Kirschen Matthew P, Li Jiren, Elmer Jonathan, Manteghinejad Amirreza, Arefan Dooman, Graham Kathryn, Morgan Ryan W, Nadkarni Vinay, Diaz-Arrastia Ramon, Berg Robert, Topjian Alexis, Vossough Arastoo, Wu Shandong
Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Departments of Radiology, Biomedical Informatics, and Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
Resuscitation. 2025 Jun 27:110693. doi: 10.1016/j.resuscitation.2025.110693.
To train deep learning models to detect hypoxic-ischemic brain injury (HIBI) on early CT scans after pediatric out-of-hospital cardiac arrest (OHCA) and determine if models could identify HIBI that was not visually appreciable to a radiologist.
Retrospective study of children who had a CT scan within 24 h of OHCA compared to age-matched controls. We designed models to detect HIBI by discriminating CT images from OHCA cases and controls, and predict death and unfavorable outcome (PCPC 4-6 at hospital discharge) among cases. Model performance was measured by AUC. We trained a second model to distinguish OHCA cases with radiologist-identified HIBI from controls without OHCA and tested the model on OHCA cases without radiologist-identified HIBI. We compared outcomes between OHCA cases with and without model-categorized HIBI.
We analyzed 117 OHCA cases (age 3.1 [0.7-12.2] years); 43 % died and 58 % had unfavorable outcome. Median time from arrest to CT was 2.1 [1.0,7.2] hours. Deep learning models discriminated OHCA cases from controls with a mean AUC of 0.87 ± 0.05. Among OHCA cases, mean AUCs for predicting death and unfavorable outcome were 0.79 ± 0.06 and 0.69 ± 0.06, respectively. Mean AUC was 0.98 ± 0.01 for discriminating between 44 OHCA cases with radiologist-identified HIBI and controls. Among 73 OHCA cases without radiologist-identified HIBI, the model identified 36 % as having presumed HIBI; 31 % of whom died compared to 17 % of cases without HIBI identified radiologically and via the model (p = 0.174).
Deep learning models can identify HIBI on early CT images after pediatric OHCA and detect some presumed HIBI visually not identified by a radiologist.
训练深度学习模型，以在儿科院外心脏骤停（OHCA）后的早期CT扫描上检测缺氧缺血性脑损伤（HIBI），并确定模型是否能够识别放射科医生在视觉上无法察觉的HIBI。
对OHCA后24小时内进行CT扫描的儿童与年龄匹配的对照组进行回顾性研究。我们设计模型，通过区分OHCA病例和对照组的CT图像来检测HIBI，并预测病例中的死亡和不良结局（出院时PCPC 4-6）。模型性能通过AUC进行测量。我们训练了第二个模型，以区分有放射科医生识别出HIBI的OHCA病例和没有OHCA的对照组，并在没有放射科医生识别出HIBI的OHCA病例上测试该模型。我们比较了有和没有模型分类HIBI的OHCA病例之间的结局。
我们分析了117例OHCA病例（年龄3.1 [0.7-12.2]岁）；43%死亡，58%有不良结局。从心脏骤停到CT的中位时间为2.1 [1.0,7.2]小时。深度学习模型区分OHCA病例和对照组的平均AUC为0.87±0.05。在OHCA病例中，预测死亡和不良结局的平均AUC分别为0.79±0.06和0.69±0.06。区分44例有放射科医生识别出HIBI的OHCA病例和对照组的平均AUC为0.98±0.01。在73例没有放射科医生识别出HIBI的OHCA病例中，该模型将36%识别为可能有HIBI；其中31%死亡，而放射学和模型均未识别出HIBI的病例中这一比例为17%（p = 0.174）。
深度学习模型可以在儿科OHCA后的早期CT图像上识别HIBI，并检测出一些放射科医生在视觉上未识别出的可能的HIBI。
