Workflow for AI-Supported Stenosis Prediction in X-Ray Coronary Angiography for SYNTAX Score Calculation

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dc.contributor.authorPopp, Antoniaen_US
dc.contributor.authorEl Al, Alaa Abden_US
dc.contributor.authorHoffmann, Marieen_US
dc.contributor.authorLaube, Annen_US
dc.contributor.authorKempfert, Jörgen_US
dc.contributor.authorHennemuth, Anjaen_US
dc.contributor.authorMeyer, Alexanderen_US
dc.contributor.editorGarrison, Lauraen_US
dc.contributor.editorJönsson, Danielen_US
dc.date.accessioned2024-09-17T06:07:00Z
dc.date.available2024-09-17T06:07:00Z
dc.date.issued2024
dc.description.abstractX-ray coronary angiography is the primary imaging modality for evaluating coronary artery disease. The visual assessment of angiography videos in clinical routines is time-consuming, requires expert experience and lacks standardization. This complicates the calculation of the SYNTAX score, a recommended instrument for therapy decision making. In this work we propose an end-to-end pipeline for segment-wise stenosis prediction in multi-view angiography videos to facilitate the calculation of the SYNTAX score. While recent approaches mainly focus on stenosis detection on frame- or video-level, our method is developed and evaluated for stenosis prediction on patient-level. The pipeline is composed as follows: (1) Selection of frames showing arteries filled with contrast medium using a convolutional neural network, (2) Stenosis detection and segment labelling on selected frames using a region-based convolutional neural network for object detection, (3) Linkage of detected regions showing the same stenosis by tracking the optical flow of the detections in the angiography video, (4) Segment assignment to the detected and tracked stenosis to predict stenotic segments on patient-level. The workflow is adjusted and evaluated using the image data and diagnostic annotations of 219 patients with multi-vessel coronary artery disease from the German Heart Center of the Charité University Hospital (DHZC), Berlin. To fine-tune the models, we used manually flagged frames for the frame classification model and bounding box annotations provided by a cardiac expert for the stenosis detection model. For the segment-wise prediction of all patients, we achieved a total sensitivity of 56.41, specificity of 85.88, precision of 52.81 and F1 score of 54.55 with varying results for the 25 coronary segments. The established workflow can facilitate visual assessment of CAD in angiography videos and increase accuracy and precision in clinical diagnostics.en_US
dc.description.sectionheadersImage Processing and Machine Learning
dc.description.seriesinformationEurographics Workshop on Visual Computing for Biology and Medicine
dc.identifier.doi10.2312/vcbm.20241188
dc.identifier.isbn978-3-03868-244-8
dc.identifier.issn2070-5786
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/vcbm.20241188
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/vcbm20241188
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Applied computing → Health informatics; Life and medical sciences
dc.subjectApplied computing → Health informatics
dc.subjectLife and medical sciences
dc.titleWorkflow for AI-Supported Stenosis Prediction in X-Ray Coronary Angiography for SYNTAX Score Calculationen_US
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VCBM 2024: Eurographics Workshop on Visual Computing for Biology and Medicine