Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Prenatal diagnosis ; h5-index 40.0

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 3rd 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. This article is protected by copyright. All rights reserved.

Nee Saw Shier, Biswas Arijit, Zaini Mattar Citra Nurfarah, Kuan Lee Hwee, Hwai Yap Choon


Small-for-gestational-age, artificial intelligence, early detection., machine learning models, nuchal fold thickness