Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review.
Ahmad Chaddad, Jiali Li, Qizong Lu, Yujie Li, Idowu Paul Okuwobi, Camel Tanougast, Christian Desrosiers, Tamim Niazi
Author Information
Ahmad Chaddad: School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China. ORCID
Jiali Li: School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China.
Qizong Lu: School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China.
Yujie Li: School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China.
Idowu Paul Okuwobi: School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China.
Camel Tanougast: Laboratoire de Conception, Optimisation et Modélisation des Systèmes, University of Lorraine, 57070 Metz, France. ORCID
Christian Desrosiers: The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure (ETS), Montreal, QC H3C 1K3, Canada.
Tamim Niazi: Lady Davis Institute for Medical Research, McGill University, Montreal, QC H3T 1E2, Canada.
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 such as 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 the 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.
