Alattar Maha, Govind Alok, Mainali Shraddha
Division of Adult Neurology, Sleep Medicine, Vascular Neurology, Department of Neurology, Virginia Commonwealth University, Richmond, VA 23284, USA.
Department of Neurology, National Institute of Mental Health and Neurosciences, Bangalore 560029, India.
Bioengineering (Basel). 2024 Feb 22;11(3):206. doi: 10.3390/bioengineering11030206.
Sleep disorders, prevalent in the general population, present significant health challenges. The current diagnostic approach, based on a manual analysis of overnight polysomnograms (PSGs), is costly and time-consuming. Artificial intelligence has emerged as a promising tool in this context, offering a more accessible and personalized approach to diagnosis, particularly beneficial for under-served populations. This is a systematic review of AI-based models for sleep disorder diagnostics that were trained, validated, and tested on diverse clinical datasets. An extensive search of PubMed and IEEE databases yielded 2114 articles, but only 18 met our stringent selection criteria, underscoring the scarcity of thoroughly validated AI models in sleep medicine. The findings emphasize the necessity of a rigorous validation of AI models on multimodal clinical data, a step crucial for their integration into clinical practice. This would be in line with the American Academy of Sleep Medicine's support of AI research.
睡眠障碍在普通人群中普遍存在，带来了重大的健康挑战。当前基于对夜间多导睡眠图（PSG）进行人工分析的诊断方法既昂贵又耗时。在这种情况下，人工智能已成为一种很有前景的工具，它提供了一种更易获得且个性化的诊断方法，对服务不足的人群尤其有益。这是一项对基于人工智能的睡眠障碍诊断模型的系统评价，这些模型在不同的临床数据集上进行了训练、验证和测试。对PubMed和IEEE数据库进行广泛检索后得到2114篇文章，但只有18篇符合我们严格的选择标准，这凸显了睡眠医学中经过充分验证的人工智能模型的稀缺性。研究结果强调了在多模态临床数据上对人工智能模型进行严格验证的必要性，这是将其整合到临床实践中的关键一步。这将与美国睡眠医学学会对人工智能研究的支持相一致。
