深度学习方法的导出,外部验证和临床影响,用于使用非形波形测量的内压力估计
Faris Gulamali1,2, Pushkala Jayaraman1,2, Ashwin S Sawant1,2
1The Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY.
人工智能模型使用非侵入性生理数据准确估计内压力 (ICP) 的增加. 这种方法在没有侵入性监测的情况下预测不良神经系统结果方面具有前景.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 医疗技术 医疗技术 医学技术
背景情况:
- 增加的内压力 (ICP) 是神经应急症的关键指标,通常需要侵入性监测.
- 不良的神经结果经常与高的ICP有关,强调需要可靠的检测方法.
研究的目的:
- 开发和验证一种人工智能 (AI) 方法,用于检测增加的ICP (aICP),仅使用非侵入性的外生理波形数据.
- 评估AI检测到的aICP与没有侵入性ICP监测的患者的临床结果的关联.
主要方法:
- 使用MIMIC-III波形数据库进行模型开发和西奈山医院数据进行外部验证的回顾性诊断研究.
- 在157名ICP监测患者的脑外波形 (心电图,动脉血压,呼吸阻抗,SpO2) 上训练了一种AI模型.
- 该模型的性能使用AUROC,灵敏度,特异性和准确性进行了评估,其与1694名患者的临床结果的关联得到了评估.
主要成果:
- 人工智能模型在测试期间实现了0.91的AUROC,在外部验证时达到0.80.
- 在外部验证时,aICP显示了73.8%的准确性,99.5%的灵敏度和76.9%的特异性.
- 升高的aICP显著与中风,脑癌,体内和脑内出血以及神经外科手术相关.
结论:
- 人工智能驱动的aICP估计提供了一种准确且非侵入性的方法来评估高内压.
- 这种非侵入性方法与各种神经疾病和神经外科手术有关,即使在没有直接ICP监测的患者中也是如此.
- 这些发现表明,人工智能在改善与ICP相关的神经并发症的非侵入性管理和预测方面具有显著的潜力.
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