Detection and Prediction of First and Second Trimester Maternal Thyroid Dysfunction

Thyroid dysfunction—hypothyroidism or hyperthyroidism—in pregnant women poses great challenges and augments the risk of complications such as abortion, preeclampsia, and labor. Thus, early detection is crucial since this may avert a poor outcome either for the mother or the fetas. These conditions are generally diagnosed using the biochemical markers such as thyroid-stimulating hormone and freethyroxine which, are insensitive in identifying sub clinical cases, rendering them largely ineffective. This is countered by the predictive analysis embedded in the machine learning (ML) technique of random forests and neural networks for an accelerated diagnosis. By blending the clinical data with demographic and lifestyle factors, these ML models reveal latent patterns often masked by conventional methods, thus guiding personalized treatment options. The present study discusses how fetal health monitoring at the first and second trimesters is being transformed by predictive analytic. The utilization of AI-based diagnostics can promote the evolution of antenatal care into a real-time risk assessment with immediate medical intervention, ultimately ameliorating maternal and fetal health outcomes.