SAW SHIER NEE
Email Address
biessn@nus.edu.sg
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Publication Machine learning improves early prediction of small-for-gestational-age births and reveals nuchal fold thickness as unexpected predictor(John Wiley and Sons Ltd, 2021-02-12) Saw, Shier Nee; Biswas, Arijit; Mattar, Citra Nurfarah Zaini; Lee, Hwee Kuan; Yap, Choon Hwai; BIOMEDICAL ENGINEERING; DEPARTMENT OF COMPUTER SCIENCE; OBSTETRICS & GYNAECOLOGYObjective: 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.Publication MOTORIZING AND OPTIMIZING ULTRASOUND STRAIN ELASTOGRAPHY FOR DETECTING INTRAUTERINE GROWTH RESTRICTION PREGNANCIES(2018-08-11) Citra Nurfarah Zaini Mattar; Saw, Shier Nee; Biswas, Arijit; Yap, Choon Hwai; Dr Citra Nurfarah Zaini Mattar; BIOMEDICAL ENGINEERING; OBSTETRICS & GYNAECOLOGYPublication Altered Placental Chorionic Arterial Biomechanical Properties During Intrauterine Growth Restriction(Nature Publishing Group, 2018) Saw, S.N; Tay, J.J.H; Poh, Y.W; Yang, L; Tan, W.C; Tan, L.K; Clark, A; Biswas, A; Mattar, C.N.Z; Yap, C.H; BIOMEDICAL ENGINEERING; OBSTETRICS & GYNAECOLOGYIntrauterine growth restriction (IUGR) is a pregnancy complication due to placental dysfunction that prevents the fetus from obtaining enough oxygen and nutrients, leading to serious mortality and morbidity risks. There is no treatment for IUGR despite having a prevalence of 3% in developed countries, giving rise to an urgency to improve our understanding of the disease. Applying biomechanics investigation on IUGR placental tissues can give important new insights. We performed pressure-diameter mechanical testing of placental chorionic arteries and found that in severe IUGR cases (RI > 90th centile) but not in IUGR cases (RI < 90th centile), vascular distensibility was significantly increased from normal. Constitutive modeling demonstrated that a simplified Fung-type hyperelastic model was able to describe the mechanical properties well, and histology showed that severe IUGR had the lowest collagen to elastin ratio. To demonstrate that the increased distensibility in the severe IUGR group was related to their elevated umbilical resistance and pulsatility indices, we modelled the placental circulation using a Windkessel model, and demonstrated that vascular compliance (and not just vascular resistance) directly affected blood flow pulsatility, suggesting that it is an important parameter for the disease. Our study showed that biomechanics study on placenta could extend our understanding on placenta physiology. © 2018, The Author(s).Publication Differences in placental capillary shear stress in fetal growth restriction may affect endothelial cell function and vascular network formation(Springer Science and Business Media LLC, 2019-12) Tun, Win M; YAP CHOON HWAI; SAW SHIER NEE; James, Joanna L; Clark, Alys R; Dr Yap Choon Hwai; BIOMEDICAL ENGINEERINGPublication Model learning analysis of 3D optoacoustic mesoscopy images for the classification of atopic dermatitis(The Optical Society, 2021-05-27) Park, Sojeong; Saw, Shier Nee; Li, Xiuting; Paknezhad, Mahsa; Coppola, Davide; Dinish, U. S.; Ebrahim Attia, Amalina Binite; Yew, Yik Weng; Guan Thng, Steven Tien; Lee, Hwee Kuan; Olivo, Malini; BIOMEDICAL ENGINEERING; DEPARTMENT OF COMPUTER SCIENCEAtopic dermatitis (AD) is a skin inflammatory disease affecting 10% of the population worldwide. Raster-scanning optoacoustic mesoscopy (RSOM) has recently shown promise in dermatological imaging. We conducted a comprehensive analysis using three machine-learning models, random forest (RF), support vector machine (SVM), and convolutional neural network (CNN) for classifying healthy versus AD conditions, and sub-classifying different AD severities using RSOM images and clinical information. CNN model successfully differentiates healthy from AD patients with 97% accuracy. With limited data, RF achieved 65% accuracy in sub-classifying AD patients into mild versus moderate-severe cases. Identification of disease severities is vital in managing AD treatment. © 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.