How Does Garbhini-GA2 Work?
Here’s a breakdown of its working principle:
- Data Acquisition: The model is trained on a large dataset of information specific to the Indian population, including ultrasound scans, maternal characteristics, and fetal biometric measurements.
- Machine Learning Techniques: Garbhini-GA2 employs machine learning algorithms to analyze the collected data and identify complex relationships between various factors that influence fetal growth and development.
- Fetal Age Estimation: Based on the learned patterns from the data, the model generates a highly accurate prediction of the fetus’s gestational age.
This multifaceted approach allows Garbhini-GA2 to account for the unique characteristics and growth patterns observed in the Indian population, leading to a significant reduction in errors compared to conventional methods.
Garbhini – GA2: IIT Madras, THSTI Develop India -Specific AI Gestation Age Estimation Model
Determining the gestational age (GA) of a fetus plays a crucial role in ensuring optimal pregnancy care. Traditionally, healthcare professionals rely on ultrasound measurements and established formulas to estimate GA. However, these methods often utilize data derived from Western populations, potentially leading to inaccuracies when applied to individuals from diverse ethnicities and backgrounds, including the Indian population.
This article delves into the groundbreaking research by IIT Madras and THSTI and their development of Garbhini-GA2, an AI-powered solution specifically tailored to address the limitations of existing methods and enhance fetal age estimation accuracy for pregnant women in India.
Synopsis
- Garbhini-GA2 is the first late-trimester GA estimation model developed and validated using Indian population data.
- The model uses three routinely measured fetal ultrasound parameters.
- The research was published in the prestigious journal Lancet Regional Health Southeast Asia.
- Garbhini-GA2 is expected to be deployed in clinics across India after further validation.