Related Experiment Video
Updated: Jun 27, 2026
Probing the Brain in Autism Using fMRI and Diffusion Tensor Imaging
Published on: September 12, 2011
AI-based non-invasive imaging technologies for early autism spectrum disorder diagnosis: A short review and future
Mostafa Abdelrahim1, Mohamed Khudri1, Ahmed Elnakib2
1Bioengineering Department, University of Louisville, Louisville, KY 40292, USA.
Insights
Early detection of Autism Spectrum Disorder (ASD) is crucial. This review explores non-invasive imaging technologies, including structural MRI and AI, to improve early ASD diagnosis in infants and children.
Area of Science:
- Neurology
- Medical Imaging
- Artificial Intelligence
Background:
- Autism Spectrum Disorder (ASD) prevalence is increasing, highlighting the need for early detection.
- Timely ASD diagnosis is critical for effective early intervention and improved treatment outcomes.
- Current diagnostic methods require enhancement with objective, non-invasive tools for early identification.
Purpose of the Study:
- To review recent advancements in non-invasive technologies for early Autism Spectrum Disorder (ASD) diagnosis.
- To explore the role of structural MRI and imaging radiomics in identifying ASD.
- To discuss the necessity of brain cortex parcellation, available databases, and AI's contribution to diagnostic accuracy.
Main Methods:
- Review of current literature on non-invasive diagnostic technologies for ASD.
- Focus on structural Magnetic Resonance Imaging (MRI) techniques and imaging radiomics.
- Analysis of the role of artificial intelligence (AI) in enhancing diagnostic accuracy.
Main Results:
- Structural MRI and specific imaging radiomics show promise for early ASD detection.
- The necessity of brain cortex parcellation for improved diagnostic accuracy is under investigation.
- Artificial intelligence tools offer significant potential to enhance the accuracy of ASD diagnosis.
Conclusions:
- Non-invasive imaging technologies, particularly structural MRI with radiomics and AI, are vital for advancing early ASD diagnosis.
- Further research is needed to refine these tools and validate their efficacy in diverse populations.
- Developing accessible databases and standardized methodologies will accelerate progress in non-invasive ASD diagnostic technology.
Abstract:
Autism Spectrum Disorder (ASD) is a neurological condition, with recent statistics from the CDC indicating a rising prevalence of ASD diagnoses among infants and children. This trend emphasizes the critical importance of early detection, as timely diagnosis facilitates early intervention and enhances treatment outcomes. Consequently, there is an increasing urgency for research to develop innovative tools capable of accurately and objectively identifying ASD in its earliest stages. This paper offers a short overview of recent advancements in non-invasive technology for early ASD diagnosis, focusing on an imaging modality, structural MRI technique, which has shown promising results in early ASD diagnosis. This brief review aims to address several key questions: (i) Which imaging radiomics are associated with ASD? (ii) Is the parcellation step of the brain cortex necessary to improve the diagnostic accuracy of ASD? (iii) What databases are available to researchers interested in developing non-invasive technology for ASD? (iv) How can artificial intelligence tools contribute to improving the diagnostic accuracy of ASD? Finally, our review will highlight future trends in ASD diagnostic efforts.
Related Concept Videos
01:14Brain Imaging
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).
01:19Autism Spectrum Disorder
These core symptoms manifest differently among individuals, ranging from mild to severe. The disorder's complexity extends beyond its clinical presentation, encompassing a diverse range of biological, cognitive, and sociocultural influences.