Summary of Exploring Gaze Pattern Differences Between Asd and Td Children Using Internal Cluster Validity Indices, by Weiyan Shi et al.
Exploring Gaze Pattern Differences Between ASD and TD Children Using Internal Cluster Validity Indices
by Weiyan Shi, Haihong Zhang, Ruiqing Ding, YongWei Zhu, Wei Wang, Kenny Tsu Wei Choo
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores whether unsupervised clustering and internal cluster validity indices can distinguish Autism Spectrum Disorder (ASD) from typically developing (TD) children based on eye-tracking data. The study applies seven clustering algorithms to gaze points, extracts 63 internal cluster validity indices, and reveals correlations with ASD diagnosis. These indices are then used to train predictive models for ASD diagnosis, achieving high accuracy (81% AUC) across three datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how computers can tell apart children with Autism Spectrum Disorder (ASD) from those who don’t have it just by looking at where they look. It uses special computer programs that group together points on a map of what people are looking at, and then checks if these groups are different between kids with ASD and kids without it. The study finds that this method can correctly identify most kids as having ASD or not, which is important for helping doctors make accurate diagnoses. |
Keywords
» Artificial intelligence » Auc » Clustering » Tracking » Unsupervised
