Shier Nee Saw1,2, Arijit Biswas3, Citra Nurfarah Zaini Mattar3, Hwee Kuan Lee1,4,5,6,7, Choon Hwai Yap8. 1. Bioinformatics Institute, Agency for Science, Technology and Research (A*STAR), Singapore. 2. Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, University of Malaya, Malaysia. 3. Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, National University Health Systems, Singapore. 4. Rehabilitation Research Institute of Singapore, Singapore. 5. School of Computing, National University of Singapore, Singapore. 6. Image and Pervasive Access Lab (IPAL), CNRS UMI, Singapore. 7. Singapore Eye Research Institute, Singapore. 8. Department of Bioengineering, Imperial College London, London, UK.
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.
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.
Authors: Xi Bai; Zhibo Zhou; Mingliang Su; Yansheng Li; Liuqing Yang; Kejia Liu; Hongbo Yang; Huijuan Zhu; Shi Chen; Hui Pan Journal: Front Public Health Date: 2022-08-08