{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T20:04:04Z","timestamp":1790193844598,"version":"4.1.0"},"reference-count":31,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Serious Games"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n In this cross-sectional diagnostic accuracy study, 275 children and adolescents aged 6\u201018 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\u00d75 folds). For stage 2, item selection and imputation were performed within each training fold. Explainability used Shapley Additive Explanations (SHAP). Significance was set at \u03b1=.05 (2-sided) with bootstrap 95% CIs.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n 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\u20100.953). At a threshold of 0.200, sensitivity was 96.1% (49\/51; 95% CI 86.8%\u201099.5%) and specificity was 58.9% (56\/95; 95% CI 48.4%\u201068.9%), 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\u20100.74), 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\n P<\/jats:italic>\n <.01) and no evidence of sex bias.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n 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.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/102714","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T19:29:12Z","timestamp":1790191752000},"page":"e102714-e102714","source":"Crossref","is-referenced-by-count":0,"title":["Two-Stage Gamified Digital Assessment for Autism Spectrum Disorder Screening and the Limits of Differentiating Social Communication Disorder in Children and Adolescents: Cross-Sectional Diagnostic Accuracy Study Using Explainable Machine Learning"],"prefix":"10.2196","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9188-0549","authenticated-orcid":false,"given":"Minyoung","family":"Jung","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6472-2474","authenticated-orcid":false,"given":"Ju","family":"Ran","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2063-4017","authenticated-orcid":false,"given":"Ennyoung","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Youngkyung","family":"Sunwoo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4056-800X","authenticated-orcid":false,"given":"SooYeon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8132-2359","authenticated-orcid":false,"given":"Ji-Hoon","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9673-6976","authenticated-orcid":false,"given":"Sungja","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"issue":"2","key":"R1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.15585\/mmwr.ss7202a1","article-title":"Prevalence and characteristics of autism spectrum disorder among children aged 8 years - Autism and Developmental Disabilities Monitoring Network, 11 sites, United States, 2020","volume":"72","author":"Maenner","journal-title":"MMWR Surveill Summ"},{"issue":"4","key":"R2","doi-asserted-by":"publisher","first-page":"862","DOI":"10.1177\/1362361320971107","article-title":"Age at autism spectrum disorder diagnosis: a systematic review and meta-analysis from 2012 to 2019","volume":"25","author":"van \u2019t Hof","journal-title":"Autism"},{"issue":"8","key":"R3","doi-asserted-by":"publisher","first-page":"2686","DOI":"10.1007\/s10803-020-04737-4","article-title":"A preliminary epidemiologic study of social (pragmatic) communication disorder relative to autism spectrum disorder and developmental disability without social communication deficits","volume":"51","author":"Ellis Weismer","journal-title":"J Autism Dev Disord"},{"issue":"4","key":"R4","doi-asserted-by":"publisher","DOI":"10.1002\/aur.70196","article-title":"How screening and diagnostic tools shape autism prevalence in school-aged children: a bibliometric-systematic review (2015-2025)","volume":"19","author":"Che Daud","journal-title":"Autism Res"},{"key":"R5","unstructured":"Constantino JN . 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