The intraoperative registration of preoperative CT volumes is a key process of most computer-assisted orthopedic surgery (CAOS) systems. In this work, is reported a new method for automatic registration of long bones, based on the segmentation of the bone cortical in intraoperative 3D ultrasound images. A bone classifier was developed based on features, obtained from the principal component analysis of the Hessian matrix, of every voxel in an intraoperative ultrasound volume. 3D freehand ultrasound was used for the acquisition of the intraoperative ultrasound volumes. Corresponding bone surface segmentations in ultrasound and preoperative CT imaging were used for the intraoperative registration. Validation on a phantom of the tibia produced encouraging results, with a maximum mean segmentation error of 0.34mm (SD=0.26mm) and a registration accuracy error of 0.64mm (SD=0.49mm).
En este trabajo presentamos la aplicación de un método de aprendizaje estadístico tomando como base el clasificador de Bayes, para detectar automáticamente la superficie de los huesos en imágenes de ultrasonido. La detección transoperatoria de los huesos del paciente permite el uso de modelos gráficos preoperatorios, de alta resolución, para guiar al cirujano durante la realización de un procedimiento ortopédico. Como caso de estudio reportamos el análisis de imágenes de ultrasonido y la construcción del modelo preoperatorio de la tibia. La distancia de error media, en la detección de automática de la superficie de la tibia, fue de 0.21 mm con una desviación estándar de 0.17 mm.
IntroductionSARS-CoV-2 infection in Mexico has caused ~2.7 million confirmed cases; around 20%–25% of health workers will be infected by the virus at their workplace, with approximately 4.4% of mortality. High infectivity of SARS-CoV-2 is related with cell entry mechanism, through the ACE receptor. SARS-CoV-2 requires transmembrane protease serine 2 to cleave its spike glycoprotein and ensure fusion of host cell and virus membrane. We propose studying prophylactic treatment with hydroxychloroquine (HCQ) and bromhexine (BHH), which have been shown to be effective in preventing SARS-CoV-2 infection progression when administered in early stages. The aim of this study is to assess the efficacy of HCQ and BHH as prophylactic treatments for SARS-CoV-2 infection in healthy health workers exposed to the virus.Methods and analysisDouble-blind randomised clinical trial, with parallel allocation at a 1:1 ratio with placebo, of low doses of HCQ plus BHH, for 60 days. Study groups will be defined as follows: (1) HCQ 200 mg/day+BHH 8 mg/8 hours versus (2) HCQ placebo plus BHH placebo. Primary endpoint will be efficacy of both interventions for the prevention of SARS-CoV-2 infection, determined by the risk ratio of infected personnel and the absolute risk. At least a 16% reduction in absolute risk is expected between the intervention and placebo groups; a minimum of 20% infection is expected in the placebo group. The sample size calculation estimated a total of 214 patients assigned: two groups of 107 participants each.Ethics and disseminationThis protocol has been approved by the local Medical Ethics Committee (National Institute of Rehabilitation ‘Luis Guillermo Ibarra Ibarra’, approval number INRLGII/25/20) and by the Federal Commission for Protection against Sanitary Risks (COFEPRIS, approval number 203 300 410A0058/2020). The results of the study will be submitted for publication in peer-reviewed journals and disseminated through conferences.Trial registration numberNCT04340349.
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