用强化学习动态选超声视频，诊断主动脉瓣狭窄更准更快
PRECISE-AS: Personalized Reinforcement Learning for Efficient Point-of-Care Echocardiography in Aortic Stenosis Diagnosis
- 基于强化学习动态选择最有效的超声视图，减少无效拍摄
- 仅用47%视频达到80.6%准确率，相比全采集效率提升显著
- 适合资源有限地区医生使用，推动个性化超声诊断普及
主动脉瓣狭窄（AS）是一种因主动脉瓣狭窄导致血流受阻的致命疾病。尽管发病率高，但受限于资源，超声心动图（echo）这一金标准诊断工具在农村和医疗薄弱地区难以普及。床旁超声（POCUS）虽更具可及性，却受限于操作者水平及影像视图选择难题。为此，我们提出一种基于强化学习（RL）的主动视频采集框架，能动态选择每位患者最具有信息量的超声视频。与依赖固定视频集的传统方法不同，本方法持续评估是否需要新增影像，从而在保证精度的同时提升效率。在2572名患者的测试数据上，该方法实现了80.6%的分类准确率，仅需47%的视频采集量即可达成，显著优于完整采集。结果表明，主动特征采集能有效提升AS诊断效率、可扩展性和个性化水平。源代码已公开：https://github.com/Armin-Saadat/PRECISE-AS。
原文摘要 · Abstract (English)
Aortic stenosis (AS) is a life-threatening condition caused by a narrowing of the aortic valve, leading to impaired blood flow. Despite its high prevalence, access to echocardiography (echo), the gold-standard diagnostic tool, is often limited due to resource constraints, particularly in rural and underserved areas. Point-of-care ultrasound (POCUS) offers a more accessible alternative but is restricted by operator expertise and the challenge of selecting the most relevant imaging views. To address this, we propose a reinforcement learning (RL)-driven active video acquisition framework that dynamically selects each patient's most informative echo videos. Unlike traditional methods that rely on a fixed set of videos, our approach continuously evaluates whether additional imaging is needed, optimizing both accuracy and efficiency. Tested on data from 2,572 patients, our method achieves 80.6% classification accuracy while using only 47% of the echo videos compared to a full acquisition. These results demonstrate the potential of active feature acquisition to enhance AS diagnosis, making echocardiographic assessments more efficient, scalable, and personalized. Our source code is available at: https://github.com/Armin-Saadat/PRECISE-AS.
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