Gleason Alec, Richter Florian, Beller Nathalia, Arivazhagan Naveen, Feng Rui, Holmes Emma, Glicksberg Benjamin S, Morton Sarah U, La Vega-Talbott Maite, Fields Madeline, Guttmann Katherine, Nadkarni Girish N, Richter Felix
Albert Einstein College of Medicine, New York, NY.
FloDri Inc, San Francisco, CA.
medRxiv. 2024 Jun 10:2024.04.17.24305953. doi: 10.1101/2024.04.17.24305953.
Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose AI, could predict neurologic changes in the neonatal intensive care unit (NICU). We collected 4,705 hours of video linked to electroencephalograms (EEG) from 115 infants. We trained a deep learning pose algorithm that accurately predicted anatomic landmarks in three evaluation sets (ROC-AUCs 0.83-0.94), showing feasibility of applying pose AI in an ICU. We then trained classifiers on landmarks from pose AI and observed high performance for sedation (ROC-AUCs 0.87-0.91) and cerebral dysfunction (ROC-AUCs 0.76-0.91), demonstrating that an EEG diagnosis can be predicted from video data alone. Taken together, deep learning with pose AI may offer a scalable, minimally invasive method for neuro-telemetry in the NICU.
婴儿的警觉性和神经学变化可以反映危及生命的病理状况，但通过检查进行评估，而检查可能是间歇性的且主观的。因此需要可靠的连续监测方法。我们假设，我们用于跟踪运动的计算机视觉方法——姿态人工智能（pose AI），可以预测新生儿重症监护病房（NICU）中的神经学变化。我们收集了来自115名婴儿的4705小时与脑电图（EEG）相关的视频。我们训练了一种深度学习姿态算法，该算法在三个评估集中准确预测了解剖标志点（受试者工作特征曲线下面积[ROC-AUC]为0.83 - 0.94），表明在重症监护病房应用姿态人工智能的可行性。然后，我们基于姿态人工智能的标志点训练分类器，观察到其在镇静（ROC-AUC为0.87 - 0.91）和脑功能障碍（ROC-AUC为0.76 - 0.91）方面表现出色，这表明仅从视频数据就能预测脑电图诊断。综上所述，深度学习与姿态人工智能相结合，可能为新生儿重症监护病房的神经遥测提供一种可扩展的、微创的方法。
