Can autism be diagnosed with AI?

TL;DR

This paper reviews the use of AI-driven radiomics, especially deep learning, for diagnosing Autism Spectrum Disorder, highlighting current techniques, challenges, and future research directions.

Contribution

It provides a comprehensive overview of radiomic methods for ASD diagnosis, emphasizing the potential of deep learning and identifying gaps for further validation.

Findings

Radiomic models based on brain morphology can predict ASD.

Deep learning techniques show promise but need more validation.

Current work is limited and requires further investigation.

Abstract

Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks like Autism Spectrum Disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and Healthy Control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.