JMIR Publications

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.