KEYWORDS: Magnetic resonance imaging, 3D modeling, Epilepsy, Brain, Neuroimaging, Image quality, 3D image processing, Pathology, Data modeling, Medical imaging
During childhood, neurological involvement in tuberous sclerosis complex (TSC) is a leading cause of death. Neurological involvement, including epilepsy, can cause significant long-term sequelae in children. Brain involvement in TSC can be detected by magnetic resonance imaging (MRI). Still, neuroimaging analysis is time-and labor-intensive, begging the need for automated approaches to these tasks to improve speed, accuracy, and availability. We explored the general feasibility of using three-dimensional convolutional neural networks (CNNs) to automatically enhance image diagnosis quality and consistency to identify anatomical abnormalities in TSC children. We trained the 3D CNN on axial T1-weighted, axial T2-weighted FLAIR, and 3DT1-FSPGR weighted images from 296 TSC and 245 Normal cases from birth to 8 years of age acquired at LeBonheur Children’s Hospital. In the best performing approach, we achieved an accuracy of 0.86 [95% CI:0.76-0.97] with 0.95% AUC. The code can be found in https://github.com/shabanian2018/TSC3DCNN
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