Please use this identifier to cite or link to this item: https://doi.org/10.1002/pd.5903
Title: Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor
Authors: Saw, Shier Nee 
Biswas, Arijit 
Mattar, Citra Nurfarah Zaini 
Lee, Hwee Kuan 
Yap, Choon Hwai
Issue Date: 12-Feb-2021
Publisher: John Wiley and Sons Ltd
Citation: Saw, Shier Nee, Biswas, Arijit, Mattar, Citra Nurfarah Zaini, Lee, Hwee Kuan, Yap, Choon Hwai (2021-02-12). Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor. Prenatal Diagnosis 41 (4) : 505-516. ScholarBank@NUS Repository. https://doi.org/10.1002/pd.5903
Rights: Attribution 4.0 International
Abstract: Objective: To investigate the performance of the machine learning (ML) model in predicting small-for-gestational-age (SGA) at birth, using second-trimester data. Methods: Retrospective data of 347 patients, consisting of maternal demographics and ultrasound parameters collected between the 20th and 25th gestational weeks, were studied. ML models were applied to different combinations of the parameters to predict SGA and severe SGA at birth (defined as 10th and third centile birth weight). Results: Using second-trimester measurements, ML models achieved an accuracy of 70% and 73% in predicting SGA and severe SGA whereas clinical guidelines had accuracies of 64% and 48%. Uterine PI (Ut PI) was found to be an important predictor, corroborating with existing literature, but surprisingly, so was nuchal fold thickness (NF). Logistic regression showed that Ut PI and NF were significant predictors and statistical comparisons showed that these parameters were significantly different in disease. Further, including NF was found to improve ML model performance, and vice versa. Conclusion: ML could potentially improve the prediction of SGA at birth from second-trimester measurements, and demonstrated reduced NF to be an important predictor. Early prediction of SGA allows closer clinical monitoring, which provides an opportunity to discover any underlying diseases associated with SGA. © 2021 The Authors. Prenatal Diagnosis published by John Wiley & Sons Ltd.
Source Title: Prenatal Diagnosis
URI: https://scholarbank.nus.edu.sg/handle/10635/232240
ISSN: 0197-3851
DOI: 10.1002/pd.5903
Rights: Attribution 4.0 International
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