Deep Learning Mixture-of-Experts Approach for Cytotoxic Edema Assessment in Infants and Children

TL;DR

This paper introduces a deep learning framework using 3D CNNs for accurate classification of Cytotoxic Edema in pediatric MRI scans, with potential implications for early diagnosis and understanding of traumatic brain injury outcomes.

Contribution

The paper presents a novel volumetric deep learning approach for CE diagnosis, demonstrating high accuracy and enabling analysis of its correlation with AHT and patient outcomes.

Findings

F1 score of 0.91 in CE classification

CE is highly correlated with AHT and patient outcomes

Proposed method outperforms previous models in accuracy

Abstract

This paper presents a deep learning framework for image classification aimed at increasing predictive performance for Cytotoxic Edema (CE) diagnosis in infants and children. The proposed framework includes two 3D network architectures optimized to learn from two types of clinical MRI data , a trace Diffusion Weighted Image (DWI) and the calculated Apparent Diffusion Coefficient map (ADC). This work proposes a robust and novel solution based on volumetric analysis of 3D images (using pixels from time slices) and 3D convolutional neural network (CNN) models. While simple in architecture, the proposed framework shows significant quantitative results on the domain problem. We use a dataset curated from a Childrens Hospital Colorado (CHCO) patient registry to report a predictive performance F1 score of 0.91 at distinguishing CE patients from children with severe neurologic injury without CE. In addition, we perform analysis of our systems output to determine the association of CE with Abusive Head Trauma (AHT) , a type of traumatic brain injury (TBI) associated with abuse , and overall functional outcome and in hospital mortality of infants and young children. We used two clinical variables, AHT diagnosis and Functional Status Scale (FSS) score, to arrive at the conclusion that CE is highly correlated with overall outcome and that further study is needed to determine whether CE is a biomarker of AHT. With that, this paper introduces a simple yet powerful deep learning based solution for automated CE classification. This solution also enables an indepth analysis of progression of CE and its correlation to AHT and overall neurologic outcome, which in turn has the potential to empower experts to diagnose and mitigate AHT during early stages of a childs life.