Analysis of ultrasound images and clinical data in two clinical scenarios: prediction of failure of induction of labor and risk of preterm delivery

García Ocaña, María Inmaculada. (2019). Analysis of ultrasound images and clinical data in two clinical scenarios: prediction of failure of induction of labor and risk of preterm delivery Master Thesis, Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial

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Título Analysis of ultrasound images and clinical data in two clinical scenarios: prediction of failure of induction of labor and risk of preterm delivery
Autor(es) García Ocaña, María Inmaculada
Abstract This work explores the use of radiomics and machine learning to extract relevant biomarkers from ultrasound (US) images that can be used in obstetric practice. Two clinical applications are studied: the prediction of induction of labor (IOL) failure based on clinical data and US images obtained prior to IOL, and the estimation of risk of preterm birth based on routinely US images acquired in the 20th week of pregnancy. Several machine learning classifiers and feature selection techniques are tested and the results are compared. The best model for the prediction of IOL failure was a random forest that model obtained an AUC of 0.75, with 69% sensitivity and 71% specificity. The best model for the prediction of preterm birth was a random forest that obtained an AUC of 0.77 AUC, with 71% sensitivity and 69% specificity . These preliminary results suggest that features obtained from US images can be used to estimate risks in these two obstetric problems. Transvaginal US is cheap, widely available at hospitals, and performed routinely. Therefore these method can be easily implemented in clinical practice and help practitioners choose a most personalized treatment for each patient, improving the outcomes. Further validation with a largest and more diverse dataset is needed, especially to assess how the image analysis methods work with images from different US vendors.
Notas adicionales Trabajo de Fin de Máster. Máster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones. UNED
Materia(s) Ingeniería Informática
Editor(es) Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial
Director/Tutor Javier Diez, Francisco
López-Linares Román, Karen
Fecha 2019-09-26
Formato application/pdf
Identificador bibliuned:master-ETSInformatica-IAA-Migarcia
http://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-Migarcia
Idioma spa
Versión de la publicación acceptedVersion
Nivel de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de recurso master Thesis
Tipo de acceso Acceso abierto

 
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Creado: Wed, 14 Oct 2020, 21:40:36 CET