血液透析期间间歇性低氧症:基于人工智能识别动脉氧和的牙模式
Hanjie Zhang1, Andrea Nandorine Ban2, Peter Kotanko2,3
1Renal Research Institute, 315 East 62nd Street, 3rd Floor, New York, NY, 10065, USA. Hanjie.Zhang@RRINY.COM.
BMC nephrology
|April 28, 2025
概括
人工智能通过检测动脉氧和 (SaO2) 牙模式,准确地识别出血液透析患者的睡眠呼吸暂停. 这种实时分析可以提醒临床医生在治疗期间呼吸系统不稳定.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學專業.
- 心脏病学 心脏病学
- 肺部病理学 肺部病理学
- 人工智能在医学中的应用
背景情况:
- 维持血液透析患者面临心血管和传染病的高风险.
- 低动脉氧和度 (SaO2) 与这些患者的炎症和不良结果有关.
- 睡眠呼吸暂停,在血液透析患者中很常见,可能会导致日内分析性低氧症和特征性的SaO2牙模式.
研究的目的:
- 开发和验证一种人工智能 (AI) 算法,用于自动检测SaO2牙模式.
- 为了识别重复的SaO2牙模式的患者,表明血液透析期间的睡眠呼吸暂停.
主要方法:
- 在血液透析过程中利用Crit-Line装置的内射分析SaO2测量.
- 采用一维卷积神经网络 (1D-CNN),一种深度学习模型,用于SaO2模式分类.
- 在89次血液透析治疗中的4075个五分钟SaO2段上训练并测试了1D-CNN模型.
主要成果:
- 1D-CNN算法在测试数据集中的SaO2牙模式的分类中实现了96%的准确性.
- 该模型显示了接收器操作曲线 (0.995) 下的高面积,表明了出色的性能.
- 超过21%的分析细分展示了典型的SaO2牙模式.
结论:
- 开发的1D-CNN算法在血液透析过程中准确地实时分类SaO2牙模式.
- 这种人工智能驱动的分类可以及时警告呼吸系统不稳定或睡眠呼吸暂停.
- 早期检测可以迅速进行诊断和治疗干预,以改善患者的护理.
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