An Automated Machine Learning–Based Quantitative Multiparametric Approach for Mitral Regurgitation Severity Grading

Anita Sadeghpour, Zhubo Jiang, Yoran M. Hummel, Matthew Frost, Carolyn S.P. Lam, Sanjiv J. Shah, Lars H. Lund, Gregg W. Stone, Madhav Swaminathan, Neil J. Weissman, Federico M. Asch*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: Considering the high prevalence of mitral regurgitation (MR) and the highly subjective, variable MR severity reporting, an automated tool that could screen patients for clinically significant MR (≥ moderate) would streamline the diagnostic/therapeutic pathways and ultimately improve patient outcomes. Objectives: The authors aimed to develop and validate a fully automated machine learning (ML)–based echocardiography workflow for grading MR severity. Methods: ML algorithms were trained on echocardiograms from 2 observational cohorts and validated in patients from 2 additional independent studies. Multiparametric echocardiography core laboratory MR assessment served as ground truth. The machine was trained to measure 16 MR-related parameters. Multiple ML models were developed to find the optimal parameters and preferred ML model for MR severity grading. Results: The preferred ML model used 9 parameters. Image analysis was feasible in 99.3% of cases and took 80 ± 5 seconds per case. The accuracy for grading MR severity (none to severe) was 0.80, and for significant (moderate or severe) vs nonsignificant MR was 0.97 with a sensitivity of 0.96 and specificity of 0.98. The model performed similarly in cases of eccentric and central MR. Patients graded as having severe MR had higher 1-year mortality (adjusted HR: 5.20 [95% CI: 1.24-21.9]; P = 0.025 compared with mild). Conclusions: An automated multiparametric ML model for grading MR severity is feasible, fast, highly accurate, and predicts 1-year mortality. Its implementation in clinical practice could improve patient care by facilitating referral to specialized clinics and access to evidence-based therapies while improving quality and efficiency in the echocardiography laboratory.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalJACC: Cardiovascular Imaging
Volume18
Issue number1
DOIs
StatePublished - Jan 2025

Funding

The authors thank Biniyam G. Demissei and Stephen Fernandez for statistical support and Camilla Hage, Abbott (Saurabh Datta and Barathi Sethuraman), and AstraZeneca for sharing data from the COAPT and PROMIS-HFpEF studies.

Keywords

  • artificial intelligence
  • continuous wave Doppler density
  • echocardiography
  • machine learning
  • mitral regurgitation

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Cardiology and Cardiovascular Medicine

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