Currently submitted to: Journal of Medical Internet Research
Date Submitted: Nov 28, 2025
Open Peer Review Period: Dec 1, 2025 - Jan 26, 2026
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Machine Learning–Based First-Trimester Antenatal Risk Prediction for Adverse Maternal and Neonatal Outcomes: A Multicenter Model Development Study
ABSTRACT
Background:
Maternal outcomes remain inequitable worldwide. Severe morbidity persists, and current risk assessment tools are largely arbitrary, focusing on biomedical factors while overlooking social determinants of health. There is a need for data-driven, artificial intelligence (AI) models to improve early pregnancy risk identification and management.
Objective:
To develop and internally validate first-trimester AI-based antenatal risk assessment models across three geographically and socio-ethnically diverse populations (Sweden, Chile, and Singapore), and to compare their performance against existing clinical risk assessment strategies.
Methods:
Retrospective population-based data from over 500,000 pregnancies from Sweden, Chile, and Singapore were used to develop Machine learning (ML) models predicting a composite of adverse maternal and neonatal outcomes. Models were trained and validated separately for each population using first-trimester variables. Model discrimination, measured by the area under the receiver operating characteristic (AUROC) curve, was compared with corresponding real-world first trimester risk assessment approaches.
Results:
The prevalence of the composite adverse outcome was 10.4% (75,647/727,354) in Sweden, 21.9% (1,302/5,934) in Chile, and 16.3% (6,165/37,813) in Singapore. In Sweden, the guideline-based risk assessment achieved an AUROC of 0.53, compared with 0.65 for the LightGBM model (P<.001). In Chile, the midwifery-led risk assessment achieved an AUROC of 0.52, versus 0.66 from the Traditional ML CatBoost model (P<.001). In Singapore, healthcare professional-based risk assessment reached an AUROC of 0.56, compared with 0.60 for the LightGBM model (P<.05). In the Swedish and Singapore, sociodemographic variables were among the most influential predictive features.
Conclusions:
AI-based models developed using first-trimester data surpassed that of existing first-trimester clinical risk stratification strategies across all three distinct populations. These findings highlight the potential of integrating social, demographic, and behavioural determinants into AI-driven, clinician-augmented antenatal care frameworks to promote more equitable and personalized pregnancy risk assessment.
Citation
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