Abstract 352: Automated Deep Learning Algorithms Outperforms Gray-White Matter Ratio for the Detection of Severe Cerebral Edema in Cardiac Arrest Survivors
Zihao Wang, Jacob Dodelson, Annelise Kulpanowski, William Copen, Brandon Hancock, Eric S Rosenthal, Brian L Edlow, W T Kimberly, Edilberto Amorim, Michael B Westover, Mingming Ning, Pamela Schaefer, Rajeev Malhotra, Joseph Giacino, David M Greer, Ona Wu- Physiology (medical)
- Cardiology and Cardiovascular Medicine
Introduction: Significant knowledge gaps remain in predicting neurological outcomes for comatose cardiac arrest survivors. Severe cerebral edema (SCE) development offers an objective imaging surrogate for neurologic injury.
Aim: We have previously shown that deep learning (DL) algorithms can detect SCE on CT. We aim to determine whether our model will perform equivalently on an independent external cohort. We also compared the performance of the DL model with one based on the gray-white matter ratio (GWR) for detecting SCE.
Method: We retrospectively analyzed CT data (299 patients, 55±17 years, 71% male, 65 with SCE). Scans whose radiology reports described herniation or more than minimal ventricular effacement were classified as SCE. Datasets were randomly split (balancing for SCE) into training, validation, and testing cohorts. We used transfer learning with a 3D residual neural network framework to train our model. The probability of SCE was calculated. We applied the model to an external cohort (151 patients, 58±17 years, 63% male, 29 with SCE). Regions of interest (ROIs) were drawn in the caudate nuclei (CN) and posterior limbs of the internal capsule (PLIC). GWR was calculated as the ratio of mean x-ray attenuation values in the CN and the PLIC. Areas under the receiver operating curves (AUC) for the DL and GWR models were compared (bootstrap test).
Results: The optimal model achieved comparable accuracies for the internal validation cohort (84% [95% CI: 78-89%], n=60) and reserved test cohort (83% [95% CI: 76 to 89%], n=61). The external cohort consisted of 175 scans. The model’s accuracy was 87% [95% CI: 81% to 91%], comparable to the results from the testing and internal validation cohorts. AUC for DL was significantly greater than for GWR (92% vs. 73%, P=0.001) (see Figure).
Conclusions: Our result shows that deep transfer learning is a promising method to automate SCE detection on CT images and outperforms GWR measurements.