Use of Artificial Intelligence in Predicting Adverse Pregnancy Outcome - Ghafoor, Maria; Aiman Saleem, Umm E; Kalsoom, Umm e; Mughis, Amber; Rehman, Iqra; Baloch, Mahnoor (Journal of Neonatal Surgery, 2025)
Background: Adverse consequences of pregnancy continue to be a significant determinant of maternal and neonatal morbidity in the low and middle-income countries. Traditional methods of antenatal risk assessment usually do not make it to distinguish the women who will develop complications later. Recent developments in artificial intelligence provide new opportunities of early risk detection based on routine clinical data. Objective: To evaluate the performance of an artificial intelligence–based prediction model in identifying pregnancies at risk of adverse outcomes. Methodology: The study was a prospective observational study, which was carried out in Women and Children Hospital, MTI, Dera Ismail Khan, between January 2025 and June 2025. A total of seventy two pregnant women had been enrolled and followed up till delivery. At booking, demographic, obstetric, clinical, laboratory and ultrasound parameters were noted. With the help of artificial intelligence, a model that grouped the participants into the high- and low-risk category was used before delivery. These were pregnancy and neonatal outcomes recorded at birth. The correlation between the AI prediction and the realized outcomes was evaluated by the chi-square test, the measures of diagnostic performance were also computed. Results: Significant percentages of the participants experienced adverse pregnancy outcomes, and the most common were low birth weight and preterm birth. The adverse outcomes were much more prevalent in women who were considered as high risk based on the AI model than in women who were characterized as low risk (p < 0.001). The model showed a high overall prediction accuracy, sensitivity, and specificity, which suggest that it is a reliable predictor. Conclusion: Artificial intelligence based risk prediction models can effectively identify pregnancies at risk of adverse outcomes using routine antenatal data. Their integration into standard antenatal care may facilitate early intervention and contribute to improved maternal and neonatal outcomes, particularly in resource-limited settings..