Development of a convolutional neural network for diagnosing osteoarthritis, trained with knee radiographs from the ELSA-Brasil Musculoskeletal.

Objective: To develop a convolutional neural network (CNN) model, trained with the Brazilian "Estudo Longitudinal de Saúde do Adulto Musculoesquelético" (ELSA-Brasil MSK, Longitudinal Study of Adult Health, Musculoskeletal) baseline radiographic examinations, for the automated classification of knee osteoarthritis.

Materials And Methods: This was a cross-sectional study carried out with 5,660 baseline posteroanterior knee radiographs from the ELSA-Brasil MSK database (5,660 baseline posteroanterior knee radiographs). The examinations were interpreted by a radiologist with specific training, and the calibration was as established previously.

The Effects of Prenatal Diagnosis on the Interaction of the Mother-Infant Dyad: A Longitudinal Study of Prenatal Care in the First Year of Life.

Introduction: Mother-child interactions during the first years of life have a significant impact on the emotional and cognitive development of the child. In this work, we study how a prenatal diagnosis of malformation may affect maternal representations and the quality of these early interactions. To this end, we conducted a longitudinal observational study of mother-child interactions from the gestational stage until the baby completed 12 months of age.

Automatic measurement of pressure ulcers using Support Vector Machines and GrabCut.

Background And Objective: Pressure ulcers are regions of trauma caused by a continuous pressure applied to soft tissues between a bony prominence and a hard surface. The manual monitoring of their healing evolution can be achieved by area assessment techniques that include the use of rulers and adhesive labels in direct contact with the injury, being highly inaccurate and subjective. In this paper we present a Support Vector Machine classifier in combination with a modified version of the GrabCut method for the automatic measurement of the area affected by pressure ulcers in digital images.