# Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniques

arXiv: https://arxiv.org/abs/1912.06010 · HTML: https://mlsanity.com/paper/1912.06010/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2019.

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

- [Application of Machine Learning in Early Recommendation of Cardiac Resynchronization Therapy](https://mlsanity.com/paper/2109.06139/) (2021)
- [Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks](https://mlsanity.com/paper/2206.07654/) (2022)
- [TransONet: Automatic Segmentation of Vasculature in Computed Tomographic Angiograms Using Deep Learning](https://mlsanity.com/paper/2311.10328/) (2023)
- [Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning](https://mlsanity.com/paper/2311.16001/) (2023)

## Abstract (arXiv)

We report an object tracking algorithm that combines geometrical constraints, thresholding, and motion detection for tracking of the descending aorta and the network of major arteries that branch from the aorta including the iliac and femoral arteries. Using our automated identification and analysis, arterial system was identified with more than 85% success when compared to human annotation. Furthermore, the reported automated system is capable of producing a stenosis profile, and a calcification score similar to the Agatston score. The use of stenosis and calcification profiles will lead to the development of better-informed diagnostic and prognostic tools.