Development and validation of AI-derived segmentation of four-chamber cine cardiac magnetic resonance.

ABSTRACT:

Background

Cardiac magnetic resonance (CMR) in the four-chamber plane offers comprehensive insight into the volumetrics of the heart. We aimed to develop an artificial intelligence (AI) model of time-resolved segmentation using the four-chamber cine.

Methods

A fully automated deep learning algorithm was trained using retrospective multicentre and multivendor data of 814 subjects. Validation, reproducibility, and mortality prediction were evaluated on an independent cohort of 101 subjects.

Results

The mean age of the validation cohort was 54 years, and 66 (65%) were males. Left and right heart parameters demonstrated strong correlations between automated and manual analysis, with a ρ of 0.91-0.98 and 0.89-0.98, respectively, with minimal bias. All AI four-chamber volum

SUBMITTER: Assadi H 

PROVIDER: S-EPMC11239622 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature