Asd Prediction from Structural Mri with Machine Learning
Abstract
Autism Spectrum Disorder (ASD) is part of the developmental disabilities. There are numerous symptoms for ASD patients, including lack of abilities in social interaction, communication obstacle and repeatable behaviors (Centers for Disease Control and Prevention., 2020c). Meanwhile, the rate of ASD prevalence has kept rising by the past 20 years from 1 out of 150 in 2000 to 1 out of 54 in 2016 (Centers for Disease Control and Prevention., 2020a). In addition, the ASD population is quite large. Specifically, 3.5 million Americans live with ASD in the year of 2014, which will cost U.S. citizens $236-$262 billion dollars annually for autism services (Buescher, Cidav, Knapp, & Mandell, 2014). So, it is critical to make an accurate diagnosis for preschool age children with ASD, in order to give them a better life. Instead of using traditional ASD behavioral tests, such as ADI-R, ADOS, and DSM-IV, we applied brain MRI images as input to make diagnosis. We revised 3D-ResNet structure to fit 110 preschool children’s brain MRI data, along with Convolution 3D and VGG model. The prediction accuracy with raw data is 65.22%. The accuracy is significantly improved to 82.61% by removing the noise around the brain. We also showed the speed of ML prediction is 308 times faster than behavior tests.
Degree
M.Sc.
Advisors
Yang, Purdue University.
Subject Area
Artificial intelligence|Computer science|Disability studies|Medical imaging
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