Lesions of the distal deep digital flexor tendon (DDFT) are frequently diagnosed using MRI in horses with foot pain. Intralesional injection of biologic therapeutics shows promise in tendon healing; however, accurate injection of distal deep digital flexor tendon lesions within the hoof is difficult. The aim of this experimental study was to evaluate accuracy of a technique for injection of the deep digital flexor tendon within the hoof using MRI-guidance, which could be performed in standing patients. We hypothesized that injection of the distal deep digital flexor tendon within the hoof could be accurately guided using open low-field MRI to target either the lateral or medial lobe at a specific location. Ten cadaver limbs were positioned in an open, low-field MRI unit. Each distal deep digital flexor tendon lobe was assigned to have a proximal (adjacent to the proximal aspect of the navicular bursa) or distal (adjacent to the navicular bone) injection. A titanium needle was inserted into each tendon lobe, guided by T1-weighted transverse images acquired simultaneously during injection. Colored dye was injected as a marker and postinjection MRI and gross sections were assessed. The success of injection as evaluated on gross section was 85% (70% proximal, 100% distal). The success of injection as evaluated by MRI was 65% (60% proximal, 70% distal). There was no significant difference between the success of injecting the medial versus lateral lobe. The major limitation of this study was the use of cadaver limbs with normal tendons. The authors conclude that injection of the distal deep digital flexor tendon within the hoof is possible using MRI guidance.
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Sci Rep
December 2024
Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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December 2024
Department of Computer Science and Digital Technologies, University of East London, London, UK.
Nursing activity recognition has immense importance in the development of smart healthcare management and is an extremely challenging area of research in human activity recognition. The main reasons are an extreme class-imbalance problem and intra-class variability depending on both the subject and the recipient. In this paper, we apply a unique two-step feature extraction, coupled with an intermediate feature 'Angle' and a new feature called mean min max sum to render the features robust against intra-class variation.
View Article and Find Full Text PDFBMJ Neurol Open
December 2024
Institute for Health Services Research and Clinical Epidemiology, Philipps University Marburg, Marburg, Germany.
Introduction: People with Parkinson's disease (PwPD) experience a wide range of motor and non-motor symptoms that have a significant impact on their health and quality of life. Effective care management for PwPD involves monitoring symptoms at home, involving specialised multidisciplinary care providers and enhancing self-management skills. This study protocol describes the process evaluation within a randomised clinical trial to assess the implementation and its impact on patient health outcomes of ParkProReakt-a proactive, multidisciplinary, digitally supported care model for community-dwelling PwPD.
View Article and Find Full Text PDFFront Endocrinol (Lausanne)
December 2024
Department of Pediatric Diabetes and Endocrinology, Clinique Pédiatrique, Centre Hospitalier, Luxembourg, Luxembourg.
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Netw Neurosci
December 2024
Retired Professor, The University of Melbourne, Victoria, Australia.
Several recent studies have optimized deep neural networks to learn high-dimensional relationships linking structural and functional connectivity across the human connectome. However, the extent to which these models recapitulate individual-specific characteristics of resting-state functional brain networks remains unclear. A core concern relates to whether current individual predictions outperform simple benchmarks such as group averages and null conditions.
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