ArticleJournal of thoracic disease2026
A multisource cue fusion-based method for coronary artery stenosis detection in digital subtraction angiography.
Article in Journal of thoracic disease, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Coronary artery disease (CAD) remains the leading cause of mortality worldwide. Coronary digital subtraction angiography (DSA) is the gold standard for evaluating coronary lesion location, extent, and severity, and it serves as the primary basis for informing decisions related to revascularization. However, the interpretation of DSA in complex lesions is often time-consuming and subject to interobserver variability. This study aims to develop and validate a multisource cue fusion-based method for precise localization of coronary artery stenosis in digital subtraction angiography. Methods: In this study, a detection network was developed for accurately localizing stenosis in key frames of coronary DSA by integrating multisource cues. Specifically, the vessel segmentation provides spatial contour cues, while adjacent frames offer dynamic cues. To effectively combine these, a cross-cue attention-based fusion module was designed, which enhances target frame representation by capturing nonlocal spatial dependencies. Furthermore, a distance-based penalty from the coronary ostium was incorporated into the loss function to improve the model's sensitivity to localization errors of proximal stenosis. Results: The proposed method achieved a precision of 87.90%, a recall of 65.78%, an F1-score of 74.99%, a mean average precision at 0.5 intersection of union (IoU) threshold (mAP@0.5) of 78.46%, and a mAP across multiple IoU thresholds from 0.5 to 0.95 with a step size of 0.5v (mAP@0.5-0.95) of 60.37%, outperforming YOLOv5, YOLOv8, and YOLOv12 on all metrics. Ablation studies further demonstrated that the total loss led to superior performance, with mAP@0.5-0.95 increasing to 60.37%, in contrast to 56.23% with complete IoU loss and 58.01% with distance-weighted loss. Conclusions: This study addresses the critical need for precise identification and localization of coronary artery stenosis in the assessment of CAD by proposing a coronary artery stenosis detection method for DSA that integrates multiple sources of information. This method effectively combines the structural information from the target frame with the dynamic temporal sequence data from adjacent frames, overcoming the limitations of single-frame information and enhancing the accuracy of stenosis localization.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.