A Review on Machine Learning for Arterial Extraction and Quantitative Assessment on Invasive Coronary Angiograms

Recently, machine learning (ML) has developed rapidly in the field of medicine, playing an important role in disease diagnosis and treatment. Our aim of this paper is to provide an overview of the advancements in ML techniques applied to invasive coronary angiography (ICA) for segmentation of coronary arteries and quantitative evaluation, such as stenosis detection and fractional flow reserve (FFR) assessment.

Machine learning techniques are used extensively along with ICA for the segmentation of arteries and quantitative evaluation of stenosis and measurement of FFR, representing a trend towards using computational methods for enhanced diagnostic precision in cardiovascular medicine.

Various research studies with different algorithms and datasets have been conducted in this field. The performance of these studies largely depends on the algorithms employed and the datasets used for training and validation. However, despite the progress made, there remains a need for ML algorithms that can be easily integrated into clinical practice.