CardioSyntax: end-to-end SYNTAX score prediction -- dataset, benchmark and method
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866912361751773184 |
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| author | Ponomarchuk, Alexander Kruzhilov, Ivan Zubkova, Galina Shadrin, Artem Utegenov, Ruslan Bessonov, Ivan Blinov, Pavel |
| author_facet | Ponomarchuk, Alexander Kruzhilov, Ivan Zubkova, Galina Shadrin, Artem Utegenov, Ruslan Bessonov, Ivan Blinov, Pavel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_19894 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CardioSyntax: end-to-end SYNTAX score prediction -- dataset, benchmark and method Ponomarchuk, Alexander Kruzhilov, Ivan Zubkova, Galina Shadrin, Artem Utegenov, Ruslan Bessonov, Ivan Blinov, Pavel Computer Vision and Pattern Recognition Image and Video Processing 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. |
| title | CardioSyntax: end-to-end SYNTAX score prediction -- dataset, benchmark and method |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2407.19894 |