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Licensed Unlicensed Requires Authentication Published by De Gruyter September 8, 2023

Automated diagnosis of autism with artificial intelligence: State of the art

  • Amir Valizadeh ORCID logo , Mana Moassefi ORCID logo , Amin Nakhostin-Ansari ORCID logo , Soheil Heidari Some’eh ORCID logo , Hossein Hosseini-Asl ORCID logo , Mehrnush Saghab Torbati ORCID logo , Reyhaneh Aghajani ORCID logo , Zahra Maleki Ghorbani ORCID logo , Iman Menbari-Oskouie ORCID logo , Faezeh Aghajani ORCID logo , Alireza Mirzamohamadi ORCID logo , Mohammad Ghafouri ORCID logo , Shahriar Faghani ORCID logo and Amir Hossein Memari ORCID logo EMAIL logo

Abstract

Autism spectrum disorder (ASD) represents a panel of conditions that begin during the developmental period and result in impairments of personal, social, academic, or occupational functioning. Early diagnosis is directly related to a better prognosis. Unfortunately, the diagnosis of ASD requires a long and exhausting subjective process. We aimed to review the state of the art for automated autism diagnosis and recognition in this research. In February 2022, we searched multiple databases and sources of gray literature for eligible studies. We used an adapted version of the QUADAS-2 tool to assess the risk of bias in the studies. A brief report of the methods and results of each study is presented. Data were synthesized for each modality separately using the Split Component Synthesis (SCS) method. We assessed heterogeneity using the I 2 statistics and evaluated publication bias using trim and fill tests combined with ln DOR. Confidence in cumulative evidence was assessed using the GRADE approach for diagnostic studies. We included 344 studies from 186,020 participants (51,129 are estimated to be unique) for nine different modalities in this review, from which 232 reported sufficient data for meta-analysis. The area under the curve was in the range of 0.71–0.90 for all the modalities. The studies on EEG data provided the best accuracy, with the area under the curve ranging between 0.85 and 0.93. We found that the literature is rife with bias and methodological/reporting flaws. Recommendations are provided for future research to provide better studies and fill in the current knowledge gaps.


Corresponding author: Amir Hossein Memari, Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, District 6, Gisha Bridge, Jalal-e-Al-e-Ahmad Hwy, No. 7, PO: 14395578, Tehran, Iran, E-mail:

  1. Research ethics: Not applicable.

  2. Author contributions: Coordination of the review: AV, MM, ANA, AHM. Designing the study: AV, MM, ANA, SHS, IMO, FA, AHM. Developing the protocol: AV, MM, ANA, AHM. Performing the search: AV, ZMG, AM, MG. Study selection: AV, MM, RA, SHH, MST, ZMG. Data extraction: AV, RA, SHH, MST, ZMG. Assessing the risk of bias in included studies: AV, MM, SHH, MST, RA, ZMG, SF. Analysis of data: AV. Interpretation of the results: AV, MM, AHM. Assessing the confidence in cumulative evidence: AV, SHH, MST, RA, ZMG. Writing the review: AV, MM, ANA, SHS, IMO, FA, AHM. Correspondence: AHM. The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: To access the data of the studies, contact their respective authors. Review data are available as appendix files.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/revneuro-2023-0050).


Received: 2023-04-26
Accepted: 2023-07-28
Published Online: 2023-09-08
Published in Print: 2024-02-26

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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