Abstract Sat302: Quantification of Neuronal Damage after Cardiac Arrest Using Machine Learning

Abstract

Background: Brain injury is a major cause of death and disability after cardiac arrest (CA). Quantitative histology enables the assessment of neuronal damage in preclinical models and CA patients. However, manual quantification of labeled neurons is time-consuming and variable, posing a significant challenge to the comprehensive assessment of the brain injury severity and neuroprotective therapy's efficacy. Hypothesis: Machine learning (ML) approach can identify and quantify neuronal damage from brain histological images of swine CA models with comparable accuracy to human raters. Methods: We developed a swine CA model to simulate out-of-hospital CA with 5 or 10 minutes of untreated ventricular fibrillation. Following 24 hours of standardized post-CA care, the animals were euthanized by transcardial perfusion with 4% paraformaldehyde. The brains were post-fixed, cryoprotected, and cryosectioned. Coronal sections (20 μm) containing the caudate putamen were stained with Fluoro-Jade C to label injured neurons. Three blinded human raters quantified Fluoro-Jade C-positive neurons in 136 images from 15 animals. These images were split into training (n=54), validation (n=27), and testing (n=55) sets for ML model development. We compared transfer learning models including VGG16, MobileNetV2, DeepLabv3+, and SegFormer. Model performance was evaluated on individual cells via precision, recall, and F1-score, then by comparing cell counts to the human raters for the best performing model. Results: Human raters showed strong reliability in image-wise counts of Fluoro-Jade C neurons with an average pairwise correlation coefficient of R=0.936. The SegFormer model demonstrated the best performance, with a test-set R=0.989 compared to neurons identified by 2 of 3 human raters, or an average R=0.967 when compared to each rater individually (Figures 1 and 2). On an individual cell level, the model yielded a precision of 0.789, a recall of 0.709, and an F1-score of 0.747 (Table 1). Conclusions: We developed and validated a reliable automated ML approach to quantify neuronal damage after CA in a swine model. Future studies will focus on validating the ML models for other brain regions, other stainings, and application in quantitative histology for CA patients.