人工智能和机器学习的当前作用:它们在儿科上呼吸道阻塞性疾病中的应用是否可行?
Virginia Dallari1,2,3, Marella Reale4,5, Matteo Fermi2,6,7
1Department of Otolaryngology Head and Neck Surgery, Santa Maria delle Croci Hospital, AUSL della Romagna, Ravenna, Italy.
概括
人工智能和机器学习在使用生理信号诊断儿科阻塞性睡眠呼吸暂停方面表现有前途. 然而,目前的研究缺乏治疗应用,在数据多样性和验证方面面临挑战.
科学领域:
- 儿科睡眠医学 儿科睡眠医学
- 医疗人工智能 医疗人工智能
- 医疗保健中的机器学习
背景情况:
- 儿科上呼吸道阻塞 (UAO) 是一个重要的临床问题.
- 人工智能 (AI) 和机器学习 (ML) 在医学诊断和管理方面提供了潜在的进步.
研究的目的:
- 系统地审查AI和ML在儿科UAO的诊断,管理和治疗中的作用和可靠性.
- 评估当前在儿科睡眠医学中的AI/ML应用.
主要方法:
- 基于PRISMA的英语语言研究的系统审查.
- 在PubMed,Scopus和Web of Science中搜索了使用AI/ML对儿科UAO (≤18岁) 的研究.
- 不包括非原创作品,无关主题,混合年龄研究以及没有AI/ML的研究.
主要成果:
- 包括76篇已识别的文章中的27篇,主要关注儿科阻塞性睡眠呼吸暂停 (OSA) 诊断和严重程度.
- 卷积神经网络 (CNN) 是常见的 (29%),经常使用夜间SPO2信号 (44%).
- 报告的高级方法如深度学习 (88.8%) 和动图/氧度计 (96%) 的高精度;Sunrise算法对严重的OSA显示了100%的灵敏度.
结论:
- 儿科睡眠医学中的ML模型主要侧重于使用生理信号和临床数据进行诊断.
- 没有研究涉及AI/ML用于治疗或监测儿科UAO.
- 挑战包括数据多样性,验证,以及更广泛的临床实施的可行性.
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