Journal of the Korean Academy of Child and Adolescent Psychiatry : eISSN 2233-9183 / pISSN 1225-729X

Table. 2.

Table. 2.

Summary of studies using AI technology with novel observational data

 Author Sample size Mean age Date type MethodAUC (%)Sensitivity (%)Specificity (%)Accuracy (%)
Tariq et al. [48]116 (ASD) 46 (TD)4.10yrs (ASD) 2.11yrs (TD)Behavioral featuresADTree, SVM, LR, RK, LİSVM Sparse 5 feature LR classifier89-9290-1001.13-10094-100
Liu et al. [39]29(ASD) 29 (TD)7.9 yrs (ASD) 7.86 yrs (TD)Eye-trackingSVM89.6393.186.2188.51
Li et al. [43]14(ASD) 16 (TD)32 yrs (ASD) 29.31 yrs (TD)Hand movementNB, SVM, RF, DT-57.1-85.768.8-87.566.7-86.7
Anzulewicz et al. [44]37(ASD) 45 (TD)4.5 yrs (ASD) 4.7 yrs (TD)Hand movementRF, RGF88.1-93.276-8367-88-
Crippa et al. [45]15(ASD) 15 (TD)3.5yrs (ASD) 2.6yrs (TD)Upper-limb movementSVM-82.2-10089.1-93.884.9-96.7

ADTree: alternating decision tree, AI: artificial intelligence, ASD: autism spectrum disorder, AUC: area under the curve, DT: decision tree, LİSVM: linear support vector machine, LR: logistic regression, NB: naïve Bayes, RF: random forest, RGF: regularized greedy forest, RK: radial kernel, SVM: support vector machine, TD: typically developing, yrs: years

J Korean Acad Child Adolesc Psychiatry 2019;30:145-52
© 2019 J Korean Acad Child Adolesc Psychiatry