Enhancing Small-for-Gestational-Age Prediction: Multi-Country Validation of Nuchal Thickness, Estimated Fetal Weight, and Machine Learning Models.

Journal: Prenatal diagnosis
PMID: 39817730

Abstract

OBJECTIVE: The first objective is to develop a nuchal thickness reference chart. The second objective is to compare rule-based algorithms and machine learning models in predicting small-for-gestational-age infants.

Authors

  • Jiaxuan Deng
    Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
  • Neha Sethi A/P Naresh Sethi
    Department of Obstetrics and Gynaecology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
  • Azanna Ahmad Kamar
    Department of Paediatrics, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
  • Rahmah Saaid
    Department of Obstetrics and Gynaecology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
  • Chu Kiong Loo
  • Citra Nurfarah Zaini Mattar
    Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health Systems, Singapore.
  • Nurul Syazwani Jalil
    Department of Obstetrics and Gynaecology, Faculty of Medicine, Universiti Malaya, Kuala Lumpur, Malaysia.
  • Shier Nee Saw
    Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore.

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