# Automated Deep Learning Analysis of Angiography Video Sequences for Coronary Artery Disease

arXiv: https://arxiv.org/abs/2101.12505 · HTML: https://mlsanity.com/paper/2101.12505/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 29 Jan 2021.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Lookahead Optimizer: k steps forward, 1 step back](https://mlsanity.com/paper/1907.08610/) (2019)
- [Label Propagation for Deep Semi-supervised Learning](https://mlsanity.com/paper/1904.04717/) (2019)
- [Deep Segmentation and Registration in X-Ray Angiography Video](https://mlsanity.com/paper/1805.06406/) (2018)
- [Deep Keyframe Detection in Human Action Videos](https://mlsanity.com/paper/1804.10021/) (2018)
- [Automated Characterization of Stenosis in Invasive Coronary Angiography Images with Convolutional Neural Networks](https://mlsanity.com/paper/1807.10597/) (2018)

## Built on by (most cited first, 4 of 4)

- [Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network](https://mlsanity.com/paper/2210.00881/) (2022)
- [CADICA: a new dataset for coronary artery disease detection by using invasive coronary angiography](https://mlsanity.com/paper/2402.00570/) (2024)
- [CathAI: Fully Automated Interpretation of Coronary Angiograms Using Neural Networks](https://mlsanity.com/paper/2106.07708/) (2021)
- [Coronary Artery Disease Classification with Different Lesion Degree Ranges based on Deep Learning](https://mlsanity.com/paper/2402.00593/) (2024)

## Abstract (arXiv)

The evaluation of obstructions (stenosis) in coronary arteries is currently done by a physician's visual assessment of coronary angiography video sequences. It is laborious, and can be susceptible to interobserver variation. Prior studies have attempted to automate this process, but few have demonstrated an integrated suite of algorithms for the end-to-end analysis of angiograms. We report an automated analysis pipeline based on deep learning to rapidly and objectively assess coronary angiograms, highlight coronary vessels of interest, and quantify potential stenosis. We propose a 3-stage automated analysis method consisting of key frame extraction, vessel segmentation, and stenosis measurement. We combined powerful deep learning approaches such as ResNet and U-Net with traditional image processing and geometrical analysis. We trained and tested our algorithms on the Left Anterior Oblique (LAO) view of the right coronary artery (RCA) using anonymized angiograms obtained from a tertiary cardiac institution, then tested the generalizability of our technique to the Right Anterior Oblique (RAO) view. We demonstrated an overall improvement on previous work, with key frame extraction top-5 precision of 98.4%, vessel segmentation F1-Score of 0.891 and stenosis measurement 20.7% Type I Error rate.