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This paper is in the following e-collection/theme issue:

Artificial Intelligence (4765) Clinical Information and Decision Making (3700) Obstetrics, Gynecology, and Reproductive Medicine (418) Decision Support for Health Professionals (2231) Secondary Use of Clinical Data for Research and Surveillance (635) Machine Learning (3168)

Published on in Vol 28 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/88450, first published .
Pregnant woman uses CURA app on phone for pregnancy tracking

Machine Learning–Based First-Trimester Antenatal Risk Prediction for Adverse Maternal and Neonatal Outcomes: Multicenter Model Development Study

Machine Learning–Based First-Trimester Antenatal Risk Prediction for Adverse Maternal and Neonatal Outcomes: Multicenter Model Development Study

Authors of this article:

Sarah Li1 Author Orcid Image ;   David Y Y Tan2 Author Orcid Image ;   Jingxian Zhang2 Author Orcid Image ;   Aniza P Mahyuddin3 Author Orcid Image ;   Harshaana Ramlal1 Author Orcid Image ;   Sebastian E Illanes4, 5 Author Orcid Image ;   Max Monckeberg4 Author Orcid Image ;   Alejandra F Plaza4 Author Orcid Image ;   Maria L Paz Morgan4 Author Orcid Image ;   Matthew W Kemp3, 6 Author Orcid Image ;   Kee Yuan Ngiam7 Author Orcid Image ;   Peter Lindgren8, 9 Author Orcid Image ;   Marius Kublickas9 Author Orcid Image ;   Karolina Kublickiene10 Author Orcid Image ;   Ruifen Weng2 Author Orcid Image ;   Sidney Yee11 Author Orcid Image ;   Mahesh Choolani3 Author Orcid Image
Article Authors Cited by Tweetations Metrics

Sarah Li   1 ;   David Y Y Tan   2 * ;   Jingxian Zhang   2 * ;   Aniza P Mahyuddin   3 ;   Harshaana Ramlal   1 ;   Sebastian E Illanes   4, 5 ;   Max Monckeberg   4 ;   Alejandra F Plaza   4 ;   Maria L Paz Morgan   4 ;   Matthew W Kemp   3, 6 ;   Kee Yuan Ngiam   7 ;   Peter Lindgren   8, 9 ;   Marius Kublickas   9 ;   Karolina Kublickiene   10 ;   Ruifen Weng   2 ;   Sidney Yee   11 ;   Mahesh Choolani   3

1 Department of Obstetrics and Gynaecology, National University Hospital, Singapore, Singapore, Singapore

2 Diagnostics Development Hub, Agency for Science, Technology and Research, Singapore, Singapore

3 Department of Obstetrics and Gynaecology, NUS Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore, Singapore

4 Department of Obstetrics and Gynecology and Reproductive Biology Laboratory, Universidad de los Andes, Santiago, Chile

5 IMPACT, Center of Interventional Medicine for Precision and Advanced Cellular Therapy, Santiago, Chile

6 Women and Infants Research Foundation, King Edward Memorial Hospital, Perth, Australia

7 Department of Surgery, National University Hospital, Singapore, Singapore

8 Department of Women’s and Children’s Health, Karolinska Institutet, Stockholm, Sweden

9 Center for Fetal Medicine, Karolinska University Hospital, Stockholm, Sweden

10 Department of Clinical Science, Intervention and Technology, Renal Medicine unit, Karolinska Institutet, Stockholm, Sweden

11 Innovation and Enterprise, Agency for Science, Technology and Research, Singapore, Singapore

*these authors contributed equally

Corresponding Author:

  • Mahesh Choolani
  • Department of Obstetrics and Gynaecology
  • NUS Yong Loo Lin School of Medicine, National University of Singapore
  • 1E Kent Ridge Road, NUHS Tower Block, Level 12
  • Singapore, Singapore 119228
  • Singapore
  • Phone: 65 93889316
  • Email: obgmac@nus.edu.sg