Autism AI: a New Autism Screening System Based on Artificial Intelligence

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dc.contributor.author Shahamiri Seyed Reza en
dc.contributor.author Thabtah Fadi en
dc.date.accessioned 2020-10-16T03:48:24Z
dc.date.available 2020-10-16T03:48:24Z
dc.date.issued 2020-6-20 en
dc.identifier.issn 1866-9956 en
dc.identifier.uri http://hdl.handle.net/2292/53353
dc.description.abstract © 2020, Springer Science+Business Media, LLC, part of Springer Nature. Autistic spectrum disorder (ASD) is a neurodevelopment condition normally linked with substantial healthcare costs and time-consuming assessments where early detection of ASD traits can help limit the development of the condition. The existing conventional ASD screening methods contain a large number of items and are based on domain expert rules which may be criticized of being lengthy and subjective. More importantly, these methods use basic scoring functions to pinpoint to autistic traits rather intelligently learning patterns from cases and controls which can be more accurate and efficient. One promising solution to deal with the above issues and speed up ASD assessment referrals is to develop intelligent artificial intelligence screening methods that not only provide accurate pre-diagnostic classifications but also improve the efficiency and accessibility of the screening process. This paper proposes a new autism screening system that replaces the conventional scoring functions in classic screening methods with deep learning algorithms. The system is composed of a mobile application that provides the user interface capturing questionnaire data; an intelligent ASD detection web service that interfaces with a Convolutional Neural Network (CNN) trained with historical ASD cases; and a database that enables the CNN to learn new knowledge from future users of the system. The CNN classification method was evaluated against a large autism dataset consisting of adult, adolescent, child, and toddler cases and controls. The results obtained from the CNN were compared with other intelligent algorithms in which superior performance was achieved by the CNN. Particularly, the proposed CNN-based ASD classification system revealed higher accuracy, sensitivity, and specificity when compared with conventional screening methods. This indeed will be of high benefit for busy medical clinics and diagnosticians and could possibly be a new direction to change the way ASD diagnosis process is conducted in the future. en
dc.language English en
dc.publisher SPRINGER en
dc.relation.ispartofseries COGNITIVE COMPUTATION en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.subject Science & Technology en
dc.subject Technology en
dc.subject Life Sciences & Biomedicine en
dc.subject Computer Science, Artificial Intelligence en
dc.subject Neurosciences en
dc.subject Computer Science en
dc.subject Neurosciences & Neurology en
dc.subject Artificial intelligence en
dc.subject Autism spectrum disorder (SD) en
dc.subject Cognitive computing en
dc.subject Deep learning en
dc.subject Intelligent systems en
dc.subject Machine learning en
dc.subject Medical screening en
dc.subject SPECTRUM QUOTIENT en
dc.title Autism AI: a New Autism Screening System Based on Artificial Intelligence en
dc.type Journal Article en
dc.identifier.doi 10.1007/s12559-020-09743-3 en
pubs.issue 4 en
pubs.begin-page 766 en
pubs.volume 12 en
dc.date.updated 2020-09-07T23:11:53Z en
dc.rights.holder Copyright: The author en
pubs.author-url http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000541397300001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=6e41486220adb198d0efde5a3b153e7d en
pubs.end-page 777 en
pubs.publication-status Published en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Article en
pubs.subtype Journal en
pubs.elements-id 805041 en
dc.identifier.eissn 1866-9964 en
pubs.number 4 en


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