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

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.

Lipoprotein (a) as a predictor of diabetic retinopathy in patients with type 2 diabetes: A systematic review.

BACKGROUND: Lipoprotein a (LP(a)), an LDL-like lipoprotein, known as a risk factor for cardiovascular diseases, has a controversial association with diabetic retinopathy in patients with type 2 diabetes-the current systematic review aimed to critically assess the association between LP(a) and diabetic retinopathy. METHODS: A systematic review of relevant studies was conducted after a thorough search in PubMed, Scopus, and Google Scholar electronic databases. We used English observational, case-control, and prospective cohort studies published up to August 2022, including type 2 diabetic patients as the population, diabetic retinopathy as the outcome, and LP(a) as the intervention. RESULT: 17 relevant studies, including 4688 patients with diabetes, were included in this systematic review. While in 13 studies, Lipoprotein(a) was recognized as a risk factor for diabetic retinopathy, only three studies reported no evidence of a relationship between the two. Also, another study showed a mixed outcome of the relationship between LP(a) and diabetic retinopathy. CONCLUSION: High serum lipoprotein(a) in patients with type 2 diabetes is considered a risk factor for diabetic retinopathy. However, further large-scaled cohort studies are still required to validate this finding.

Abstract screening using the automated tool Rayyan: results of effectiveness in three diagnostic test accuracy systematic reviews.

OBJECTIVE: To evaluate the performance of the automated abstract screening tool Rayyan. METHODS: The records obtained from the search for three systematic reviews were manually screened in four stages. At the end of each stage, Rayyan was used to predict the eligibility score for the remaining records. At two different thresholds (≤2.5 and < 2.5 for exclusion of a record) Rayyan-generated ratings were compared with the decisions made by human reviewers in the manual screening process and the tool's accuracy metrics were calculated. RESULTS: Two thousand fifty-four records were screened manually, of which 379 were judged to be eligible for full-text assessment, and 112 were eventually included in the final review. For finding records eligible for full-text assessment, at the threshold of < 2.5 for exclusion, Rayyan managed to achieve sensitivity values of 97-99% with specificity values of 19-58%, while at the threshold of ≤2.5 for exclusion it had a specificity of 100% with sensitivity values of 1-29%. For the task of finding eligible reports for inclusion in the final review, almost similar results were obtained. DISCUSSION: At the threshold of < 2.5 for exclusion, Rayyan managed to be a reliable tool for excluding ineligible records, but it was not much reliable for finding eligible records. We emphasize that this study was conducted on diagnostic test accuracy reviews, which are more difficult to screen due to inconsistent terminology.

Disease-modifying therapies and T1 hypointense lesions in patients with multiple sclerosis: A systematic review and meta-analysis.

BACKGROUND: Previous research has shown that cerebral T1 hypointense lesions are positively correlated with the disability of multiple sclerosis (MS) patients. Hence, they could be used as an objective marker for evaluating the progression of the disease. Up to this date, there has not been a systematic evaluation of the effects of disease-modifying therapies (DMTs) on this prognostic marker. OBJECTIVES: To evaluate the effects of FDA-approved DMTs on the numbers and volume of T1 hypointense lesions in adult patients with MS. METHODS: We included studies with the mentioned desired outcomes. In March 2021, we searched MEDLINE (Ovid), Embase, and CENTRAL to find relevant studies. All included studies were assessed for the risk of bias using the RoB-2 tool. Extracted data were analyzed using a random-effects model. Certainty of evidence was assessed using GRADE. RESULTS: Thirteen studies with 7484 participants were included. Meta-analysis revealed the mean difference between the intervention and comparator groups for the number of lesions was -1.3 (95% CI: -2.1, -0.5) and for the mean volume of lesions was -363.1 (95% CI: -611.6, -114.6). Certainty of evidence was judged to be moderate. Heterogeneity was considerable. DISCUSSION: DMTs reduce the number and volume of T1 hypointense lesions. Although, these findings must be interpreted cautiously due to the high values of heterogeneity.