{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/end-to-end-syntax-score-prediction-benchmark","title":"CardioSyntax: end-to-end SYNTAX score prediction -- dataset, benchmark and method","arxiv_id":"2407.19894","date":"2024-07-29","proceeding":null,"authors":["Alexander Ponomarchuk","Ivan Kruzhilov","Galina Zubkova","Artem Shadrin","Ruslan Utegenov","Ivan Bessonov","Pavel Blinov"],"abstract":"The SYNTAX score has become a widely used measure of coronary disease severity, crucial in selecting the optimal mode of the revascularization procedure. This paper introduces a new medical regression and classification problem - automatically estimating SYNTAX score from coronary angiography. Our study presents a comprehensive CardioSYNTAX dataset of 3,018 patients for the SYNTAX score estimation and coronary dominance classification. The dataset features a balanced distribution of individuals with zero and non-zero scores. This dataset includes a first-of-its-kind, complete coronary angiography samples captured through a multi-view X-ray video, allowing one to observe coronary arteries from multiple perspectives. Furthermore, we present a novel, fully automatic end-to-end method for estimating the SYNTAX. For such a difficult task, we have achieved a solid coefficient of determination R2 of 0.51 in score value prediction and 77.3% accuracy for zero score classification.","url_abs":"https://arxiv.org/abs/2407.19894v2","url_pdf":"https://arxiv.org/pdf/2407.19894v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"end-to-end-syntax-score-prediction-benchmark","repo_url":"https://github.com/palevas/syntax-score-prediction","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"value-prediction","task_name":"Value prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}