Purpose

The purpose of AngioQuant is to assist physicians in clinical decision-making by leveraging machine learning to improve the interpretation of angiograms. Specifically, the model aims to help with cath report generation. By enhancing the accuracy and efficiency of image-based diagnosis, this tool supports more informed and personalized treatment planning for patients with complex cardiovascular conditions.

Operational Workflow

In collaboration with SoftLink, AngioQuant is software powered by image-based-AI and trained on many hundreds of meticulously annotated angiograms of the major coronary arteries. The AI outlines and segments lesions and generates automatic reports giving valuable information such as lesion length, percent stenosis, and bifurcation angle.

This tool gives clinicians and researches the power of a dedicated imaging lab equipped with state-of-the-art quantitative analysis tools. The tool is already capable of measuring lesions, and with additional effort can be expanded for treatment recommendations, clinical or educational, and potential in-lab integration.

Future Goal: Automatic Bifurcation Lesion Detection

Building on the angiographic analysis, the system will proceed to perform Quantitative Coronary Analysis (QCA), which will automatically detect any bifurcation lesions. Based on the characteristics of the bifurcation, the software will then provide a recommendation for the most appropriate bifurcation stenting technique.