Early identification of Autism Spectrum Disorder (ASD) is crucial for enabling timely intervention and improving
developmental outcomes. Conventional diagnostic methods rely heavily on behavioral observation and expert judgment, often
leading to delayed diagnosis. In recent years, machine learning and deep learning techniques have been increasingly explored to
support early ASD prediction using facial images, behavioral videos, neuro imaging data, and clinical datasets. This review
presents a comparative analysis of recent approaches for early ASD prediction, focusing on methodologies, datasets, predictive
performance, and key limitations. The findings indicate that deep learning-based approaches, particularly those using facial
images and behavioral video analysis, generally achieve higher accuracy compared to traditional machine learning models.
However, challenges such as limited dataset diversity, reduced cross-dataset generalization, lack of interpretability, and
insufficient clinical validation remain prevalent across studies. In addition to the review, this work includes the implementation
of an existing deep learning-based facial image classification method to validate reported findings. Overall, this critical review
highlights current progress and emphasizes the need for robust, interpretable, and clinically validated approaches to support
early ASD screening and complement traditional diagnostic practices.
[1] Indian Council of Medical Research (ICMR), “ICMR Annual Report 2023–24,” 2024, Accessed: Mar. 24, 2026.
[Online]. Available: https://www.icmr.gov.in/icmrobject/uploads/Documents/ 1744706555_icmr_annualreport_2023
24_78804328_1.pdf, 2024
[2] F. W. Alsaade and M. S. Alzahrani, “Classification and Detection of Autism Spectrum Disorder Based on Deep
Learning Algorithms,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/8709145.
[3] A. Rashid and S. Shaker, “Autism spectrum Disorder detection Using Face Features based on Deep Neural network,”
Wasit Journal of Computer and Mathematics Science, vol. 2, no. 1, pp. 74–83, Mar. 2023, doi: 10.31185/wjcm.100.
[4] J Wang et al., “Early markers of neurodevelopmental disorders based on general movements for very preterm infants:
Study protocol for a multicentre prospective cohort study in a clinical setting in China,” BMJ Open, vol. 13, no. 5, May
2023, doi: 10.1136/bmjopen-2022-069692.
[5] M. T. Ali et al., “A personalized classification of behavioral severity of autism spectrum disorder using a
comprehensive machine learning framework,” Sci. Rep., vol. 13, no. 1, Dec. 2023, doi: 10.1038/s41598-023-43478-z.
[6] M. S. Alam, E. A. A. Elsheikh, F. M. Suliman, M. M. Rashid, and A. R. Faizabadi, “Innovative Strategies for Early
Autism Diagnosis: Active Learning and Domain Adaptation Optimization,” Diagnostics, vol. 14, no. 6, Mar. 2024, doi:
10.3390/diagnostics14060629.
[7] M. M. Rashid, M. S. Alam, M. Y. Ali, and S. Yvette, “Developing a multi-modal dataset for deep learning-based neural
networks in autism spectrum disorder diagnosis,” in AIP Conference Proceedings, American Institute of Physics, Aug.
2024. doi: 10.1063/5.0229867.
[8] S. Jeba Berlin, D. Pandian, S. S. Rajagopalan, and D. B. Jayagopi, “DETECTING A CHILD’S STIMMING
BEHAVIOURS FOR AUTISM SPECTRUM DISORDER DIAGNOSIS USING RGBPOSE-SLOWFAST
NETWORK,” in Proceedings - International Conference on Image Processing, ICIP, IEEE Computer Society, 2022,
pp. 3356–3360. doi: 10.1109/ICIP46576.2022.9897867.
[9] A. S. Heinsfeld, A. R. Franco, R. C. Craddock, A. Buchweitz, and F. Meneguzzi, “Identification of autism spectrum
disorder using deep learning and the ABIDE dataset,” Neuroimage Clin., vol. 17, pp. 16–23, 2018, doi:
10.1016/j.nicl.2017.08.017.
[10] K. Vakadkar, D. Purkayastha, and D. Krishnan, “Detection of Autism Spectrum Disorder in Children Using Machine
Learning Techniques,” SN Comput. Sci., vol. 2, no. 5, Sep. 2021, doi: 10.1007/s42979-021-00776-5.
[11] V. Lokegaonkaret al., “Introducing SSBD+ Dataset with a Convolutional Pipeline for detecting Self-Stimulatory
Behaviours in Children using raw videos,” Nov. 2023, [Online]. Available: http://arxiv.org/abs/2311.15072
[12] C. H. Yoo, J. H. Yoo, M. K. Back, W. J. Wang, and Y. G. Shin, “A unified framework to stereotyped behavior
detection for screening Autism Spectrum Disorder,” Pattern Recognit. Lett., vol. 186, pp. 156–163, Oct. 2024, doi:
10.1016/j.patrec.2024.10.001.
[13] B. Khan, S. M. Bhatti, and A. Akram, “Autism Spectrum Disorder Detection in Children Via Deep Learning Models
Based on Facial Images,” Bulletin of Business and Economics (BBE), vol. 13, no. 1, Mar. 2024, doi:
10.61506/01.00241.
[14] M. F. Rabbi, S. M. M. Hasan, A. I. Champa, and M. A. Zaman, “A Convolutional Neural Network Model for Early
Stage Detection of Autism Spectrum Disorder,” in 2021 International Conference on Information and Communication
Technology for Sustainable Development, ICICT4SD 2021 - Proceedings, Institute of Electrical and Electronics
Engineers Inc., Feb. 2021, pp. 110–114. doi: 10.1109/ICICT4SD50815.2021.9397020.
[15] H. Alkahtani, T. H. H. Aldhyani, and M. Y. Alzahrani, “Deep Learning Algorithms to Identify Autism Spectrum
Disorder in Children-Based Facial Landmarks,” Applied Sciences (Switzerland), vol. 13, no. 8, Apr. 2023, doi:
10.3390/app13084855.
[16] A. Lu and M. Perkowski, “Deep learning approach for screening autism spectrum disorder in children with facial
images and analysis of ethnoracial factors in model development and application,” Brain Sci., vol. 11, no. 11, Nov.
2021, doi: 10.3390/brainsci11111446.
