Machine learning to identify hypoxic-ischemic brain injury on early head CT after pediatric cardiac arrest.
Authors
Affiliations (6)
Affiliations (6)
- 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. Electronic address: [email protected].
- Departments of Radiology, Biomedical Informatics, and Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
- Departments of Emergency Medicine, Critical Care Medicine, and Neurology, University of Pittsburgh, Pittsburgh, PA, USA.
- Department of Radiology, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
- 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.
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
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 hours 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±0.01for 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.