Early Detection
In collaboration with the KI Research Institute, we develop machine learning models to detect developmental delays early using data from routine screenings at Israel's MOH mother-child health clinics. This data covers development from birth to age 6, enabling timely interventions that improve long-term outcomes. Together with KI, we plan to evaluate the adaptability of these methods to different populations and health systems.
Standardization of a Developmental Milestone Scale Using Data From Children in Israel
Through our partnership with KI, we introduce a new data-driven developmental scale based on national assessments of a multicultural population. This collaborative cross-sectional, population-based study analyzed 3,774,517 developmental assessments of 643,958 children from birth to age 6 years, conducted by trained nurses in Israel's Maternal Child Health Clinics (Tipat Halav). The Tipat Halav Israel Screening Developmental Scale, developed jointly with KI, presents the 75%, 90%, and 95% achievement rates for evaluated milestones.
Sex-specific developmental scales for surveillence
Developing predictive algorithim for autism
Together with the KI Research Institute, we developed an automatic prediction model for early childhood autism screening based on longitudinal data from routine developmental surveillance. Our cohort study of 1.2 million children demonstrated that the prediction models achieved performance on par with the Modified Checklist for Autism in Toddlers (M-CHAT), a popular autism screening tool. This collaborative tool can be seamlessly integrated into clinical workflows to improve early identification of children who may benefit from timely interventions.
Preterm developmental scales for surveillance
In collaboration with KI, we examine a fundamental question: Is standard age-correction appropriate for all levels of prematurity? The developmental timeline of preterm children differs from their term-born counterparts. Current methods for prematurity correction assume linearity by age and uniformity across time and developmental domains. Using large-scale data, our joint research with KI aims to validate these assumptions.
Breast feeding as risk and resilience factor for developmental delays
In collaboration with KI, we investigate the impact of breastfeeding on developmental milestone attainment. While breastfeeding is known to have protective and beneficial effects on infants' health, our joint research examines whether it affects developmental milestones. In search of modifiable factors to promote healthy cognitive development, we analyze the association between breastfeeding (duration and exclusivity) and milestone attainment. Special focus is given to preterm children, who are prone to higher rates of neurodevelopmental challenges.
Creating a score for child development survaillance
In partnership with KI, we propose a new method for developmental monitoring. Unlike growth measures, development is traditionally assessed as pass/fail on age-appropriate milestones. Our collaborative approach integrates these assessments into a quantitative score, creating a developmental equivalent of growth curves. This simple, continuous scoring method reflects known risk factors for developmental delays and can reveal new ones.
Assessing the Attainment Rates of Updated CDC Milestones Using a New Israeli Developmental Scale
In partnership with KI, we compared the new Israeli evidence-based developmental scale with the updated CDC checklists. Our collaborative findings showed that Israeli children achieved almost all comparable milestones earlier than CDC-defined threshold ages across all domains. This research demonstrates that population-specific, evidence-based developmental scales can enable more personalized developmental surveillance.
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