用自监督学习从少量心电数据中筛查阵发性房颤，效果优于传统方法。
Leveraging Self-Supervised Learning Methods for Remote Screening of Subjects with Paroxysmal Atrial Fibrillation
- 用自监督学习从正常窦性心律的单导联心电图中提取特征
- 在小样本条件下，自监督方法准确率显著高于有监督学习
- 适合数据量少、标注难的临床筛查场景，尤其适用于远程医疗
人工智能在临床研究中具有发现人类难以察觉模式的潜力，能建立输入与临床结果之间的强关联。然而，这些方法通常需要大量标注数据，而医疗数据受限于隐私法规和专家标注成本，难以获取，导致研究瓶颈。本研究探索自监督学习（SSL）在小规模队列中获取初步临床研究结果的应用。针对一个未充分研究的任务——利用远程监测的单导联心电图，在正常窦性心律期间筛查阵发性房颤（P-AF），我们评估了先进的自监督学习方法与有监督学习方法的表现。结果显示，自监督学习在此任务中表现更优，更重要的是，它避免了在小样本情况下有监督方法可能带来的错误结论。
原文摘要 · Abstract (English)
The integration of Artificial Intelligence (AI) into clinical research has great potential to reveal patterns that are difficult for humans to detect, creating impactful connections between inputs and clinical outcomes. However, these methods often require large amounts of labeled data, which can be difficult to obtain in healthcare due to strict privacy laws and the need for experts to annotate data. This requirement creates a bottleneck when investigating unexplored clinical questions. This study explores the application of Self-Supervised Learning (SSL) as a way to obtain preliminary results from clinical studies with limited sized cohorts. To assess our approach, we focus on an underexplored clinical task: screening subjects for Paroxysmal Atrial Fibrillation (P-AF) using remote monitoring, single-lead ECG signals captured during normal sinus rhythm. We evaluate state-of-the-art SSL methods alongside supervised learning approaches, where SSL outperforms supervised learning in this task of interest. More importantly, it prevents misleading conclusions that may arise from poor performance in the latter paradigm when dealing with limited cohort settings.
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