Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study

Fung, R; Villar, J; Dashti, A; Ismail, LC; Staines-Urias, E; Ohuma, EO; Salomon, LJ; Victora, CG; Barros, FC; Lambert, A; Carvalho, M; Jaffer, YA; Noble, JA; Gravett, MG; Purwar, M; Pang, R; Bertino, E; Munim, S; Min, AM; McGready, R; Norris, SA; Bhutta, ZA; Kennedy, SH; Papageorghiou, AT; Ourmazd, A; Norris, S; Abbott, SE; Abubakar, A; Acedo, J; Ahmed, I; Al-Aamri, F; Al-Abduwani, J; Al-Abri, J; Alam, D; Albernaz, E; Algren, H; Al-Habsi, F; Alija, M; Al-Jabri, H; Al-Lawatiya, H; Al-Rashidiya, B; Altman, DG; Al-Zadjali, WK; Andersen, HF; Aranzeta, L; Baricco, M; Barros, FC; Barsosio, H; Batiuk, C; Ash, S

Journal article

Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study

Abstract:: Background Preterm birth is a major global health challenge, the leading cause of death in children under 5 years of age, and a key measure of a population’s general health and nutritional status. Current clinical methods of estimating fetal gestational age are often inaccurate. For example, between 20 and 30 weeks of gestation, the width of the 95% prediction interval around the actual gestational age is estimated to be 18–36 days, even when the best ultrasound estimates are used. The aims o...
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Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Fung, R., Villar, J., Dashti, A., Ismail, L. C., Staines-Urias, E., Ohuma, E. O., Salomon, L. J., Victora, C. G., Barros, F. C., Lambert, A., Carvalho, M., Jaffer, Y. A., Noble, J. A., Gravett, M. G., Purwar, M., Pang, R., Bertino, E., Munim, S., Min, A. M., … Ash, S. (2020). Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study. The Lancet Digital Health, 2(7), e368–e375.

MLA Style

Fung, R., et al. “Achieving Accurate Estimates of Fetal Gestational Age and Personalised Predictions of Fetal Growth Based on Data from an International Prospective Cohort Study: a Population-Based Machine Learning Study.” The Lancet Digital Health, vol. 2, no. 7, Elsevier, 2020, pp. e368–e375.

Chicago Style

Fung, R, J Villar, A Dashti, LC Ismail, E Staines-Urias, EO Ohuma, LJ Salomon, et al. 2020. “Achieving Accurate Estimates of Fetal Gestational Age and Personalised Predictions of Fetal Growth Based on Data from an International Prospective Cohort Study: a Population-Based Machine Learning Study.” The Lancet Digital Health 2 (7): e368–e375.
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