  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.
