# A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography

arXiv: https://arxiv.org/abs/1804.04360 · HTML: https://mlsanity.com/paper/1804.04360/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

## Built on by (most cited first, 16 of 16)

- [Transformer Network for Significant Stenosis Detection in CCTA of Coronary Arteries](https://mlsanity.com/paper/2107.03035/) (2021)
- [Recent Progress in Transformer-based Medical Image Analysis](https://mlsanity.com/paper/2208.06643/) (2022)
- [Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation](https://mlsanity.com/paper/2101.02375/) (2021)
- [CAD-RADS Scoring using Deep Learning and Task-Specific Centerline Labeling](https://mlsanity.com/paper/2202.03671/) (2022)
- [ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis](https://mlsanity.com/paper/2310.04749/) (2023)
- [CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: a clinically-inspired deep learning pipeline](https://mlsanity.com/paper/2304.07277/) (2023)
- [A 3D deep learning classifier and its explainability when assessing coronary artery disease](https://mlsanity.com/paper/2308.00009/) (2023)
- [Automatic Reverse Engineering: Creating computer-aided design (CAD) models from multi-view images](https://mlsanity.com/paper/2309.13281/) (2023)
- [An Intra- and Cross-frame Topological Consistency Scheme for Semi-supervised Atherosclerotic Coronary Plaque Segmentation](https://mlsanity.com/paper/2501.07850/) (2025)
- [Cardiac-CLIP: A Vision-Language Foundation Model for 3D Cardiac CT Images](https://mlsanity.com/paper/2507.22024/) (2025)
- [Disarranged Zone Learning (DZL): An unsupervised and dynamic automatic stenosis recognition methodology based on coronary angiography](https://mlsanity.com/paper/2110.00896/) (2021)
- [Deep Learning for Diagonal Earlobe Crease Detection](https://mlsanity.com/paper/2210.11582/) (2022)
- [O2CTA: Introducing Annotations from OCT to CCTA in Coronary Plaque Analysis](https://mlsanity.com/paper/2303.06358/) (2023)
- [3D CT-Based Coronary Calcium Assessment: A Feature-Driven Machine Learning Framework](https://mlsanity.com/paper/2510.25347/) (2025)
- [Clinical Risk-Aware Multi-Level Grading for Coronary Artery Stenosis through Curved Feature Reconstruction](https://mlsanity.com/paper/2606.30082/) (2026)
- [A multicenter benchmark and clinically structured metric for coronary CTA report generation](https://mlsanity.com/paper/2609.00909/) (2026)

## Abstract (arXiv)

Various types of atherosclerotic plaque and varying grades of stenosis could lead to different management of patients with coronary artery disease. Therefore, it is crucial to detect and classify the type of coronary artery plaque, as well as to detect and determine the degree of coronary artery stenosis. This study includes retrospectively collected clinically obtained coronary CT angiography (CCTA) scans of 163 patients. To perform automatic analysis for coronary artery plaque and stenosis classification, a multi-task recurrent convolutional neural network is applied on multi-planar reformatted (MPR) images of the coronary arteries. First, a 3D convolutional neural network is utilized to extract features along the coronary artery. Subsequently, the extracted features are aggregated by a recurrent neural network that performs two simultaneous multi-class classification tasks. In the first task, the network detects and characterizes the type of the coronary artery plaque (no plaque, non-calcified, mixed, calcified). In the second task, the network detects and determines the anatomical significance of the coronary artery stenosis (no stenosis, non-significant i.e. <50% luminal narrowing, significant i.e. >50% luminal narrowing). For detection and classification of coronary plaque, the method achieved an accuracy of 0.77. For detection and classification of stenosis, the method achieved an accuracy of 0.80. The results demonstrate that automatic detection and classification of coronary artery plaque and stenosis are feasible. This may enable automated triage of patients to those without coronary plaque and those with coronary plaque and stenosis in need for further cardiovascular workup.