Introduction: Dental caries is the most common oral disease worldwide, affecting up to 90% of children globally. It can lead to pain, infection, and impaired quality of life. Early prevention is a key strategy for reducing the prevalence of dental caries in young children. Valid and reliable diagnostic or prognostic tools that enable accurate individualised prediction of current or future dental caries are essential for facilitating personalised caries prevention and early intervention. However, no efficacious tools currently exist in early childhood; the optimal period for disease prevention. We aim to develop and validate diagnostic and prognostic prediction tools for dental caries in young children, utilising a combination of environmental, physical, behavioural and biological early life data. Methods and analysis: Data sources include two prospective studies, with a total sample size of approximately 600 children. These cohorts have collected detailed demographic, antenatal, perinatal and postnatal data from medical records and parent-completed questionnaires and biological samples including a dental plaque swab. Candidate predictor variables will include sociodemographic characteristics, health history, behavioural, and microbiological characteristics. The outcome variable will be the presence, incidence, or severity of dental caries diagnosed using the International Caries Detection and Assessment System (ICDAS). Statistical and machine learning approaches will be utilised for selection of predictor variables and model development. Internal validation will be conducted using resampling methods (i.e. bootstrapping) and nested cross validation. Model performance will be evaluated using standard performance metrics such as accuracy, discrimination, and calibration. Where feasible, external validation will be performed in an independent cohort. Model development and reporting will be guided by the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement and the Prediction model Risk Of Bias Assessment Tool (PROBAST) guidelines. Discussion: This protocol describes data collection procedures, outcome and predictor variable configuration, and planned omics-based diagnostic and prognostic prediction analyses. The study employs a discovery-driven approach for the development and validation phases, allowing findings from select steps to inform subsequent stages. Ethics and dissemination: This study has ethical and governance approval from The Royal Children Hospital Melbourne Human Research Ethics Committee (HREC/111803/RCHM-2024).

The authors have declared no competing interest.

Financial disclosure and conflicts of interest This study is undertaken as part of an MRFF-funded study led by the Murdoch Children Research Institute (MRFF grant code: MRF2007268). The funder had no role in the design, data collection, data analysis, and reporting of this study. The authors have no conflicts of interest to declare.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Research Ethics and Governance of The Royal Children Hospital Melbourne gave ethical approval for this work.

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The datasets generated and analysed during this study are not publicly available due to consent not being obtained from participants for public sharing of data but are available from the corresponding author on reasonable request and conditional on the scope of ethically approved research related to BCG, allergy, infections or child health in the MIS BAIR study.