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Percentile Growth Curves for Placenta Measures: A Dynamic Shiny Application

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Computational Science and Its Applications – ICCSA 2022 Workshops (ICCSA 2022)

Abstract

For decades, researchers and health professionals have been using fetal and newborns measurements to evaluate its development. In recent years, there have been new studies suggesting that the placenta’s measurements and its evolutions are capable of reflecting changes in the fetus’s development and even newborn and adult diseases.

Most of these analyses are done using growth curves that use linear regression methodologies such as previous studies done. To account for errors associated with this regression and use a more robust method, quantile regression is used to create the placenta’s growth curves. The dataset used for this study was collected on Portuguese CGC Genetics and involves the Portuguese parturient population from different regions.

It is also an objective of this study to create a dynamic application that allows the researcher or health professional to enter placental growth values and compare them to the created growth curves to evaluate the evolution of the placenta. This application uses a CSV file with the information gathered from the placenta and is uploaded to the application which then plots the values on the created growth curves. The application also allows the user to edit the values. This application was created on Shiny and can be accessed at https://samuelalves.shinyapps.io/APP2/.

This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020

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Correspondence to Samuel Alves .

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Alves, S., Braga, A.C., Nogueira, R. (2022). Percentile Growth Curves for Placenta Measures: A Dynamic Shiny Application. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13377. Springer, Cham. https://doi.org/10.1007/978-3-031-10536-4_36

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  • DOI: https://doi.org/10.1007/978-3-031-10536-4_36

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  • Online ISBN: 978-3-031-10536-4

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