Advancements in Pediatric Obstructive Sleep Apnea: Cognitive Implications and the Role of AI in Precision Medicine

Bomeng Zhao¹Haixia Fan²Huiyan Niu¹Yan Li¹Shudan Deng¹Lu Zhai¹Limantian Wang¹Xiaoling Gao^3*

• ¹Second Hospital of Shanxi Medical University, Taiyuan city, China
• ²First Hospital of Shanxi Medical University, Taiyuan, China
• ³Department of Sleep Center, Second Hospital of Shanxi Medical University, Taiyuan city, China

Background: Obstructive sleep apnea (OSA) in children is linked to cognitive impairment, which is further aggravated by fragmented sleep, intermittent hypoxia, and comorbid attention deficit hyperactivity disorder (ADHD), often resulting in poor academic performance, behavioral problems, and delayed neurodevelopment. Methods: A bibliometric and scientometric analysis was conducted using Web of Science Core Collection (WoSCC) and Scopus databases (1983–2025). After exclusion of non-article records and duplicates, 1,610 studies were included. CiteSpace, VOSviewer, and the R-based Bibliometrix package were employed to analyze publication trends, authorship, institutional collaborations, co-citations, and keyword evolution. Results: Since 2010, scholarly attention to pediatric obstructive sleep apnea and its cognitive implications has grown rapidly, with research output increasing at an annual rate of 11.96%. The United States, China, and several European countries stand out as major contributors, both in terms of publications and international collaboration. Prominent academic centers—such as Harvard University, the University of Michigan, and the University of Chicago—serve as key institutional hubs. Among the most influential contributors to the field are David Gozal and Leila Kheirandish-Gozal, while leading journals include Sleep Medicine and the Journal of Clinical Sleep Medicine. Keyword mapping highlights the central focus on "obstructive sleep apnea," "children," and "sleep-disordered breathing," alongside diagnostic and treatment terms such as "polysomnography" and "adenotonsillectomy." Notably, there is growing emphasis on cognitive and behavioral aspects, including "cognition," "behavior," and "ADHD," as well as comorbidities like "obesity" and "Down syndrome." Recent clusters underscore advances in artificial intelligence (AI) and machine learning (ML) based models using oximetry, electrocardiogram (ECG), and acoustic data, enabling early detection and supporting precision medicine approaches. Conclusion: The relationship between pediatric obstructive sleep apnea and neurocognitive development has gained significant attention, with recent research focusing on cognitive outcomes and emerging technologies. While conventional treatments remain important, their limited impact on cognitive recovery underscores the need for early diagnosis and personalized therapies. Advances in artificial intelligence and machine learning are improving diagnostic accuracy, enabling earlier interventions, and supporting neurocognitive function in affected children. These developments reflect a shift toward precision-based, tailored care rather than one-size-fits-all treatments.