{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,10]],"date-time":"2026-11-10T08:01:49Z","timestamp":1794297709343,"version":"4.9.0"},"reference-count":71,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T00:00:00Z","timestamp":1743552000000},"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. Mol. Biosci."],"abstract":"<jats:p>Coronary artery stenosis detection by invasive coronary angiography plays a pivotal role in computer-aided diagnosis and treatment. However, it faces the challenge of stenotic morphology confusion stemming from coronary-background similarity, varied morphology, and small-area stenoses. Furthermore, existing automated methods ignore long-temporal information mining. To address these limitations, this paper proposes a long-term temporal enhanced You Only Look Once (YOLO) method for automatic stenosis detection and assessment in invasive coronary angiography. Our approach integrates long-term temporal information and spatial information for stenosis detection with state-space models and YOLOv8. First, a spatial-aware backbone based on a dynamic Transformer and C2f Convolution of YOLOv8 combines the local and global feature extraction to distinguish the coronary regions from the background. Second, a spatial\u2013temporal multi-level fusion neck integrates the long-term temporal and spatial features to handle varied stenotic morphology. Third, a detail-aware detection head leverages low-level information for accurate identification of small stenoses. Extensive experiments on 350 invasive coronary angiography (ICA) video sequences demonstrate the model\u2019s superior performance over seven state-of-the-art methods, particularly in detecting small stenoses (&amp;lt;50%), which were previously underexplored.<\/jats:p>","DOI":"10.3389\/fmolb.2025.1558495","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T08:26:51Z","timestamp":1743582411000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["LT-YOLO: long-term temporal enhanced YOLO for stenosis detection on invasive coronary angiography"],"prefix":"10.3389","volume":"12","author":[{"given":"Jiaxin","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuesong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,4,2]]},"reference":[{"key":"B1","first-page":"118","article-title":"Abnormal object detection in thoracic X-ray using you only look once (YOLO)","author":"Adji","year":"2021"},{"key":"B2","doi-asserted-by":"publisher","first-page":"16189","DOI":"10.1109\/access.2024.3359418","article-title":"Automated brain tumor segmentation and classification in MRI using YOLO-based Deep Learning","volume":"12","author":"Almufareh","year":"2024","journal-title":"IEEE Access"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1007\/s11548-022-02812-y","article-title":"Spatiotemporal analysis of speckle dynamics to track invisible needle in ultrasound sequences using convolutional neural networks: a phantom study","volume":"18","author":"Amiri Tehrani Zade","year":"2023","journal-title":"Int. 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