Evidence map›Paper›PMID 35757211›Full record

ArticleFrontiers in psychiatry2022

Different Eye Tracking Patterns in Autism Spectrum Disorder in Toddler and Preschool Children.

Xue-Jun Kong, Zhen Wei, Binbin Sun, Yiheng Tu, Yiting Huang, Ming Cheng, Siyi Yu, Georgia Wilson, Joel Park, Zhe Feng and 3 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 3 pooled it
3.5field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 31 citations in OpenAlex.

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  13. [Machine learning algorithms for identifying autism spectrum disorder through eye-tracking in different intention videos].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 4 institutions in 2 countries.

Xue-Jun KongAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Zhen WeiDepartment of Child Psychiatry and Rehabilitation, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China.
Binbin SunDepartment of Child Psychiatry and Rehabilitation, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China.
Yiheng TuDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Yiting HuangDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Ming ChengDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Siyi YuDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Georgia WilsonDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Joel ParkDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Zhe FengDepartment of Child Psychiatry and Rehabilitation, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China.
Mark VangelDepartment of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Jian KongDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.
Guobin WanDepartment of Child Psychiatry and Rehabilitation, Shenzhen Maternity and Child Healthcare Hospital, Southern Medical University, Shenzhen, China.
Massachusetts General Hospital · USHarvard University · USShenzhen Maternity and Child Healthcare Hospital · CNSouthern Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Children with autism spectrum disorder (ASD) have been observed to be associated with fixation abnormality as measured eye tracking, but the dynamics behind fixation patterns across age remain unclear. Materials and Methods: In this study, we investigated gaze patterns between toddlers and preschoolers with and without ASD while they viewed video clips and still images (i.e., mouth-moving face, biological motion, mouthing face vs. moving object, still face picture vs. objects, and moving toys). Results: We found that the fixation time percentage of children with ASD showed significant decrease compared with that of TD children in almost all areas of interest (AOI) except for moving toy (helicopter). We also observed a diagnostic group (ASD vs. TD) and chronological age (Toddlers vs. preschooler) interaction for the eye AOI during the mouth-moving video clip. Support vector machine analysis showed that the classifier could discriminate ASD from TD in toddlers with an accuracy of 80% and could discriminate ASD from TD in preschoolers with an accuracy of 71%. Conclusion: Our results suggest that toddlers and preschoolers may be associated with both common and distinct fixation patterns. A combination of eye tracking and machine learning methods has the potential to shed light on the development of new early screening/diagnosis methods for ASD.

Indexed as

autismeye trackingmachine learningpercentage of fixation timepreschoolsupport vector machinetoddler

Identifiers

PMID35757211
PMCPMC9218189
OpenAlexW4285744856

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.