首个基于心尖长轴切面预测射血分数的数据集与模型，性能媲美临床标准。
Learning from Scarce Labels: Multi-View Echocardiography for Ejection Fraction Prediction
- 利用临床记录与超声视频时间关联，结合视图分类器和代理标注生成2.5万+带标签视频。
- 模型在心尖长轴切面下实现6.86%平均绝对误差，接近心尖四腔切面的临床水平。
- 多视角融合进一步提升至6.37%误差，适合无法获取标准视角的临床场景。
据我们所知，本文首次公开了从心尖长轴（PLAX）超声心动图预测左心室射血分数（EF）的数据资源。由于此前不存在PLAX-EF数据集，本研究提出创新的数据生成策略以应对标签稀缺问题。通过结合临床记录与超声视频的时间相关性，辅以视图分类器微调和代理标注，构建了超过25,000个带有标签的PLAX视频数据集。该数据集使首个可复现的PLAX-EF模型得以训练，实现6.86%的平均绝对误差（MAE）。考虑到目前临床标准的apical four-chamber（A4C）方法的MAE为6%-7%，本结果表明从PLAX视图估算EF具有可行性与临床意义。此外，通过简单的无权重晚期融合，将PLAX与A4C预测结果结合，进一步将误差降至6.37%，凸显多视角整合的价值。为促进后续研究，我们已将数据标签、训练模型及可运行演示发布于GitHub、Hugging Face与Google Colab：https://github.com/Jeffrey4899/PLAX_EF_Labels_202509。
原文摘要 · Abstract (English)
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 generation strategy to overcome this scarcity. By leveraging a time-based correlation between clinical notes and echocardiographic videos, combined with fine-tuning view classifiers and proxy labeling, we created a labeled dataset of over 25,000 PLAX videos. This enables us to train the first reproducible PLAX EF model, achieving a mean absolute error (MAE) of 6.86%. Given that apical four-chamber (A4C) methods, the clinical standard, report MAE values of 6%-7%, our results demonstrate that EF estimation from PLAX views is both feasible and clinically relevant. This surpasses the performance of existing methods and provides a clinically relevant solution for situations where apical views may not be feasible. Going further, we demonstrate that combining PLAX and A4C predictions via simple unweighted late fusion improves both single-view baselines to a 6.37% MAE, underscoring the value of multi-view integration. To promote continued research, we release the dataset labels, trained models, and runnable demos on GitHub, Hugging Face, and Google Colab: https://github.com/Jeffrey4899/PLAX_EF_Labels_202509
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