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ORIGINAL RESEARCH article

Front. Med.
Sec. Precision Medicine
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1436646
This article is part of the Research Topic Cluster-based Intelligent Recommendation System for Hybrid Healthcare Units View all 14 articles

Utilizing Deep Learning Models in an Intelligent Eye-Tracking System for Autism Spectrum Disorder Diagnosis

Provisionally accepted
Nizar Alsharif Nizar Alsharif 1Mosleh H. Al-Adhaileh Mosleh H. Al-Adhaileh 2Mohammed Al-Yaari Mohammed Al-Yaari 3*Nesren Farhah Nesren Farhah 4Zafar I. Khan Zafar I. Khan 5
  • 1 Department of Computer Engineering,Albaha University, Al Bahah, Saudi Arabia
  • 2 Deanship of E-Learning and Distance Education, King Faisal University, Al-Ahsa, Saudi Arabia
  • 3 King Faisal University, Al-Ahsa, Saudi Arabia
  • 4 Department of Health Informatics, College of Health Sciences, Saudi Electronic University, Dammam, Saudi Arabia
  • 5 Department of Computer Science, College of Computer & Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia

The final, formatted version of the article will be published soon.

    Timely and unbiased evaluation of Autism Spectrum Disorder (ASD) is essential for providing lasting benefits to affected individuals. However, conventional ASD assessment heavily relies on subjective criteria, lacking objectivity. Recent advancements propose the integration of modern processes, including artificial intelligence-based eye-tracking technology, for early ASD assessment. Nonetheless, the current diagnostic procedures for ASD often involve specialized investigations that are both time-consuming and costly, heavily reliant on the proficiency of specialists and employed techniques. To address the pressing need for prompt, efficient, and precise ASD diagnosis, an exploration of sophisticated intelligent techniques capable of automating disease categorization was presented. Leveraging deep learning algorithms, specifically MobileNet, VGG19, DenseNet169, and a hybrid of MobileNet-VGG19, automated classifiers, that hold promise for enhancing diagnostic precision and effectiveness, was developed. Users and healthcare professionals can utilize these classifiers to enhance the accuracy rate of ASD diagnosis. This study has utilized a freely accessible dataset comprising 547 eye-tracking systems that can be used to scan pathways obtained from 328 characteristically emerging children and 219 children with autism. To counter overfitting, state-ofthe-art image resampling approaches to expand the training dataset was employed. Notably, the MobileNet model demonstrated superior performance compared to existing systems, achieving an impressive accuracy of 100%, while the VGG19 model achieved 92% accuracy. These findings demonstrate the potential of eye-tracking data to aid physicians in efficiently and accurately screening for autism. Moreover, the reported results suggest that deep learning approaches outperform existing event detection algorithms, achieving a similar level of accuracy as manual coding.

    Keywords: Autism Spectrum Disorder, eye tracking, Deep leaning, VGG19, MobileNet, DenseNet169, Hybrid model

    Received: 22 May 2024; Accepted: 05 Jul 2024.

    Copyright: © 2024 Alsharif, Al-Adhaileh, Al-Yaari, Farhah and Khan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Mohammed Al-Yaari, King Faisal University, Al-Ahsa, Saudi Arabia

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.