OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A Multimodal Approach for Identifying Autism Spectrum Disorders in Children
Junxia Han, Guoqian Jiang, Gaoxiang Ouyang, et al.
IEEE Transactions on Neural Systems and Rehabilitation Engineering (2022) Vol. 30, pp. 2003-2011
Open Access | Times Cited: 49

Showing 1-25 of 49 citing articles:

Application of data fusion for automated detection of children with developmental and mental disorders: A systematic review of the last decade
Smith K. Khare, Sonja March, Prabal Datta Barua, et al.
Information Fusion (2023) Vol. 99, pp. 101898-101898
Open Access | Times Cited: 72

Multi-modality approaches for medical support systems: A systematic review of the last decade
Massimo Salvi, Hui Wen Loh, Silvia Seoni, et al.
Information Fusion (2023) Vol. 103, pp. 102134-102134
Open Access | Times Cited: 52

Autism Spectrum Disorder detection framework for children based on federated learning integrated CNN-LSTM
Abdullah Lakhan, Mazin Abed Mohammed, Karrar Hameed Abdulkareem, et al.
Computers in Biology and Medicine (2023) Vol. 166, pp. 107539-107539
Closed Access | Times Cited: 41

The emergence of artificial intelligence in autism spectrum disorder research: A review of neuro imaging and behavioral applications
i b, P. M. Durai Raj Vincent
Computer Science Review (2025) Vol. 56, pp. 100718-100718
Closed Access | Times Cited: 1

Deep learning with image-based autism spectrum disorder analysis: A systematic review
Md. Zasim Uddin, Md Shahriar, Md. Nadim Mahamood, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 127, pp. 107185-107185
Closed Access | Times Cited: 23

Identification of autism spectrum disorder based on electroencephalography: A systematic review
Jing Li, Xiangjin Kong, Linlin Sun, et al.
Computers in Biology and Medicine (2024) Vol. 170, pp. 108075-108075
Closed Access | Times Cited: 14

Eye Tracking Biomarkers for Autism Spectrum Disorder Detection using Machine Learning and Deep Learning Techniques: Review
R. Asmetha Jeyarani, Radha Senthilkumar
Research in autism spectrum disorders (2023) Vol. 108, pp. 102228-102228
Open Access | Times Cited: 18

Screening for Autism
Kate Wallis, W. Spencer Guthrie
Pediatric Clinics of North America (2024) Vol. 71, Iss. 2, pp. 127-155
Closed Access | Times Cited: 5

3T dilated inception network for enhanced autism spectrum disorder diagnosis using resting-state fMRI data
V. Kavitha, C. Siva Ram Murthy
Cognitive Neurodynamics (2025) Vol. 19, Iss. 1
Closed Access

INN-ASDNet: Embracing Involutional Neural Networks and Random Forest for Prediction of Autism Spectrum Disorder
Bhagya Lakshmi Polavarapu, Mahesh Kumar Morampudi, T N Tarun, et al.
Arabian Journal for Science and Engineering (2025)
Closed Access

Machine learning in automated diagnosis of autism spectrum disorder: a comprehensive review
Khosro Rezaee
Computer Science Review (2025) Vol. 56, pp. 100730-100730
Closed Access

Dual Deep Autoencoder Split Generative Adversarial Networks with Gooseneck Barnacle Optimization-Based Prediction of Autism Spectrum Disorder in Facial Images
Jyothi Goddu, S. Anuradha, Y. Srinivas
International Journal of Computational Intelligence and Applications (2025)
Closed Access

STL Net: A spatio-temporal multi-task learning network for Autism spectrum disorder identification
Yongjie Huang, Yanyan Zhang, Man Chen, et al.
Biomedical Signal Processing and Control (2025) Vol. 106, pp. 107678-107678
Closed Access

Innovative Deep Learning Approaches in Autism Research
Elham Amjad, Babak Sokouti
(2025), pp. 1-12
Closed Access

Vitasd: Robust Vision Transformer Baselines for Autism Spectrum Disorder Facial Diagnosis
Xu Cao, Wenqian Ye, Elena Sizikova, et al.
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2023), pp. 1-5
Open Access | Times Cited: 15

Approaches to Speech Therapy for Children with Autism Spectrum Disorders (ASD)
Mariana Нryntsiv, Mariia Zamishchak, Yuliia Bondarenko, et al.
International Journal of Child Health and Nutrition (2025) Vol. 14, Iss. 1, pp. 32-45
Open Access

Self-training EEG discrimination model with weakly supervised sample construction: An age-based perspective on ASD evaluation
Tengfei Gao, Dan Chen, Meiqi Zhou, et al.
Neural Networks (2025) Vol. 187, pp. 107337-107337
Closed Access

Commentary: Machine learning for autism spectrum disorder diagnosis – challenges and opportunities – a commentary on Schulte‐Rüther et al. (2022)
Xu Cao, Jianguo Cao
Journal of Child Psychology and Psychiatry (2023) Vol. 64, Iss. 6, pp. 966-967
Open Access | Times Cited: 10

Multimodal Deep Learning in Early Autism Detection—Recent Advances and Challenges
Sheril Sophia Dcouto, J. Pradeepkandhasamy
(2024) Vol. 118, pp. 205-205
Open Access | Times Cited: 3

Machine learning approaches for electroencephalography and magnetoencephalography analyses in autism spectrum disorder: A systematic review
Sushmit Das, Reza Zomorrodi, Mina Mirjalili, et al.
Progress in Neuro-Psychopharmacology and Biological Psychiatry (2022) Vol. 123, pp. 110705-110705
Open Access | Times Cited: 18

Eye-movement analysis on facial expression for identifying children and adults with neurodevelopmental disorders
Kota Iwauchi, Hiroki Tanaka, Kosuke Okazaki, et al.
Frontiers in Digital Health (2023) Vol. 5
Open Access | Times Cited: 8

Utilizing Constructed Neural Networks for Autism Screening
Eugenia I. Toki, Jenny Pange, Giorgos Tatsis, et al.
Applied Sciences (2024) Vol. 14, Iss. 7, pp. 3053-3053
Open Access | Times Cited: 2

Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder
Ali Jahani, Iman Jahani, Ali Khadem, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2

Page 1 - Next Page

Scroll to top