ArticlePloS one2025
The potential of evaluating shape drawing using machine learning for predicting high autistic traits.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Factors Associated With Professionals' Context-Specific Trust in AI-Based Screening for Autism Spectrum Disorder: Cross-Sectional Survey Study.JMIR formative research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChildren with high autistic traits often exhibit deficits in drawing, an important skill for social adaptability. Machine learning is a powerful technique for learning predictive models from movement data, so drawing processes and product characteristics can be objectively evaluated. This study aimed to assess the potential of evaluating shape drawing using machine learning to predict high autistic traits.
methodSeventy boys (5.03 ± 0.16) and 63 girls (5.06 ± 0.18) from the general population participated in the study. Participants were asked to draw shapes in the following order: equilateral triangle, inverted equilateral triangle, square, and the sun. A model for classifying participants as likely to have high autistic traits was developed using a support vector machine algorithm with a linear kernel utilizing 16 variables. A 16-inch liquid crystal display pen tablet was used to acquire data on hand-finger fine motor activity while the participants drew each shape. The X and Y coordinates of the pen tip, pen pressure, pen orientation, pen tilt, and eye movements were recorded to determine whether the participants had any problems with this skill. Eye movements were assessed using a webcam. These data and eye movements were used to identify the variables for the support vector machine model. DATA AND
resultsFor each shape, a model support vector machine was created to classify the high and low autistic trait groups, with accuracy, sensitivity, and specificity all above 85%. The specificity values across all models were 100%. In the inverted equilateral triangle model, specificity, accuracy, and sensitivity values were 100%.
conclusionsThese results demonstrate the potential of assessing shape characteristics using machine learning to predict high levels of autistic traits. Future studies with a wider variety of shapes are warranted to establish further the potential efficacy of drawing skills for screening for autism spectrum conditions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.