Electrical Electronics Engineering: Open Access
ISSN: 3071-0774 | DOI: 10.64030/3071-0774
Quek YS, Meng Wyzley PE, Ramly F, Chai BS, Si Tong SL and Hassan JB
Artificial intelligence (AI) has revolutionized obstetrics care by leveraging neural networks to analyze extensive patient data for improved diagnosis, monitoring, and prognosis and provide individualized patient care. The MoirAI System® utilizes population data, patient measurements, and AI algorithms to make accurate predictions, focusing on prenatal gestational weight as an illustrative example. The system enhances traditional decision-making by unlocking the potential of complex, interconnected obstetric data, enabling early inference and patient-driven changes. Continuous monitoring and predictions based on historical data improve sensitivity and specificity, thereby enhancing clinical judgment. The study evaluates the system's accuracy through Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), indicating promising results. Further validation and adaptation of AI models are necessary to achieve broader applicability and better predictive accuracy. This study underscores the potential of AI to enhance pregnancy care and calls for continuous development and validation of AI predictive models.