完善睡眠分期的准确性:转移学习与可得分模型相结合
Wolfgang Ganglberger1,2,3, Samaneh Nasiri2,3,4, Haoqi Sun1,2,3
1Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Sleep
|August 31, 2024
概括
转移学习 (TL) 显著改善了自动睡眠分期的准确性,匹配和超过了人类专家的同意. 开发了一种新的可得分模型,以评估人工智能驱动的睡眠分析的可靠性.
科学领域:
- 人工智能在医学中的应用
- 睡眠医学 睡眠医学
- 生物医学信号处理
背景情况:
- 使用多睡眠学 (PSG) 进行自动睡眠分期对于诊断睡眠障碍至关重要.
- 现有的人工智能模型经常在减少组装或有限的数据方面扎,影响准确性.
- 人类在睡眠阶段化方面的专家协议为自动化系统提供了一个基准.
研究的目的:
- 通过转移学习 (TL) 提高睡眠分期的准确性,以达到或超过人类专家间的协议.
- 开发一个可得分模型来评估自动睡眠分期结果的质量和可靠性.
- 使用韩国数据集 (KoGES) 对人类专家验证AI性能.
主要方法:
- 一个深度神经网络最初是在美国大型PSG数据集上训练的.
- 应用转移学习 (TL) 来校准模型,使用韩国基因组和流行病学研究 (KoGES) 的减少组合和有限样本.
- 模型的性能与三名人类得分者的专家间可靠性进行了比较,并开发了可得分性评估.
主要成果:
- 基本模型显示中度一致 (κ = 0.55),低于专家间一致 (κ = 0.62).
- 用有针对性的抽样进行TL校准提高了性能,模型超过了专家间的协议 (κ = 0.70).
- 可得分性评估中适度预测模型和专家的一致性 (R2 = 0.42),这表明高得分的录音的一致性更高.
结论:
- 有针对性的TL显著提高了非典型组件的自动睡眠分阶段性能,超过了人类专家的同意.
- 可得分性评估提供了一种可靠的方法,用于自动化睡眠分析的质量控制.
- 这种人工智能驱动的方法推进了自动化睡眠分析,显示了在临床环境中超越人类表现的潜力.
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