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New method predicts ejection fraction from echocardiograms using scarce data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method to predict left ventricular ejection fraction (EF) from parasternal long-axis (PLAX) echocardiography, addressing the scarcity of relevant datasets. By correlating clinical notes with echocardiographic videos and employing view classifiers and proxy labeling, they generated a dataset of over 25,000 PLAX videos. The resulting model achieved a mean absolute error (MAE) of 6.86%, demonstrating the clinical feasibility of PLAX EF estimation. Further integration of PLAX and apical four-chamber (A4C) predictions improved the MAE to 6.37%, and the dataset, models, and demos have been released on GitHub, Hugging Face, and Google Colab. AI

IMPACT This research demonstrates a novel approach to medical image analysis, potentially improving diagnostic capabilities where standard views are not feasible.

RANK_REASON The cluster describes a research paper detailing a new method and dataset for a medical prediction task. [lever_c_demoted from research: ic=1 ai=0.7]

Read on Hugging Face Daily Papers →

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New method predicts ejection fraction from echocardiograms using scarce data

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The cluster describes a research paper detailing a new method and dataset for a medical prediction task. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Learning from Scarce Labels: Multi-View Echocardiography for Ejection Fraction Prediction

    We present, to the best of our knowledge, the first publicly available resource for predicting left ventricular ejection fraction (EF) from parasternal long-axis (PLAX) echocardiography. Because no PLAX-EF datasets previously existed, our work focuses on an innovative data genera…

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