A Review on Early Detection and Prediction of Autism Spectrum Disorders using Deep Learning Techniques

The complicated neurological condition known as autism spectrum disorder (ASD) is characterized by repetitive behaviors and persistent challenges with social interaction. The results of interventions are greatly improved by early identification of ASD. A type of artificial intelligence called deep learning has shown promise in behavioral analysis, genetic data interpretation, and medical imaging, providing promising pathways for early ASD prediction. This study examines how convolutional neural networks (CNNs) and recurrent neural networks (RNNs), two recent developments in deep learning techniques, are used to detect ASD. It looks into deep learning frameworks’ clinical viability as well as datasets and methodology. Challenges including model interpretability, ethical issues, and data scarcity are also covered. The study’s conclusion highlights the necessity of cooperation between physicians and AI developers and suggests possible future possibilities.