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, USA.
FloDri Inc, San Francisco, CA, USA.
EClinicalMedicine. 2024 Nov 11;78:102919. doi: 10.1016/j.eclinm.2024.102919. eCollection 2024 Dec.
Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).
We collected video data linked to electroencephalograms (video-EEG) from infants with corrected age less than 1 year at Mount Sinai Hospital in New York City, a level four urban NICU between February 1, 2021 and December 31, 2022. We trained a deep learning pose recognition algorithm on video feeds, labeling 14 anatomic landmarks in 25 frames/infant. We then trained classifiers on anatomic landmarks to predict cerebral dysfunction, diagnosed from EEG readings by an epileptologist, and sedation, defined by the administration of sedative medications.
We built the largest video-EEG dataset to date (282,301 video minutes, 115 infants) sampled from a diverse patient population. Infant pose was accurately predicted in cross-validation, held-out frames, and held-out infants with respective receiver operating characteristic area under the curves (ROC-AUCs) 0.94, 0.83, 0.89. Median movement increased with age and, after accounting for age, was lower with sedative medications and in infants with cerebral dysfunction (all P < 5 × 10, 10,000 permutations). Sedation prediction had high performance on cross-validation, held-out intervals, and held-out infants (ROC-AUCs 0.90, 0.91, 0.87), as did prediction of cerebral dysfunction (ROC-AUCs 0.91, 0.90, 0.76).
We show that pose AI can be applied in an ICU setting and that an EEG diagnosis, cerebral dysfunction, can be predicted from video data alone. Deep learning with pose AI may offer a scalable, minimally invasive method for neuro-telemetry in the NICU.
Friedman Brain Institute Fascitelli Scholar Junior Faculty Grant and Thrasher Research Fund Early Career Award (F.R.). The Clinical and Translational Science Awards (CTSA) grant UL1TR004419 from the National Center for Advancing Translational Sciences. Office of Research Infrastructure of the National Institutes of Health under award number S10OD026880 and S10OD030463.
婴儿的警觉性和神经学变化可反映危及生命的病理状况，但通过体格检查进行评估，而体格检查可能是间歇性的且主观。需要可靠的连续方法。我们推测，我们用于跟踪运动、姿态人工智能（AI）的计算机视觉方法可预测新生儿重症监护病房（NICU）中的神经学变化。
我们收集了与脑电图（视频脑电图）相关的视频数据，这些数据来自纽约市西奈山医院年龄小于1岁的校正年龄婴儿，该医院是一家四级城市NICU，时间为2021年2月1日至2022年12月31日。我们在视频输入上训练了一种深度学习姿态识别算法，为每个婴儿的25帧标注14个解剖标志。然后，我们在解剖标志上训练分类器，以预测由癫痫专家根据脑电图读数诊断的脑功能障碍以及由镇静药物给药定义的镇静状态。
我们构建了迄今为止最大的视频脑电图数据集（282,301视频分钟，115名婴儿），该数据集来自不同的患者群体。在交叉验证、留出帧和留出婴儿中，婴儿姿态得到了准确预测，各自的曲线下面积（ROC-AUC）分别为0.94、0.83、0.89。中位运动随年龄增加，在考虑年龄因素后，使用镇静药物的婴儿和患有脑功能障碍的婴儿的中位运动较低（所有P<5×10，10,000次排列）。镇静预测在交叉验证、留出间隔和留出婴儿中表现良好（ROC-AUC分别为0.90、0.91、0.87），脑功能障碍的预测也是如此（ROC-AUC分别为0.91、0.90、0.76）。
我们表明姿态AI可应用于重症监护病房环境，并且仅从视频数据就能预测脑电图诊断结果——脑功能障碍。使用姿态AI的深度学习可能为NICU中的神经遥测提供一种可扩展的、微创方法。
弗里德曼脑研究所法西泰利学者初级教员奖和思拉舍研究基金早期职业奖（F.R.）。国家推进转化科学中心的临床和转化科学奖（CTSA）资助UL1TR004419。美国国立卫生研究院研究基础设施办公室，资助编号分别为S10OD026880和S10OD030463。
