Machine learning for accurate estimation of fetal gestational age based on ultrasound images
Issued Date
2023-12-01
Resource Type
eISSN
23986352
Scopus ID
2-s2.0-85149974498
Journal Title
npj Digital Medicine
Volume
6
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
npj Digital Medicine Vol.6 No.1 (2023)
Suggested Citation
Lee L.H., Bradburn E., Craik R., Yaqub M., Norris S.A., Ismail L.C., Ohuma E.O., Barros F.C., Lambert A., Carvalho M., Jaffer Y.A., Gravett M., Purwar M., Wu Q., Bertino E., Munim S., Min A.M., Bhutta Z., Villar J., Kennedy S.H., Noble J.A., Papageorghiou A.T. Machine learning for accurate estimation of fetal gestational age based on ultrasound images. npj Digital Medicine Vol.6 No.1 (2023). doi:10.1038/s41746-023-00774-2 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/81321
Title
Machine learning for accurate estimation of fetal gestational age based on ultrasound images
Author's Affiliation
Intelligent Ultrasound Group plc
Mahidol Oxford Tropical Medicine Research Unit
Ministry of Health Oman
University of Sharjah
The Aga Khan University
London School of Hygiene & Tropical Medicine
Hospital for Sick Children University of Toronto
Green Templeton College
University of Oxford
Universidade Catolica de Pelotas
University of the Witwatersrand, Johannesburg
University of Washington
Universidade Federal de Pelotas
Università degli Studi di Torino
Peking University Health Science Center
University of Oxford Medical Sciences Division
Ketkar Hospital
Mahidol Oxford Tropical Medicine Research Unit
Ministry of Health Oman
University of Sharjah
The Aga Khan University
London School of Hygiene & Tropical Medicine
Hospital for Sick Children University of Toronto
Green Templeton College
University of Oxford
Universidade Catolica de Pelotas
University of the Witwatersrand, Johannesburg
University of Washington
Universidade Federal de Pelotas
Università degli Studi di Torino
Peking University Health Science Center
University of Oxford Medical Sciences Division
Ketkar Hospital
Other Contributor(s)
Abstract
Accurate estimation of gestational age is an essential component of good obstetric care and informs clinical decision-making throughout pregnancy. As the date of the last menstrual period is often unknown or uncertain, ultrasound measurement of fetal size is currently the best method for estimating gestational age. The calculation assumes an average fetal size at each gestational age. The method is accurate in the first trimester, but less so in the second and third trimesters as growth deviates from the average and variation in fetal size increases. Consequently, fetal ultrasound late in pregnancy has a wide margin of error of at least ±2 weeks’ gestation. Here, we utilise state-of-the-art machine learning methods to estimate gestational age using only image analysis of standard ultrasound planes, without any measurement information. The machine learning model is based on ultrasound images from two independent datasets: one for training and internal validation, and another for external validation. During validation, the model was blinded to the ground truth of gestational age (based on a reliable last menstrual period date and confirmatory first-trimester fetal crown rump length). We show that this approach compensates for increases in size variation and is even accurate in cases of intrauterine growth restriction. Our best machine-learning based model estimates gestational age with a mean absolute error of 3.0 (95% CI, 2.9–3.2) and 4.3 (95% CI, 4.1–4.5) days in the second and third trimesters, respectively, which outperforms current ultrasound-based clinical biometry at these gestational ages. Our method for dating the pregnancy in the second and third trimesters is, therefore, more accurate than published methods.