High-Throughput Deep Learning Detection of Mitral Regurgitation.
Amey Vrudhula1,2, Grant Duffy1, Milos Vukadinovic1,3
1Department of Cardiology, Smidt Heart Institute (A.V., G.D., M.V., S.C.), Cedars-Sinai Medical Center, Los Angeles, CA.
This study developed an automated deep learning pipeline to detect moderate or severe mitral regurgitation (MR) from echocardiograms. The AI demonstrated high accuracy in identifying MR, offering potential for automated screening.
Area of Science:
- Cardiology
- Artificial Intelligence
- Medical Imaging
Background:
- Diagnosis of mitral regurgitation (MR) relies on echocardiography with Doppler imaging.
- Current diagnostic methods require expert interpretation, highlighting a need for automated solutions.
Purpose of the Study:
- To develop and validate a fully automated deep learning pipeline for MR detection.
- To identify apical 4-chamber view videos with color Doppler and assess MR severity.
Main Methods:
- Utilized a large dataset of 58,614 transthoracic echocardiograms (2,587,538 videos) from Cedars-Sinai Medical Center.
- Developed an automated pipeline for view identification and MR severity assessment.
- Validated the model internally and externally on distinct patient cohorts.
Main Results:
- The view classifier achieved an AUC of 0.998 internally and 0.996 externally.
- The pipeline detected moderate or greater MR with AUCs of 0.916 (internal) and 0.951 (external).
- Severe MR detection showed AUCs of 0.934 (internal) and 0.969 (external).
Conclusions:
- A novel automated pipeline demonstrated excellent performance in identifying significant MR.
- The developed approach shows potential for automated screening and surveillance of mitral regurgitation.
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