The University of Miami recently posted about their “Behavioral Imaging of Autism” study at the Center for Autism and Related Disabilities (CARD). The study utilizes digitally collected data to diagnose patients. In the above photo Amy Ahn works with a toddler while wearing glasses that record facial expressions and gestures. This data will be used to develop technologies that will assist clinicians in diagnosing Autism Spectrum Disorder (ASD). A main goal of the study is to identify technologies that can improve access to diagnoses as well as diagnose patients earlier leading to earlier interventions.
…the earlier children are diagnosed, the quicker they can take advantage of interventions that can improve their lives dramatically.
This study includes a collaboration with Mei-Ling Shyu’s lab in the Department of Engineering at the University of Miami. They recently submitted an abstract to be presented at the 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). The abstract describes the ‘SP-ASDNet’ model, this classifier utilizes long short-term memory networks and convolution neural networks. Using visual data of a participant’s scanpath (when looking at an image) it is able to classify patient’s with ASD. Currently the SP-ASDNet was able to achieve 74.22% accuracy which is close to psychologists diagnosis rate of 70%.
There are a number of groups working towards machine learning assisted diagnosis of ASD. If you would like to learn more about CARD at the University of Miami checkout their website. If you’d like to learn more about machine learning approaches to diagnose ASD here is a helpful review.
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