Enhancing Small‐for‐Gestational‐Age Prediction: Multi‐Country Validation of Nuchal Thickness, Estimated Fetal Weight, and Machine Learning Models
Small-for-gestational-age infants demonstrate significantly reduced second-trimester nuchal thickness. Employing the disjunctive rule enhanced small-for-gestational-age prediction. Logistic regression and support vector machines show superior performance among all models, highlighting the advantages of machine learning. Larger prospective studies are needed to assess clinical utility.
Authors
- Nur Asyilla Che Jalil (ORCID: https://orcid.org/0000-0001-5871-2295)
- Azanna Ahmad Kamar (ORCID: https://orcid.org/0000-0003-0383-1283)
- Rahmah Saaid (ORCID: https://orcid.org/0000-0003-0971-7481)
- Neha Sethi (ORCID: https://orcid.org/0000-0003-3477-3709)
- Chu Kiong Loo (ORCID: https://orcid.org/0000-0001-7867-2665)
- Shier Nee Saw (ORCID: https://orcid.org/0000-0002-2598-8452)
- Citra Nurfarah Zaini Mattar (ORCID: https://orcid.org/0000-0003-0068-6957)
- Jiaxuan Deng (ORCID: https://orcid.org/0009-0006-4756-7188)
Institutions
- National University of Singapore (SG)
- University of Malaya (MY)
- National University Hospital (SG)
Publication Details
- Journal
- Prenatal Diagnosis
- Published
- 2025-01-16
- DOI
- https://doi.org/10.1002/pd.6748
- Citations
- 1
- Primary Topic
- Pregnancy and preeclampsia studies
- Type
- article
- Field-Weighted Citation Impact
- 2.06
Funders
- Kementerian Sains, Teknologi dan Inovasi