Computer Vision News - March 2021

8 Overall, the best diagnostic accuracies were obtained using individual structural datasets (SCV, Ar, and WM), while the greatest effect sizes and significance levels were obtained from SCV and all datasets combined. The AUC was highest for SCV and all datasets showing that they confer the greatest diagnostic accuracy across the spectrum of Tis, as shown in the Fig. 5. Research Figure 4: The total index (TI), based on subcortical volume data (SCV), and the autism quotient (AQ) showed a moderate statistically significant correlation (F(1,42) = 15.140, p < 0.0005), with an R2 of 0.26. In this dataset, the Tis are based on MRI segmentation measures which shows how each brain is close to the “autism-likeness” . Positive values show that a brain is closer to an autism representation and using a cut-off of positive TI increases specificity and decreases sensitivity. The method was compared with a few different machine learning models to predict the diagnostic status of the left‐out individual, and the percentage of correct classifications for each method was calculated following 5000 iterations of training and testing. The correlation using SCV data with the regression line is shown in Fig. 4.