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.

翻译：将人工智能（AI）融入临床研究具有巨大潜力，能够揭示人类难以发现的模式，从而在输入数据与临床结果之间建立有意义的关联。然而，这些方法通常需要大量标注数据，而在医疗领域，由于严格的隐私法规以及需要专家进行数据标注，获取此类数据十分困难。这一要求在研究尚未探索的临床问题时形成了瓶颈。本研究探索了应用自监督学习（SSL）作为从有限规模队列的临床研究中获取初步结果的途径。为评估我们的方法，我们聚焦于一个尚未充分探索的临床任务：利用远程监测、在正常窦性心律期间采集的单导联心电图信号，对受试者进行阵发性心房颤动（P-AF）筛查。我们评估了最先进的自监督学习方法与监督学习方法，结果表明自监督学习在此项关注任务中表现优于监督学习。更重要的是，在有限队列设置下，自监督学习能够避免因监督学习范式性能不佳而可能产生的误导性结论。