Digital Twin Predicts Birth Date to Prevent Premature Births

Researchers developed a digital twin to simulate fetal development and predict birth dates, potentially reducing premature births.

The digital twin uses machine learning algorithms to analyze fetal growth patterns and identify potential risks of premature birth.

The system can also provide personalized recommendations for pregnant women, such as diet and exercise plans.

The researchers plan to test the digital twin in clinical trials to evaluate its effectiveness in preventing premature births.