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- W4310584551 abstract "Preterm birth (PTB) is the leading cause of morbidity and mortality in neonates. Currently, the prediction of PTB is based on the identification of a short (less than 25 mm) cervix length (CL) measured by transvaginal ultrasound (TVUS). However, this methodology suffers a low sensitivity (< 50%). Therefore, there is an unmet need for developing better predictors of PTB. Textural analysis of B-mode US images has shown potential in providing quantitative biomarkers for tissue characterization. In this study, we investigated the utility of a texture-based machine-learning method applied to TVUS images to differentiate between term and preterm delivery and identify the potential risk of PTB. Sagittal TVUS images taken at 28 - 32 weeks of gestation were analyzed, and five regions of interest (ROI) were labeled. Morphological transforms (Prewitt, Sobel) and normalization were applied to the images to generate a vast pool of imaging features. To select the best features for building predictive models, Borda ranking was applied. With the selected features, three classifier models were made: logistic regression (LR), random forest (RF), and multilayer perceptron (MLP). At a fixed false positive rate of 10 percent, the MLP model achieved a sensitivity of 67 percent, suggesting that the Borda ranking procedure has a high potential for selecting meaningful features to be used in simple non-linear models, such as the MLP." @default.
- W4310584551 created "2022-12-12" @default.
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- W4310584551 date "2022-10-10" @default.
- W4310584551 modified "2023-10-03" @default.
- W4310584551 title "Cervix Ultrasound Texture Analysis to Differentiate Between Term and Preterm Birth Pregnancy: A Machine Learning Approach" @default.
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- W4310584551 doi "https://doi.org/10.1109/ius54386.2022.9958755" @default.
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