhou, 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