Machine learning to identify hypoxic-ischemic brain injury on early head CT after pediatric cardiac arrest.

Abstract

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.