Precision measurement of cardiac structure and function in cardiovascular magnetic resonance using machine learning.

ABSTRACT:

Background

Measurement of cardiac structure and function from images (e.g. volumes, mass and derived parameters such as left ventricular (LV) ejection fraction [LVEF]) guides care for millions. This is best assessed using cardiovascular magnetic resonance (CMR), but image analysis is currently performed by individual clinicians, which introduces error. We sought to develop a machine learning algorithm for volumetric analysis of CMR images with demonstrably better precision than human analysis.

Methods

A fully automated machine learning algorithm was trained on 1923 scans (10 scanner models, 13 institutions, 9 clinical conditions, 60,000 contours) and used to segment the LV blood volume and myocardium. Performance was quantified by measuring precision on an independent multi-

SUBMITTER: Davies RH 

PROVIDER: S-EPMC8908603 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature