Diagnosing Autism Spectrum Disorder Without Expertise: A Pilot Study of 5- to 17-Year-Old Individuals Using Gazefinder

Abstract

Atypical eye gaze is an established clinical sign in the diagnosis of autism spectrum disorder (ASD). We propose a computerized diagnostic algorithm for ASD, applicable to children and adolescents aged between 5 and 17 years using Gazefinder, a system where a set of devices to capture eye gaze patterns and stimulus movie clips are equipped in a personal computer with a monitor. We enrolled 222 individuals aged 5-17 years at seven research facilities in Japan. Among them, we extracted 39 individuals with ASD without any comorbid neurodevelopmental abnormalities (ASD group), 102 typically developing individuals (TD group), and an independent sample of 24 individuals (the second control group). All participants underwent psychoneurological and diagnostic assessments, including the Autism Diagnostic Observation Schedule, second edition, and an examination with Gazefinder (2 min). To enhance the predictive validity, a best-fit diagnostic algorithm of computationally selected attributes originally extracted from Gazefinder was proposed. The inputs were classified automatically into either ASD or TD groups, based on the attribute values. We cross-validated the algorithm using the leave-one-out method in the ASD and TD groups and tested the predictability in the second control group. The best-fit algorithm showed an area under curve (AUC) of 0.84, and the sensitivity, specificity, and accuracy were 74, 80, and 78%, respectively. The AUC for the cross-validation was 0.74 and that for validation in the second control group was 0.91. We confirmed that the diagnostic performance of the best-fit algorithm is comparable to the diagnostic assessment tools for ASD.

Keywords: Gazefinder; Japan; adolescent; autism spectrum disorder; machine learning; school-age children.

Figure 1

Movie clips implemented with Gazefinder, and the AOIs. (A) The 12 movie clips. (B) 100 AOIs embedded in 12 movie clips.

Figure 2

Strategy for creating the best-fit algorithm.

Figure 3

ROC curves of the best-fit diagnostic algorithm* and the votes of the 141 LOO** algorithms. Solid line: ROC curve of the best-fit diagnostic algorithm (AUC = 0.84, sensitivity = 71%, specificity = 80%, accuracy = 78%), dotted line: ROC curve of the votes of the 141 LOO algorithms (AUC = 0.74, sensitivity = 65%, specificity = 70%, accuracy = 67%), thick line: ROC curve of the best-fit diagnostic algorithm in an independent sample (second control group: AUC = 0.91, sensitivity = 87%, specificity = 80%, accuracy = 88%). *The merged algorithm of the final AOI rate score algorithm for age <10 years, and the final AOI count score algorithm for 10 years and over. **Leave-one-out method to cross-validate the best-fit diagnostic algorithm.