Examinando por Autor "Chabert, Steren"
Mostrando 1 - 4 de 4
Resultados por página
Opciones de ordenación
Ítem Four functional magnetic resonance imaging techniques for skeletal muscle exploration, a systematic review(Elsevier, 2021) Caroca, Sergio; Villagran, Diego; Chabert, SterenBackground: The study of muscle health has become more relevant lately, due to global aging and a higher incidence of musculoskeletal pathologies. Current exploration techniques, such as electromyography, do not provide accurate spatial information. Objective: The objective of this work is to perform a systematic review of the literature to synthesize the contributions that can offer functional MRI techniques commonly used in neuroimaging, applied to skeletal muscle: Blood Oxygen Level Dependent (BOLD), IntraVoxel Incoherent Motion (IVIM), Arterial Spin Labeling (ASL) and Dynamic Contrast Enhanced (DCE). Evidence acquisition: Web of Science and Medline databases were searched, over the last 10 years, focused on the use of BOLD, ASL, IVIM or DCE in skeletal muscle. Evidence synthesis: 59 articles were included after applying the selection criteria. 37 studies were performed in healthy subjects, and 22 in patients with different pathologies: in peripheral arterial disease, systemic sclerosis, diabetes, osteoporosis, adolescent idiopathic scoliosis, and dermatomyositis. Reference values in healthy subjects still vary in some cases. Conclusion: The studies show the feasibility of implementing functional MRI through BOLD, ASL, IVIM or DCE imaging in several muscles and their possible utility in different pathologies. A synthesis of how to implement such exploration is given here. Clinical impact: These four techniques are based on sequences already present in clinical MRI scanners, therefore, their use for functional muscle exploration does not require additional investment. These techniques allow visualization and quantification of parameters associated with the vascular health of the muscles and represent interesting support for musculoskeletal exploration.Ítem Image Quality Assessment to Emulate Experts’ Perception in Lumbar MRI Using Machine Learning(MDPI, 2021) Chabert, Steren; Castro, Juan Sebastian; Muñoz, Leonardo; Cox, Pablo; Riveros, Rodrigo; Vielma, Juan; Huerta, Gamaliel; Querales, Marvin; Saavedra, Carolina; Veloz, Alejandro; Salas, RodrigoMedical image quality is crucial to obtaining reliable diagnostics. Most quality controls rely on routine tests using phantoms, which do not reflect closely the reality of images obtained on patients and do not reflect directly the quality perceived by radiologists. The purpose of this work is to develop a method that classifies the image quality perceived by radiologists in MR images. The focus was set on lumbar images as they are widely used with different challenges. Three neuroradiologists evaluated the image quality of a dataset that included T1-weighting images in axial and sagittal orientation, and sagittal T2-weighting. In parallel, we introduced the computational assessment using a wide range of features extracted from the images, then fed them into a classifier system. A total of 95 exams were used, from our local hospital and a public database, and part of the images was manipulated to broaden the distribution quality of the dataset. Good recall of 82% and an area under curve (AUC) of 77% were obtained on average in testing condition, using a Support Vector Machine. Even though the actual implementation still relies on user interaction to extract features, the results are promising with respect to a potential implementation for monitoring image quality online with the acquisition process.Ítem Introducción al análisis de imágenes de resonancia magnética funcional en estado de reposo(Universidad de Valparaíso, 2015-01) Ramírez Sarmiento, Fernando José; Chabert, SterenNuestro cerebro gestiona todas nuestras actividades, pensamiento e ideas, lo que es vital para nuestro desarrollo como personas, antes se pensaba que cada estructura de nuestro encéfalo estaba destinada a una tarea en específico, pero hoy se entiende que es un conjunto de ellas. El análisis de imágenes de resonancia magnética en estado de reposo nos permite encontrar este conjunto de estructuras, que serán llamadas redes en estado de reposo, esta técnica ha tomado una importante relevancia en la última década al poder extraer información de nuestro cerebro de forma no invasiva ofreciendo apoyo clínico en el diagnóstico de enfermedades neurológica y mentales , este informe describe la resonancia magnética funcional en estado de reposo desde sus orígenes hasta las investigaciones en la actualidad, las técnicas más utilizadas como análisis de correlación basado en semilla y análisis de componentes independientes, a través de estos métodos se logró corroborar tres redes en modo defecto, donde se logró obtener mejores resultados a través del análisis de componentes independientes , porque describe de forma más clara las redes por defecto en un volumen de datos, pero el análisis de correlación basado en semilla tiene una ventaja que es responder a una pregunta inmediata al poder evaluar una red a la vez, también se encuentra una descripción detalla de la utilización de los toolbox gratuitos disponibles para Matlab, que se utilizarán para encontrar las redes en estado de reposo más comunes, para así facilitar el aprendizaje a los nuevos investigadores y estudiantes, estos podrán entrar rápidamente en el tema y realizar sus propias investigaciones. Se describen las dos técnicas antes mencionadas en el análisis de fMRI Estado de reposo, cada una con sus ventajas y desventajas donde el lector podrá escoger según sus necesidades el método más adecuado.Ítem Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review(Frontiers, 2022) Bertini, Ayleen; Salas, Rodrigo; Chabert, Steren; Sobrevia, Luis; Pardo, FabiánIntroduction: Artificial intelligence is widely used in medical field, and machine learning has been increasingly used in health care, prediction, and diagnosis and as a method of determining priority. Machine learning methods have been features of several tools in the fields of obstetrics and childcare. This present review aims to summarize the machine learning techniques to predict perinatal complications. Objective: To identify the applicability and performance of machine learning methods used to identify pregnancy complications. Methods: A total of 98 articles were obtained with the keywords “machine learning,” “deep learning,” “artificial intelligence,” and accordingly as they related to perinatal complications (“complications in pregnancy,” “pregnancy complications”) from three scientific databases: PubMed, Scopus, and Web of Science. These were managed on the Mendeley platform and classified using the PRISMA method. Results: A total of 31 articles were selected after elimination according to inclusion and exclusion criteria. The features used to predict perinatal complications were primarily electronic medical records (48%), medical images (29%), and biological markers (19%), while 4% were based on other types of features, such as sensors and fetal heart rate. The main perinatal complications considered in the application of machine learning thus far are pre-eclampsia and prematurity. In the 31 studies, a total of sixteen complications were predicted. The main precision metric used is the AUC. The machine learning methods with the best results were the prediction of prematurity from medical images using the support vector machine technique, with an accuracy of 95.7%, and the prediction of neonatal mortality with the XGBoost technique, with 99.7% accuracy. Conclusion: It is important to continue promoting this area of research and promote solutions with multicenter clinical applicability through machine learning to reduce perinatal complications. This systematic review contributes significantly to the specialized literature on artificial intelligence and women’s health.