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Importance: Stereotypical motor movements (SMMs) are a form of restricted and repetitive behavior, which is a core symptom of autism spectrum disorder (ASD). Current quantification of SMM severity is extremely limited, with studies relying on coarse and subjective caregiver reports or laborious manual annotation of short video recordings., Objective: To assess the utility of a new open-source AI algorithm that can analyze extensive video recordings of children and automatically identify segments with heterogeneous SMMs, thereby enabling their direct and objective quantification., Design, Setting, and Participants: This retrospective cohort study included 241 children (aged 1.4 to 8.0 years) with ASD. Video recordings of 319 behavioral assessments carried out at the Azrieli National Centre for Autism and Neurodevelopment Research in Israel between 2017 and 2021 were extracted. Behavioral assessments included cognitive, language, and autism diagnostic observation schedule, 2nd edition (ADOS-2) assessments. Data were analyzed from October 2020 to May 2024., Exposures: Each assessment was recorded with 2 to 4 cameras, yielding 580 hours of video footage. Within these extensive video recordings, manual annotators identified 7352 video segments containing heterogeneous SMMs performed by different children (21.14 hours of video)., Main Outcomes and Measures: A pose estimation algorithm was used to extract skeletal representations of all individuals in each video frame and was trained an object detection algorithm to identify the child in each video. The skeletal representation of the child was then used to train an SMM recognition algorithm using a 3 dimensional convolutional neural network. Data from 220 children were used for training and data from the remaining 21 children were used for testing., Results: Among 319 behavioral assessment recordings from 241 children (172 [78%] male; mean [SD] age, 3.97 [1.30] years), the algorithm accurately detected 92.53% (95% CI, 81.09%-95.10%) of manually annotated SMMs in our test data with 66.82% (95% CI, 55.28%-72.05%) precision. Overall number and duration of algorithm-identified SMMs per child were highly correlated with manually annotated number and duration of SMMs (r = 0.8; 95% CI, 0.67-0.93; P < .001; and r = 0.88; 95% CI, 0.74-0.96; P < .001, respectively)., Conclusions and Relevance: This study suggests the ability of an algorithm to identify a highly diverse range of SMMs and quantify them with high accuracy, enabling objective and direct estimation of SMM severity in individual children with ASD.

Importance: With the continuous increase in the prevalence of autistic spectrum disorder (ASD), effective early screening is crucial for initiating timely interventions and improving outcomes., Objective: To develop predictive models for ASD using routinely collected developmental surveillance data and to assess their performance in predicting ASD at different ages and in different clinical scenarios., Design, Setting, and Participants: This retrospective cohort study used nationwide data of developmental assessments conducted between January 1, 2014, and January 17, 2023, with minimal follow-up of 4 years and outcome collection in March 2023. Data were from a national program of approximately 1000 maternal child health clinics that perform routine developmental surveillance of children from birth to 6 years of age, serving 70% of children in Israel. The study included all children who were assessed at the maternal child health clinics (N = 1 187 397). Children were excluded if they were born at a gestational age of 33 weeks or earlier, had no record of gestational age, or were followed up for less than 4 years without an ASD outcome. The data set was partitioned at random into a development set (80% of the children) and a holdout evaluation set (20% of the children), both with the same prevalence of ASD outcome., Exposures: For each child, demographic and birth-related covariates were extracted, as were per-visit growth measurements, quantified developmental milestone assessments, and referral summary covariates. Only information that was available before the prediction age was used for training and evaluating the models., Main Outcome and Measure: The main outcome was eligibility for a governmental disabled child allowance due to ASD, according to administrative data of the National Insurance Institute of Israel. The performance of the models that predict the outcome was evaluated and compared with previous work on the Modified Checklist for Autism in Toddlers (M-CHAT)., Results: The study included 1 187 397 children (610 588 [51.4%] male). The performance of the ASD prediction models improved with prediction age, with fair accuracy already at 12 months of age. A model that combined longitudinal measures of developmental milestone assessments with a minimal set of demographic variables, which was applied at 18 to 24 months of age, achieved an area under the receiver operating characteristic curve of 0.83, with a sensitivity of 45.1% at a specificity of 95.0%. A model using single-visit assessments achieved an area under the receiver operating characteristic curve of 0.81 and a sensitivity of 41.2% at a specificity of 95.0%. The best performing prediction models surpassed the pooled performance of M-CHAT (sensitivity, 40%; specificity, 95%) reported in studies with a similar design., Conclusions and Relevance: This cohort study found that ASD can be predicted from routine developmental surveillance data at an accuracy surpassing M-CHAT screening. This tool may be seamlessly integrated in the clinical workflow to improve early identification of children who may benefit from timely interventions.

Importance: Routine developmental screening tests for children are used worldwide for early detection of developmental delays. However, assessment of developmental milestone norms lacks strong normative data, and there are inconsistencies among different screening tools., Objective: To establish milestone norms and build an updated developmental scale., Design, Setting, and Participants: This is a cross-sectional, population-based study conducted between 2014 and 2020. Developmental assessments were conducted by trained public health nurses, documented in national maternal child health clinics, known as Tipat Halav, which serve all children in Israel. Participants included all children born between January 2014 and September 2020, who were followed at the maternal child health clinics from birth to age 6 years. Exclusion criteria were preterm birth, missing gestational age, low birth weight (<2.5 kg), abnormal weight measurement (<3% according to standardized child growth charts), abnormal head circumference measurement (<3% or >97% according to standardized child growth charts), and visits without developmental data or without the child's age. Data analysis was performed from September 2020 to June 2021., Exposures: In total, 59 milestones in 4 developmental domains were evaluated, and the achievement rate per child's age was calculated for each milestone., Main Outcomes and Measures: A contemporary developmental scale, the Tipat Halav Israel Screening (THIS) Developmental Scale, was built, presenting the 75%, 90%, and 95% achievement rates for each milestone. The THIS scale was compared with other commonly used screening tests, including the Denver Developmental Screening Test II (Denver II), the Alberta Infant Motor Scale (AIMS), and the Centers for Disease Control and Prevention (CDC) Developmental Assessment., Results: A total of 839 574 children were followed in the maternal child health clinics between January 2014 and September 2020 in Israel, and 195 616 children were excluded. A total of 3 774 517 developmental assessments were performed for the remaining 643 958 children aged 0 to 6 years (319 562 female children [49.6%]), resulting in the establishment of new developmental norms. In terms of the comparable milestones, THIS milestones had a match of 18 of 27 (67%) with the Denver II, 7 of 7 (100%) with AIMS, and 10 of 19 (53%) with the CDC Developmental Assessment. The remaining unmatched milestones were achieved earlier in the THIS scale compared with other screening tools., Conclusions and Relevance: The THIS developmental scale is based on the largest population evaluated to date for developmental performance, representing the heterogeneous, multicultural population comprising this cohort. It is recommended for further evaluation worldwide.