Accuracy of machine learning algorithms for the diagnosis of autism spectrum disorder based on cerebral sMRI, rs-fMRI, and EEG: protocols for three systematic reviews and meta-analyses

Objective: To determine the diagnostic accuracy of the applied machine learning algorithms for the diagnosis of autism spectrum disorder (ASD) based on structural magnetic resonance imaging (sMRI), resting-state functional MRI (rs-fMRI), and electroencephalography (EEG). Methods: We will include cross-sectional studies (both single-gates and two-gates) that have evaluated the diagnostic accuracy of machine learning algorithms on the sMRI data of ASD patients regardless of age, sex, and ethnicity. On the 22nd of May 2021, we searched Embase, MEDLINE, APA PsycINFO, IEEE Xplore, Scopus, and Web of Science for eligible studies. We also searched grey literature within various sources. We will use an adapted version of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess the risk of bias and applicability. Data will be synthesized using the relatively new Split Component Synthesis (SCS) method. We plan to assess heterogeneity using the I2 statistics and assess publication bias using trim and fill tests combined with ln DOR. Certainty of evidence will be assessed using the GRADE approach for diagnostic studies. Funding: These studies are funded by Sports Medicine Research Center, Tehran, Iran. Registration: PROSPERO submission IDs: 262575, 262825, and 262831.