AI-based non-invasive imaging technologies for early autism spectrum disorder diagnosis: A short review and future directions
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 enhance...
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| Published in | Artificial intelligence in medicine Vol. 161; p. 103074 |
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| Main Authors | , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
01.03.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0933-3657 1873-2860 1873-2860 |
| DOI | 10.1016/j.artmed.2025.103074 |
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| 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. |
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| AbstractList | 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.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. 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. |
| ArticleNumber | 103074 |
| Author | Shehata, Mohamed Saleh, Gehad A. Barnes, Gregory Weafer, Kate Khudri, Mohamed Elnakib, Ahmed Abdelrahim, Mostafa Batouty, Nihal M. El-Baz, Ayman Contractor, Sohail Khalil, Ashraf Ghazal, Mohammed |
| Author_xml | – sequence: 1 givenname: Mostafa surname: Abdelrahim fullname: Abdelrahim, Mostafa organization: Bioengineering Department, University of Louisville, Louisville, KY 40292, USA – sequence: 2 givenname: Mohamed surname: Khudri fullname: Khudri, Mohamed organization: Bioengineering Department, University of Louisville, Louisville, KY 40292, USA – sequence: 3 givenname: Ahmed surname: Elnakib fullname: Elnakib, Ahmed organization: School of Engineering, Penn State Erie-The Behrend College, Erie, PA 16563, USA – sequence: 4 givenname: Mohamed surname: Shehata fullname: Shehata, Mohamed organization: Bioengineering Department, University of Louisville, Louisville, KY 40292, USA – sequence: 5 givenname: Kate surname: Weafer fullname: Weafer, Kate organization: Neuroscience Program, Departments of Biology and Psychology, Bellarmine University, Louisville, KY, USA – sequence: 6 givenname: Ashraf surname: Khalil fullname: Khalil, Ashraf organization: Zayed University, Abu Dhabi, United Arab Emirates – sequence: 7 givenname: Gehad A. surname: Saleh fullname: Saleh, Gehad A. organization: Diagnostic and Interventional Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt – sequence: 8 givenname: Nihal M. surname: Batouty fullname: Batouty, Nihal M. organization: Diagnostic and Interventional Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt – sequence: 9 givenname: Mohammed surname: Ghazal fullname: Ghazal, Mohammed organization: Electrical, Computer and Biomedical Engineering Department, Abu Dhabi University, 59911 Abu Dhabi, United Arab Emirates – sequence: 10 givenname: Sohail surname: Contractor fullname: Contractor, Sohail organization: Department of Radiology, University of Louisville, Louisville, KY 40202, USA – sequence: 11 givenname: Gregory surname: Barnes fullname: Barnes, Gregory organization: Department of Neurology, Pediatric Research Institute, University of Louisville, Louisville, KY 40202, USA – sequence: 12 givenname: Ayman surname: El-Baz fullname: El-Baz, Ayman email: ayman.elbaz@louisville.edu organization: Bioengineering Department, University of Louisville, Louisville, KY 40292, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39919468$$D View this record in MEDLINE/PubMed |
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| Title | AI-based non-invasive imaging technologies for early autism spectrum disorder diagnosis: A short review and future directions |
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