Jul 2026· International Conference on Wirtschaftsinformatik· 0 citations· 79 references
TL;DR
Based on the evaluated studies, transfer learning with diverse datasets and modalities has great promise for early ASD diagnosis, and a hybrid transfer learning-based framework is advised to assist clinicians and therapists in accurately diagnosing and assessing ASD severity.
Abstract
Autism spectrum disorder (ASD), a neurodevelopmental condition, affects approximately 1% of children globally and their social and cognitive abilities. This leads to difficulties in communication, repetitive behaviors, psychomotor skills, and eye contact maintenance. In recent years, there has been an increased utilization of Artificial Intelligence (AI) for the early detection of autism. Using knowledge gathered from 116 peer-reviewed publications, this study assessed algorithmic efficacy, model performance, multimodal data integration, classification metrics, generalization ability, and clinical usefulness. Machine Learning, Deep Learning (DL), Graph Neural Networks, Federated Learning, auto encoders, and Natural Language Processing, the Attention Mechanism seemed to be among the several AI techniques that were investigated in this study. To protect a child's developmental progress, this survey investigates how early detection of autism facilitates prompt therapeutic treatments, minimizes intellectual disabilities, improves mobility, and supports customized care. The research emphasizes the effectiveness of predicting ASD with minimal time using different data modalities, including EEG microstates, ABIDE I & II, to assess DL models such as GoogleNet, Xception, AlexNet, ResNet, VGG, and DenseNet. Three types of data were analyzed: biochemical (biomarkers and physiological metrics), behavioral (facial features, eye gazing, and audio-video cues), and structural and functional (MRI, EEG, and ECG) images. The research demonstrated strong diagnostic performance with models attaining accuracy rates ranging from 90% to 96% across diverse datasets. Classification measures, such as accuracy, sensitivity, specificity, precision, recall, and F1-score, were used in the performance evaluation. Error and statistical metrics, such as RMSE, MSE, R2, Kappa, and G-mean, were also employed. The dependability and efficiency of the models in detecting ASD were enhanced by validation methods, such as confusion matrix, receiver operating characteristics (ROC) curves, and AUC. Based on the evaluated studies, transfer learning with diverse datasets and modalities has great promise for early ASD diagnosis. Even with a minimal data size, these techniques increase robustness, accuracy, and generalization. For real-time clinical applications, a hybrid transfer learning-based framework is advised to assist clinicians and therapists in accurately diagnosing and assessing ASD severity.
Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by diverse behavioral, cognitive, sensory, and communication profiles, making early diagnosis and personalized intervention challenging. Recent advances in machine learning (ML) and deep learning (DL) have enabled the development of computational tools for ASD screening, classification, severity assessment, and intervention monitoring. This review synthesizes findings from 50 recent studies that applied ML and DL techniques to ASD-related datasets, including electroencephalography (EEG), eye-tracking, behavioral video, microbiome, voice acoustic, demographic, and multimodal data. The review addresses three key questions: (i) which data modalities and computational approaches are most frequently used, (ii) how diagnostic performance is evaluated across different study designs, and (iii) what methodological challenges limit clinical translation. The literature is organized according to data modality, algorithmic approach, and clinical readiness. Approaches examined include conventional ML methods, convolutional neural networks, graph neural networks, hybrid deep learning architectures, federated learning, explainable artificial intelligence, topological data analysis, and multimodal fusion. The findings suggest that multimodal and graph-based approaches provide a more comprehensive representation of ASD phenotypes than single-modality methods. Explainability and privacy-preserving learning have also emerged as important considerations for clinical deployment. However, many reported high-performance models are based on small sample sizes, repeated use of the ABIDE dataset, class imbalance, single-site validation, or limited external testing, raising concerns regarding generalizability. Beyond diagnostic accuracy, this review evaluates model interpretability, calibration, scalability, validation rigor, and clinical applicability. Overall, the analysis highlights the need for standardized benchmarks, externally validated multimodal datasets, clinically relevant evaluation metrics, and decision-support systems that complement rather than replace expert clinical assessment in ASD diagnosis and management.
S. K, Lakshmi Annapurna Y· Journal of Visualized Experi...· 0 citations
Autism Spectrum Disorder (ASD) is a neurological and developmental condition characterized by challenges in social interaction, communication (both verbal and non-verbal), and repetitive behaviours. While genetics play a key role in its onset, early diagnosis remains essential for effective intervention. Machine learning (ML) offers a promising approach to streamline and accelerate ASD detection, making it faster and more cost-effective than traditional methods. This paper evaluates eight classification models to identify key ASD features and automate diagnosis. We compare their performance on large datasets to enhance predictive accuracy. ML has transformed healthcare by leveraging vast data volumes for analysis, with technological advances over the past decade improving diagnostic tools now standard in medical settings. ASD affects individuals variably, with symptoms typically appearing between 18 months and 3 years. Although genetic and environmental factors contribute, no single cause is confirmed. Traditional screenings rely heavily on clinician expertise, involving manual assessments and scoring, which can be subjective and time-consuming—even experts face uncertainties in predicting onset or severity. Parents seek rapid, reliable results. ML and deep learning (DL) address these gaps by analyzing complex patterns in data, enabling early prediction of ASD and its severity. This study implements diverse algorithms to support precise, automated screening, reducing diagnostic delays and improving outcomes.
Devireddy Mamatha, K. Maheswari· 2026 6th International Confe...· 0 citations
Early diagnosis of autism spectrum disorder (ASD) plays a crucial role in facilitating prompt interventions and assessing post-therapy progress, both of which can greatly improve developmental trajectories. The emergence of artificial intelligence—particularly deep learning—has opened new possibilities for clinicians to detect ASD with improved precision and speed. This study investigates the utilization of deep learning models to recognize ASD among 13-year-old children based on eye movement data collected as participants observed static images and short video sequences. The dataset included visual and numerical variables, such as gaze position and pupil diameter, allowing for a multimodal analytical approach. For the numerical dataset, a multilayer perceptron (MLP) neural network produced the best outcomes, yielding an accuracy of 91.7% and a recall rate of 83.3% in ASD classification. Meanwhile, the Vision Transformer (ViT) model performed best for image-based analysis, reaching an accuracy of 78.2% and a recall rate of 88.6%. Overall, the findings highlight the potential of deep learning frameworks as objective, data-driven tools for ASD detection in both clinical and research contexts.Key words: Autism, Computer Vision, Deep Learning Model, Vision Transformer.
Muhamad Syukron, R. Faresta· Jurnal Ilmiah Kursor· 0 citations
A comparative analysis of recent approaches for early ASD prediction, focusing on methodologies, datasets, predictive performance, and key limitations indicates that deep learning-based approaches, particularly those using facial images and behavioral video analysis, generally achieve higher accuracy than traditional machine learning models.
Gaurangkumar Patel, H.B. Pandya, B. Trivedi· SPU - Journal of Science, Te...· 0 citations
The need to develop large, well‐balanced datasets, the application of explainable AI techniques, standardization and regulatory guidelines for facilitating the clinical translation of ASD detection systems are suggested.
Anupama N, Chandrashekar M Patil· International Journal of Dev...· 0 citations
Early detection of autism spectrum disorder (ASD) is essential for timely intervention. This study presents a hybrid artificial intelligence framework for non-invasive ASD pre-screening using children’s coloring, drawing, and handwriting activities. The proposed framework combines deep convolutional neural networks (VGG16, ResNet50, and EfficientNetB0) as feature extractors with a support vector machine (SVM) classifier to distinguish four diagnostic categories: non-ASD, mild ASD, moderate ASD, and severe ASD. Experimental results demonstrate task-specific performance across architectures. ResNet50–SVM achieved perfect classification for coloring tasks, with 100% accuracy, precision, recall, and F1-score. VGG16–SVM performed best for drawing, achieving 88% accuracy and recall, 89% precision, and an F1-score of 87%. EfficientNetB0–SVM produced the highest handwriting performance, achieving 96% across all evaluation metrics. These findings demonstrate the potential of computer vision-based analysis of children’s expressive activities as an effective, non-invasive ASD pre-screening tool. Future work will focus on expanding dataset diversity and integrating multimodal behavioral cues to improve model generalization and clinical applicability.
Aina Khairina Ahmad Khair, Wan Mohd Yaakob Wan Bejuri, Mohd Murtadha Mohamad et al.· Bulletin of Electrical Engin...· 0 citations