Emerging Approaches for Early Diagnosis of Autism: A Comprehensive Survey of Machine, Deep and Transfer Learning Methods

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.