A Survey of Advanced Classification and Feature Extraction Techniques Across Various Autism Data Sources

Sonia Slimen, Sonia Slimen, Anis Mezghani, Anis Mezghani, Monji Kherallah, Faiza Charfi

2025

Abstract

Autism, often known as autism spectrum disorder (ASD), is characterized by a range of neurodevelopmental difficulties that impact behavior, social relationships, and communication. Early diagnosis is crucial to provide timely interventions and promote the best possible developmental outcomes. Although well-established, traditional methods such as behavioral tests, neuropsychological assessments, and clinical facial feature analysis are often limited by societal stigma, expense, and accessibility. In recent years, artificial intelligence (AI) has emerged as a transformative tool. AI utilizes advanced algorithms to analyze a variety of data modalities, including speech patterns, kinematic data, facial photographs, and magnetic resonance imaging (MRI), in order to diagnose ASD. Each modality offers unique insights: kinematic investigations show anomalies in movement patterns, face image analysis reveals minor phenotypic indicators, speech analysis shows aberrant prosody, and MRI records neurostructural and functional problems. By accurately extracting information from these modalities, deep learning approaches enhance diagnostic efficiency and precision. However, challenges remain, such as the need for diverse datasets to build robust models, potential algorithmic biases, and ethical concerns regarding the use of private biometric data. This paper provides a comprehensive review of feature extraction methods across various data modalities, emphasising how they might be included into AI frameworks for the detection of ASD. It emphasizes the potential of multimodal AI systems to revolutionize autism diagnosis and their responsible implementation in clinical practice by analyzing the advantages, limitations, and future directions of these approaches.

Paper Citation

in Harvard Style

Slimen S., Mezghani A., Kherallah M. and Charfi F. (2025). A Survey of Advanced Classification and Feature Extraction Techniques Across Various Autism Data Sources. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 849-860. DOI: 10.5220/0013379600003890

in Bibtex Style

@conference{icaart25,
author={Sonia Slimen and Anis Mezghani and Monji Kherallah and Faiza Charfi},
title={A Survey of Advanced Classification and Feature Extraction Techniques Across Various Autism Data Sources},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={849-860},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013379600003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - A Survey of Advanced Classification and Feature Extraction Techniques Across Various Autism Data Sources
SN - 978-989-758-737-5
AU - Slimen S.
AU - Mezghani A.
AU - Kherallah M.
AU - Charfi F.
PY - 2025
SP - 849
EP - 860
DO - 10.5220/0013379600003890
PB - SciTePress