ArticlePeerJ. Computer science2025
Quantification of left ventricular mass in multiple views of echocardiograms using model-agnostic meta learning in a few-shot setting.
Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
Background: Reliable measurement of left ventricular mass (LVM) in echocardiography is essential for early detection of left ventricular dysfunction, coronary artery disease, and arrhythmia risk, yet growing patient volumes have created critical shortage of experts in echocardiography. Recent deep learning approaches reduce inter-operator variability but require large, fully labeled datasets for each standard view-an impractical demand in many clinical settings. Methods: To overcome these limitations, we propose a heatmap-based point-estimation segmentation model trained Results: As a result, the proposed MAML methods demonstrated comparable performance using mean distance error, mean angle error, successful distance error and spatial angular similarity in a few-shot setting compared to models trained with larger labeled datasets for each view of the echocardiogram.
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