At the 18-month well-child visit, pediatricians are supposed to do something remarkably difficult: detect, in a few minutes of observation, whether a toddler’s social and communicative development is veering off course. The American Academy of Pediatrics recommends universal autism-specific screening at 18 and 24 months, alongside general developmental screening, precisely because early intervention works best when it starts early. Yet the average age of autism diagnosis in the United States remains above four years, a gap that squanders months or years of critical developmental plasticity. A new study published in the Journal of Autism and Developmental Disorders argues that the missing ingredient may have been sitting in the exam room all along: the parent, armed with a smarter questionnaire.
The research team, led by Raymond Sturner of the Johns Hopkins University School of Medicine and the Center for Promotion of Child Development through Primary Care, set out to build an optimal algorithm of parent-administered items for detecting both autism and general developmental delay at the 18-month visit. Their starting point was a candid assessment of why existing screens fall short. The Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-CHAT-R/F), the most widely used autism screener, has documented weaknesses at this young age. Its positive predictive value drops sharply in younger toddlers, with community samples reporting values as low as .28 to .36 at around 18 months compared with .61 to .69 in older children. The required follow-up interview, designed to rescue that predictive value, is rarely completed in real primary care, and even research studies apply it inconsistently.
There is a deeper structural problem, too. Validation studies of the M-CHAT-R/F have generally not included diagnostic evaluations of representative samples of children who passed the screen, which makes honest estimates of sensitivity and specificity nearly impossible. Prior work by the same team, drawing on a large community sample that included matched screen-negative children, found the sensitivity of the M-CHAT-R/F to be just .36, well below the Q-CHAT-10-O, an ordinally scored version of the Quantitative Checklist for Autism in Toddlers, which reached .63 with half the items and no follow-up interview. No autism screen for the 18-month age group has ever achieved the generally recommended performance benchmark of greater than .70 for both sensitivity and specificity in community samples, for autism or for developmental delay.
The new study took an unusually ambitious approach to item selection. Rather than writing questions from scratch, the researchers curated candidate items from the most commonly used autism screens, including the M-CHAT-R/F, the Parent’s Observation of Social Interaction, and the Q-CHAT-10-O, and supplemented them with items drawn from longitudinal studies of children at elevated likelihood for autism: the First Year Inventory developed at the University of North Carolina and the Parent Observation of Early Markers Scales from Brock University. They also included expressive vocabulary items from the MacArthur-Bates Communicative Development Inventory (MCDI), because prior work had shown that vocabulary production is a powerful contributor to autism prediction around 18 months, even though expressive language delay is not itself a DSM-5 diagnostic criterion for autism.
The scale of the community data collection was substantial. Parents of 11,878 children aged 16 to 20 months, recruited from research-enrolled community pediatric offices in Maryland, Massachusetts, and North Carolina, completed the M-CHAT-R, the Q-CHAT-10, and the age-appropriate Ages and Stages Questionnaires-3 through an online screening system before scheduled 18-month visits. From the 787 children with any positive screen, plus matched controls who passed both screens, the team enrolled families willing to undergo full diagnostic testing. After exclusions and attrition, the final sample comprised 408 children with complete data on key measures and confirmed case status. Diagnostic evaluations used the ADOS-2 Toddler Module administered by testers blind to screening results, together with the Mullen Scales of Early Learning. Developmental delay was defined using criteria that mirror the thresholds most states apply for early intervention eligibility.
The analytical machinery was equally distinctive. The team fitted the MCDI Words and Sentences vocabulary data, obtained from the open Wordbank database, to a Rasch measurement model in which individual words carry difficulty scores on the same logit scale as toddler ability estimates. That allowed them to build a computerized adaptive testing version of the vocabulary checklist, in which each word presented depends on the respondent’s previous answers, cutting response burden while simulation studies confirmed the short form retained high person reliability of .98 and person separation of 6.78. For classification, they applied gradient-boosted tree modeling with feature selection via the Boruta method and Shapley values, plus Bayesian hyperparameter optimization. To guard against machine learning capitalizing on chance associations, models were trained on a synthetic dataset of 25,000 cases generated from roughly half the authentic data, validated on a synthetic set of 12,500 cases, and then evaluated on an authentic holdback sample of 202 children.
The resulting instrument, which the authors call the Toddler Autism and Developmental Adaptive Screen, distills the candidate pool down to just 19 features. The final model is anchored by expressive vocabulary percentile and items representing joint attention, the cluster of behaviors, pointing to share interest, following a point, showing objects just to share, and shifting eye gaze to check a parent’s reaction, that decades of prospective sibling studies have identified as the earliest and most consistent emerging signs of autism. Other retained items probe pretend play, unusual finger movements, sensitivity to noise, and imitation of household activities. Notably, the machine learning process retained some redundant content in multiple wordings from different source instruments, which the authors interpret as reflecting both the clinical importance of those topics and the value of asking things in several ways to ensure accurate parent report.
The performance numbers are the headline. On the authentic holdback sample, the new model’s sensitivity for autism reached .77, compared with .36 for the M-CHAT-R/F administered to the same children, roughly a doubling of detection rate. It also outperformed the Q-CHAT-10-O (.77 versus .63) in this 16-to-20-month age group. For developmental delay, the screen achieved sensitivity and specificity both above .70, with a positive predictive value of .60, and proved twice as sensitive as the standard ASQ-3 general developmental screen (.73 versus .39) against state early intervention eligibility criteria. The authors report that the tool appears to be the only screen for this age with generally recommended performance, over .70 on both sensitivity and specificity, for both autism and developmental delay simultaneously. Positive predictive value for autism, at .37, was not significantly higher than the M-CHAT-R/F’s, a reminder that no screening tool escapes the base-rate problem entirely.
One of the most conceptually interesting findings concerns how the same items behave differently depending on the outcome being predicted. Twelve items were identified for detecting developmental delay, half of them overlapping with the autism item set, yet none are weighted the same way for the two purposes, and one item about favorite activities even flips its meaning, with reading books weighting toward delay and lining up objects weighting toward autism. Rather than relying on simple cut scores, the model weights each feature in combination with the others, mirroring the way autism is clinically defined as a syndrome of features rather than a single deficit. The authors emphasize that the instrument is intended as a primary screening tool, not a diagnostic one, producing a pass or fail for autism, a separate pass or fail for developmental delay, and an expressive vocabulary percentile for age.
Practical advantages could matter as much as raw accuracy. The entire screen consists of 18 standard items plus an adaptive vocabulary measure capped at 25 words, where the current standard practice of administering both the M-CHAT-R and the ASQ-3 requires 50 caregiver items plus a follow-up interview that often never happens. No follow-up interview is needed, results can integrate with electronic health records, and smartphone administration makes the approach feasible even in low-income settings. The authors are candid about limitations: the sample over-represented highly educated parents, attrition affected both screen-positive and screen-negative groups, children exposed to English less than half the time were excluded, and not every child with autism is detectable at 18 months, so continued surveillance at older ages remains essential. The assembled tool is now being tested in a larger, more socioeconomically diverse community sample. If those results replicate, the humble parent questionnaire, retooled with item response theory and machine learning, could become one of the most consequential screening advances in early child development.
Subject of Research: Machine learning-based parent-report screening for autism and developmental delay at the 18-month pediatric visit
Article Title: Exploring the Potential of Parent Report for Autism/Developmental Screening at the 18-Month Visit
Article References: Exploring the Potential of Parent Report for Autism/Developmental Screening at the 18-Month Visit. (n.d.). https://doi.org/10.1007/s10803-026-07472-4
Image Credits: AI Generated
DOI: 10.1007/s10803-026-07472-4
Keywords: autism screening, developmental delay, M-CHAT, 18-month visit, parent report, machine learning, early intervention, joint attention, expressive vocabulary, pediatrics, computerized adaptive testing, Q-CHAT
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Teresa Odom. (September 30, 2026). Machine Learning Boosts Parent-Report Autism Screening at 18-Month Checkups. Scienmag. https://scienmag.com/machine-learning-boosts-parent-report-autism-screening-at-18-month-checkups/
Teresa Odom. “Machine Learning Boosts Parent-Report Autism Screening at 18-Month Checkups.” Scienmag, 30 September 2026, https://scienmag.com/machine-learning-boosts-parent-report-autism-screening-at-18-month-checkups/. Accessed 30 September 2026.
Teresa Odom. “Machine Learning Boosts Parent-Report Autism Screening at 18-Month Checkups.” Scienmag. September 30, 2026. https://scienmag.com/machine-learning-boosts-parent-report-autism-screening-at-18-month-checkups/
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Tags: 18-month visitalgorithm-based screening toolsautism diagnosis age in the U.S.autism screeningautism screening at 18 monthscomputerized adaptive testingdevelopmental delaydevelopmental delay detectionearly autism detectionEarly interventionearly intervention for autismexpressive vocabularyjoint attentionM-CHATM-CHAT-R/F limitationsMachine learningmachine learning in pediatric assessmentparent-reportparent-report autism screeningpediatrician autism screening challengespediatricsQ-CHATrole of parents in developmental assessmentsuniversal autism screening guidelines
