Evidence map›Paper›PMID 41080772›Full record

ArticleFrontiers in psychiatry2025

Development and evaluation of robotic detection technology for assessing autism.

Wing-Chee So, Elsa Wong, Wingo Ng, Sally Lay, Wynes Wong, Ming-Ting So, Xiao-Han Lee, Yuen-Yung Lee

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Wing-Chee SoDepartment of Educational Psychology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Elsa WongNEC Hong Kong Limited, Hong Kong, Hong Kong SAR, China.
Wingo NgNEC Hong Kong Limited, Hong Kong, Hong Kong SAR, China.
Sally LayNEC Hong Kong Limited, Hong Kong, Hong Kong SAR, China.
Wynes WongNEC Hong Kong Limited, Hong Kong, Hong Kong SAR, China.
Ming-Ting SoDepartment of Educational Psychology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Xiao-Han LeeDepartment of Educational Psychology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Yuen-Yung LeeDepartment of Educational Psychology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: An objective and standardized assessment for assessing autism is needed. This study aimed to develop and validate robotic detection technology for assessing autism. The robot HUMANE, installed with computer vision and linked with face and motion recognition technology, autonomously detected atypical eye gaze and repetitive motor movements, two of the features of autism, while narrating stories. It autonomously prompted the child if they did not establish eye gaze with the robot or produced motor movements for five seconds continuously. Method: The study involved 119 children aged between three and six years old (M=4.53, SD=1.89; 38 females) and included children confirmed or not confirmed with autism. They all received the Autism Diagnostic Observation Schedule-second edition (ADOS‑2), the standard diagnostic tool for autism. HUMANE's detection performance - the number of robot prompts and the cumulative duration of inattentiveness/improper posture - was then evaluated against the calibrated severity score of ADOS-2. Results: Our results showed that the average sensitivity and specificity of the detection reached 0.80, the Diagnostic Odds Ratio was beyond 30, and the AUC was .85. Discussion: These results indicate that the robotic detection technology of atypical eye gaze and repetitive motor movements can contribute to the diagnostic process to identify the presence or absence of autism.

Indexed as

assessmentautismautonomous robotchildrendetection

Identifiers

PMID41080772
PMCPMC12512041

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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 OpenQuestion graph.