Authors - Nassour Annour Saad, Mahamat Issa Hassan, Marayi Choroma, Mahamat Atteib Ibrahim Doutoum, Djaury Dadjia Abstract - Pediatric type 1 diabetes (T1D) remains a major public health priority, especially in low-resource settings where presenting diabetic ketoacidosis is still common. This systematic review (2020–2025), conducted under PRISMA 2020 and complemented by TRIPOD/TRIPOD+AIinspired criteria for predictive models, synthesizes AI/ML work on early screening and risk stratification in children. Islet autoantibodies and genetic risk scores improve discrimination, but the literature shows substantial AUC variability depending on sample size, calibration, and validation design (single split versus repeated or family-level validation). Ensemble models often outperform classical approaches with multimodal data. We emphasize external validation, class-imbalance handling, and reproducible pipelines. The main gap remains the absence of models simultaneously integrating autoantibodies, HLA/GRS, and C-peptide.
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