In the last decades, the use of
Up to 30% of thoracolumbar (TL) fractures are missed in the emergency room. Failure to identify these fractures can result in neurological injuries up to 51% of the casesthis article aimed to clarify the incidence and risk factors of traumatic fractures in China. The China National Fracture Study (CNFS. Obtaining sagittal and anteroposterior radiographs of the TL spine are the first diagnostic step when suspecting a traumatic injury. In most cases, CT and/or MRI are needed to confirm the diagnosis. These are time and resource consuming. Thus, reliably detecting vertebral fractures in simple radiographic projections would have a significant impact. We aim to develop and validate a deep learning tool capable of detecting TL fractures on lateral radiographs of the spine. The clinical implementation of this tool is anticipated to reduce the rate of missed vertebral fractures in emergency rooms. We collected sagittal radiographs, CT and MRI scans of the TL spine of 362 patients exhibiting traumatic vertebral fractures. Cases were excluded when CT and/or MRI where not available. The reference standard was set by an expert group of three spine surgeons who conjointly annotated (fracture/no-fracture and AO Classification) the sagittal radiographs of 171 cases. CT and/or MRI were used confirm the presence and type of the fracture in all cases. 302 cropped vertebral images were labelled “fracture” and 328 “no fracture”. After augmentation, this dataset was then used to train, validate, and test deep learning classifiers based on the ResNet18 and VGG16 architectures. To ensure that the model's prediction was based on the correct identification of the fracture zone, an Activation Map analysis was conducted.Introduction and Objective
Materials and Methods
Gait analysis is an indispensable tool for scientific assessment and treatment of individuals whose ability to walk is impaired. The high cost of installation and operation are a major limitation for wide-spread use in clinical routine. Advances in
Osteoarthritis (OA) is a degenerative joint disease affecting millions worldwide. Early detection of OA and monitoring its progression is essential for effective treatment and for preventing irreversible damage. Although sensors have emerged as a promising tool for monitoring analytes in patients, their application for monitoring the state of pathology is currently restricted to specific fields (such as diabetes). In this study, we present the development of an optical sensor system for real-time monitoring of inflammation based on the measurement of nitric oxide (NO), a molecule highly produced in tissues during inflammation. Single-walled carbon nanotubes (SWCNT) were functionalized with a single-stranded DNA (ssDNA) wrapping designed using an
The objectives of this study are to evaluate the impact of the CoVID-19 pandemic on the development of relevant emerging digital healthcare trends and to explore which digital healthcare trend does the health industry need most to support HCPs. A web survey using 39 questions facilitating Five-Point Likert scales was performed from 1.8.2020 – 31.10.2020. Of 260 participants invited, 90 participants answered the questionnaire. The participants were located in the Hospital/HCP sector in 11.9%, in other healthcare sectors in 22.2%, in the pharmaceutical sector in 11.1%, in the medical device and equipment industry in 43.3%. The Five-Point Likert scales were in all cases fashioned as from 1 (strongly disagree) to 5 (strongly agree). As the top 3 most impacted digital health care trends strongly impacted by CoVID-19, respondents named:. - remote management of patients by telemedicine, mean answer 4.44. - shared data governance under patient control, mean answer 3.80. - new virtual interaction between HCP´s and medical industry, mean answer 3.76. Respondents were asked which level of readiness of the healthcare system currently possess to cope with the current trend impacted by CoVID-19. - Digital and efficient healthcare logistics, mean answer 1.54. - Integrated health care, mean answer 1.73. - Use of big data and
Precision medicine tailoring the patient pathway based on the risk, prognosis, and treatment response may bring benefits to the patients. To identify risk factors contributing to the early failure of treatment (development of events of interest) and when possible to change the prognosis via modifying these factors may improve the outcome and/or lower the risk of complications. There is an emerging goal to identify such parameters in total knee arthroplasty (TKA) thus lower the risk of revision surgery. The goal of this study was to identify factors explaining the risk for early revision of TKA using an