# Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography

arXiv: https://arxiv.org/abs/1906.04419 · HTML: https://mlsanity.com/paper/1906.04419/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [Cardiac-CLIP: A Vision-Language Foundation Model for 3D Cardiac CT Images](https://mlsanity.com/paper/2507.22024/) (2025)
- [CASR-Net: An Image Processing-focused Deep Learning-based Coronary Artery Segmentation and Refinement Network for X-ray Coronary Angiogram](https://mlsanity.com/paper/2510.27315/) (2025)
- [Disarranged Zone Learning (DZL): An unsupervised and dynamic automatic stenosis recognition methodology based on coronary angiography](https://mlsanity.com/paper/2110.00896/) (2021)
- [O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis](https://mlsanity.com/paper/2303.06358/) (2023)
- [Deep Learning-Based Prediction of Fractional Flow Reserve along the Coronary Artery](https://mlsanity.com/paper/2308.04923/) (2023)
- [Peptidomic-Based Prediction Model for Coronary Heart Disease Using a Multilayer Perceptron Neural Network](https://mlsanity.com/paper/2509.03884/) (2025)

## Abstract (arXiv)

In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA). We present a method for automatic and non-invasive detection of patients requiring ICA, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187 patients, 137 of them underwent invasive FFR measurement in 192 different coronary arteries. These FFR measurements served as a reference standard for the functional significance of the coronary stenosis. The centerlines of the coronary arteries were extracted and used to reconstruct straightened multi-planar reformatted (MPR) volumes. To automatically identify arteries with functionally significant stenosis that require ICA, each MPR volume was encoded into a fixed number of encodings using two disjoint 3D and 1D convolutional autoencoders performing spatial and sequential encodings, respectively. Thereafter, these encodings were employed to classify arteries using a support vector machine classifier. The detection of coronary arteries requiring invasive evaluation, evaluated using repeated cross-validation experiments, resulted in an area under the receiver operating characteristic curve of 0.81 ± 0.02 on the artery-level, and 0.87 ± 0.02 on the patient-level. The results demonstrate the feasibility of automatic non-invasive detection of patients that require ICA and possibly subsequent coronary artery intervention. This could potentially reduce the number of patients that unnecessarily undergo ICA.