Abstract:

Obstructive Sleep Apnea（OSA） is one of the most common respiratory diseases in adults. It is characterized by frequent upper airway collapse during sleep, which seriously affects people’s sleep quality and health. The diagnosis of obstructive sleep apnea syndrome mainly depends on polysomnography, but this method can not meet the current large number of diagnostic needs. With the emergence and development of artificial intelligence, it is assumed that deep learning can effectively assist doctors in the diagnosis of the syndrome. Starting from the clinical diagnosis of obstructive sleep apnea, this paper introduces the advantages of lateral radiographs of craniofacial region as a diagnostic data set, and the status quo of artificial intelligence in the diagnosis of OSA, puts forward the technical route of artificial intelligence in assisting physicians in the diagnosis of OSA, and analyzes the problems and challenges in the current diagnosis system.

Key words: Obstructive Sleep Apnea（OSA）, polysomnography, deep learning, lateral radiographs of craniofacial region

摘要：

阻塞性睡眠呼吸暂停（Obstructive Sleep Apnea，OSA）是成年人较为常见的呼吸类疾病之一，该疾病的特点是睡眠过程中频繁出现上气道完全或部分塌陷，严重影响人们的睡眠质量以及身体健康。阻塞性睡眠呼吸暂停综合征的诊断主要依靠多导睡眠监测，但这种方法无法满足目前大量的诊断需求。随着人工智能的出现及发展，假设深度学习可以有效地协助医生进行诊断该综合征。主要从阻塞性睡眠呼吸暂停的临床诊断方式出发，介绍了颅面侧位片作为诊断数据集的优势，以及人工智能诊断OSA的现状，提出了人工智能辅助医师诊断OSA的技术路线，分析了目前该诊断系统仍然存在的问题和挑战。

关键词: 阻塞性睡眠呼吸暂停, 多导睡眠监测, 深度学习, 颅面侧位片