BRIC-THSTI Faridabad and IIT Madras researchers have developed an India-specific model to determine the age of a foetus in a pregnant woman in the second and third trimesters precisely. Currently, the age of a foetus (gestational age, GA) is determined using a formula developed for Western populations and are likely to be erroneous when applied in the later part of pregnancy due to variations in the growth of the foetus in Indian population. The newly developed second and third-trimester GA formula, Garbhini-GA2, accurately estimates the age of a foetus for the Indian population, reducing error by almost three times. Accurate GA is necessary for the appropriate care of pregnant women and for determining precise delivery dates.

Researchers at the Biotechnology Research and Innovation Council - Translational Health Science and Technology Institute (THSTI), Faridabad and Indian Institute of Technology Madras (IIT Madras), as part of the Interdisciplinary Group for Advanced Research on Birth Outcomes – DBT India Initiative (GARBH-Ini) program have developed a model for estimating the GA in pregnant women during the second and third trimesters. The Garbhini-GA2 is the first late-trimester GA estimation model to be developed and initially validated using Indian population data. Garbhini-GA2 which uses three routinely measured foetal ultrasound parameters, was developed using GARBH-Ini cohort data documented at Gurugram Civil Hospital, Haryana, and was initially validated in an independent cohort in South India.

In the results published in the Lancet Regional Health Southeast Asia on February 13, 2024, the researchers used genetic algorithm-based methods to develop Garbhini-GA2, which when applied in the second and third trimesters of pregnancy, was more accurate than the currently used models. For instance, the Garbhini-GA2 model, compared to Hadlock, reduces the GA estimation median error by more than three times.

Ultrasound dating in early pregnancy is the standard of care for determining GA. However a major proportion of women in India have their first ultrasound done in their second and third trimester of pregnancy. In these women, the application of Indian population-specific GA formulae, with better accuracy, can potentially improve pregnancy care leading to better outcomes. This accurate dating will also enhance the precision of epidemiological estimates for pregnancy outcomes in the country. Once validated in prospective pan-India cohorts, this Garbhini-GA2 can be deployed in clinics across India, improving the care delivered by obstetricians and neonatologists, thus reducing maternal and infant mortality rates in India.

This study was conducted in partnership with Gurugram Civil Hospital, Gurugram, Safdarjung Hospital, New Delhi, Christian Medical College Vellore and Pondicherry Institute of Medical Sciences, Puducherry. The GARBH-Ini program is a flagship programme supported by the Department of Biotechnology (DBT), Govt of India. The data science research was funded by the Grand Challenges India program of the Biotechnology Industry Research Assistance Council (BIRAC), DBT, Govt. of India. Additional funding came from the Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI) and the Centre for Integrative Biology and Systems Medicine (IBSE), IIT Madras.

Highlighting the importance of this study, Dr Shinjini Bhatnagar, the principal investigator of the GARBH-Ini programme and distinguished professor at THSTI, said, “Improving the GA accuracy is a critical component of the broader goals of the Garbhini study, which aims to reduce the adverse pregnancy outcomes. The mere application of sophisticated data science tools is not sufficient without addressing a specific clinical need. The crux of ensuring that these technological advancements yield tangible benefits in the clinical realm lies in the end-to-end partnership between clinicians and data scientists. Such collaboration ensures that the development of solutions is not only technically sound but also clinically relevant and seamlessly integrated into healthcare workflows. This study is an exemplar of this approach.”

Dr Himanshu Sinha, at Bhupat and Jyoti Mehta School of Biosciences, Department of Biotechnology, IIT Madras who led the data science work, said, “IIT Madras has been contributing towards solving healthcare problems at the grassroots and local level with the aim of enhancing public health in India. To this end, working with our clinical partner, THSTI, we utilise advanced data science and AI/ML techniques to build tools to predict unfavourable birth outcomes. The first step towards this is to develop accurate GA models that perform significantly better than currently used models designed using Western populations.”

Dr Rajesh Gokhale, Secretary, DBT, noted that “Garbh-Ini is a flagship programme of DBT, and the development of these population-specific models for estimating gestational age is a commendable outcome. These models are being validated across the country.”

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