Authors
Minyoung Jung, Ju Ran, Ennyoung Lee, Youngkyung Sunwoo, SooYeon Kim, Ji-Hoon Kim, Sungja Cho
Published in
JMIR serious games. Volume 14. Pages e102714. Sep 23, 2026. Epub Sep 23, 2026.
Abstract
Distinguishing autism spectrum disorder (ASD) from social communication disorder (SCD) is clinically challenging because both conditions present with overlapping social communication deficits. Standard caregiver-reported instruments capture surface-level behavioral similarities rather than underlying cognitive differences, motivating the development of digital gamified assessments that measure social cognitive processes directly.
This study developed and evaluated a 2-stage gamified digital pipeline: stage 1 (Buddy Plan, a self-report module) for high-sensitivity ASD screening, and stage 2 (Buddy Drill, story-based social-judgment scenarios), which was examined with a leakage-controlled analysis, for assessing whether ASD can be differentiated from SCD.
In this cross-sectional diagnostic accuracy study, 275 children and adolescents aged 6-18 years (mean 11.07, SD 3.13 years; 175/275, 63.6% male) were recruited by convenience sampling from 5 clinical and community sites in the Republic of Korea (May 2024 to February 2025) across 5 Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) groups: ASD (n=51), SCD (n=54), attention-deficit/hyperactivity disorder (n=23), high risk (n=52), and neurotypically developing (ND; n=95). Diagnoses were established by board-certified child psychiatrists. Participants completed 2 tablet-based modules: Buddy Plan (52 self-report items; stage 1) and Buddy Drill (153 story-based scenarios; stage 2). The primary outcome was diagnostic accuracy (area under the receiver operating characteristic curve [AUC], sensitivity, and specificity). Four machine learning algorithms were trained with nested cross-validation (5×5 folds). For stage 2, item selection and imputation were performed within each training fold. Explainability used Shapley Additive Explanations (SHAP). Significance was set at α=.05 (2-sided) with bootstrap 95% CIs.
Group differences were tested by 1-way ANOVA. For stage 1 (ASD vs ND; n=146), random forest achieved a nested AUC of 0.912 (95% CI 0.856-0.953). At a threshold of 0.200, sensitivity was 96.1% (49/51; 95% CI 86.8%-99.5%) and specificity was 58.9% (56/95; 95% CI 48.4%-68.9%), with 2 false negatives. For stage 2 (ASD vs SCD; n=100), the fully nested pipeline yielded only chance-level discrimination: regularized logistic regression achieved a nested AUC of 0.62 (95% CI 0.51-0.74), and no feature configuration (self-report: 0.55, objective: 0.62, combined: 0.63) exceeded chance. SHAP identified 5 cross-algorithm stage 1 biomarkers with significant ASD-versus-ND differences (all P<.01) and no evidence of sex bias.
The gamified Buddy Plan module shows promise for high-sensitivity ASD screening. In contrast, once feature-selection leakage was removed with a fully nested pipeline, the Buddy Drill module did not robustly differentiate ASD from SCD, and the apparent advantage of objective features over self-report features seen in leaky analyses did not persist. Because the results derive from internal cross-validation in a single, predominantly male Korean cohort without external validation or IQ matching, they represent preliminary evidence of screening feasibility rather than validated clinical differentiation. Prospective, externally validated, IQ- and language-matched studies are required.
PMID:
42777146
Bibliographic data and abstract were imported from PubMed on 24 Sep 2026.
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