Automated Analysis of Femoral Artery Calcification Using Machine Learning Techniques
This work addresses the need for better-informed diagnostic and prognostic tools in medical imaging for patients with arterial calcification, though it appears incremental as it builds on existing techniques like thresholding and motion detection.
The authors tackled the problem of automated analysis of femoral artery calcification by developing an object tracking algorithm that combines geometrical constraints, thresholding, and motion detection, achieving over 85% success in identifying the arterial system compared to human annotation and producing stenosis profiles and calcification scores similar to the Agatston score.
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.