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机器学习在心脏骤停后缺氧缺血性脑损伤的早期检测中的应用。

Machine Learning for Early Detection of Hypoxic-Ischemic Brain Injury After Cardiac Arrest.

机构信息

Neurosciences Intensive Care Unit, Department of Neurology, University of Chicago Medicine and Biological Sciences, 5841 S. Maryland Ave., MC 2030, Chicago, IL, 60637-1470, USA.

Department of Neurological Surgery, University of Chicago Medicine and Biological Sciences, Chicago, IL, USA.

出版信息

Neurocrit Care. 2022 Jun;36(3):974-982. doi: 10.1007/s12028-021-01405-y. Epub 2021 Dec 6.

Abstract

BACKGROUND

Establishing whether a patient who survived a cardiac arrest has suffered hypoxic-ischemic brain injury (HIBI) shortly after return of spontaneous circulation (ROSC) can be of paramount importance for informing families and identifying patients who may benefit the most from neuroprotective therapies. We hypothesize that using deep transfer learning on normal-appearing findings on head computed tomography (HCT) scans performed after ROSC would allow us to identify early evidence of HIBI.

METHODS

We analyzed 54 adult comatose survivors of cardiac arrest for whom both an initial HCT scan, done early after ROSC, and a follow-up HCT scan were available. The initial HCT scan of each included patient was read as normal by a board-certified neuroradiologist. Deep transfer learning was used to evaluate the initial HCT scan and predict progression of HIBI on the follow-up HCT scan. A naive set of 16 additional patients were used for external validation of the model.

RESULTS

The median age (interquartile range) of our cohort was 61 (16) years, and 25 (46%) patients were female. Although findings of all initial HCT scans appeared normal, follow-up HCT scans showed signs of HIBI in 29 (54%) patients (computed tomography progression). Evaluating the first HCT scan with deep transfer learning accurately predicted progression to HIBI. The deep learning score was the most significant predictor of progression (area under the receiver operating characteristic curve = 0.96 [95% confidence interval 0.91-1.00]), with a deep learning score of 0.494 having a sensitivity of 1.00, specificity of 0.88, accuracy of 0.94, and positive predictive value of 0.91. An additional assessment of an independent test set confirmed high performance (area under the receiver operating characteristic curve = 0.90 [95% confidence interval 0.74-1.00]).

CONCLUSIONS

Deep transfer learning used to evaluate normal-appearing findings on HCT scans obtained early after ROSC in comatose survivors of cardiac arrest accurately identifies patients who progress to show radiographic evidence of HIBI on follow-up HCT scans.

摘要

背景

在自主循环恢复(ROSC)后不久,确定心脏骤停后存活的患者是否患有缺氧缺血性脑损伤(HIBI)可能至关重要,这有助于告知患者家属,并确定最可能从神经保护治疗中获益的患者。我们假设,使用深度转移学习来分析 ROSC 后进行的头部计算机断层扫描(HCT)上正常表现的结果,可以帮助我们识别 HIBI 的早期证据。

方法

我们分析了 54 名昏迷的心脏骤停幸存者,这些患者都进行了 ROSC 后早期的初始 HCT 扫描和后续的 HCT 扫描。每位纳入患者的初始 HCT 扫描均由经过认证的神经放射科医师读为正常。深度转移学习用于评估初始 HCT 扫描并预测后续 HCT 扫描中 HIBI 的进展。一组 16 名额外的患者用于模型的外部验证。

结果

我们队列的中位年龄(四分位距)为 61(16)岁,25(46%)名患者为女性。尽管所有初始 HCT 扫描的结果均显示正常,但 29(54%)名患者(CT 进展)的后续 HCT 扫描显示出 HIBI 的迹象。使用深度转移学习评估首次 HCT 扫描可准确预测向 HIBI 的进展。深度学习评分是进展的最显著预测指标(受试者工作特征曲线下面积为 0.96[95%置信区间 0.91-1.00]),深度学习评分为 0.494 时具有 1.00 的敏感性、0.88 的特异性、0.94 的准确性和 0.91 的阳性预测值。对独立测试集的进一步评估证实了该方法的高性能(受试者工作特征曲线下面积为 0.90[95%置信区间 0.74-1.00])。

结论

在昏迷的心脏骤停幸存者中,使用深度转移学习来评估 ROSC 后早期的 HCT 扫描正常表现,可以准确识别出在后续 HCT 扫描中进展为显示 HIBI 放射学证据的患者。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2a1f/8647961/4a747c24d59f/12028_2021_1405_Fig1_HTML.jpg

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