AUTHOR=Al-Adhaileh Mosleh Hmoud , Alsubari Saleh N. M. , Al-Nefaie Abdullah H. , Ahmad Sultan , Alhamadi Asma Abdulmana 

TITLE=Diagnosing autism spectrum disorder based on eye tracking technology using deep learning models

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1690177

DOI=10.3389/fmed.2025.1690177

ISSN=2296-858X

ABSTRACT=IntroductionChildren with Autism Spectrum Disorder (ASD) often find it difficult to maintain eye contact, which is vital for social communication. Eye tracking (ET) technology helps determine how long children with ASD focus on someone, how frequently they do so, and in which direction their gaze moves. ET provides insights into social attention by enabling precise, real-time tracking of gaze patterns as individuals process social information visually. It is a dependable method for identifying and developing social attentional biomarkers, particularly in challenging conditions like ASD.ObjectiveThis study aims to implement deep learning (DL) algorithms using eye-tracking data from social attention tasks involving children with ASD.MethodsThe approach was tested using standard datasets collected from individuals with and without ASD through eye-tracking technology. Convolutional neural networks (CNNs) and long short-term memory (LSTM) models were used to analyze data from children with ASD. Data preprocessing techniques addressed missing data and converted categorical features into numerical values. Mutual information-based feature selection was employed to reduce the feature set by identifying the most relevant features, thereby improving system performance. These features were then analyzed using LSTM and CNN-LSTM models to evaluate their potential for diagnosing ASD.ResultsThe experimental results showed that the highest accuracy achieved was 99.78% with the CNN-LSTM model. Furthermore, the findings indicated that the proposed method outperformed previous studies.ConclusionThe system successfully diagnosed ASD using the ET dataset. This approach shows promise for clinical application, assisting healthcare professionals in diagnosing ASD more accurately through advanced artificial intelligence technology.