# CardioSyntax: end-to-end SYNTAX score prediction -- dataset, benchmark and method

arXiv: https://arxiv.org/abs/2407.19894 · HTML: https://mlsanity.com/paper/2407.19894/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 51 references that are in the map.
- First posted on arXiv 29 Jul 2024.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation](https://mlsanity.com/paper/2303.09975/) (2023)
- [Frame attention networks for facial expression recognition in videos](https://mlsanity.com/paper/1907.00193/) (2019)
- [Rethinking Video ViTs: Sparse Video Tubes for Joint Image and Video Learning](https://mlsanity.com/paper/2212.03229/) (2022)
- [NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification](https://mlsanity.com/paper/1811.05014/) (2018)
- [CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography](https://mlsanity.com/paper/2402.00570/) (2024)
- [Automated Detection of Coronary Artery Stenosis in X-ray Angiography using Deep Neural Networks](https://mlsanity.com/paper/2103.02969/) (2021)
- [A 3D CNN Network with BERT For Automatic COVID-19 Diagnosis From CT-Scan Images](https://mlsanity.com/paper/2106.14403/) (2021)
- [Neural network-based coronary dominance classification of RCA angiograms](https://mlsanity.com/paper/2309.06958/) (2023)

## Built on by (most cited first, 3 of 3)

- [CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography](https://mlsanity.com/paper/2607.22139/) (2026)
- [Modeling Clinical Workflow for SYNTAX Scoring from Coronary Angiography Videos](https://mlsanity.com/paper/2609.23553/) (2026)
- [SynSeq: End-to-End SYNTAX Score Prediction from Coronary Angiography Videos](https://mlsanity.com/paper/2609.27696/) (2026)

## Abstract (arXiv)

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.