RESUMEN
Low back pain is a painful disorder that prevents normal mobilization, increases muscle tension and whose first-line treatment is usually non-steroidal anti-inflammatory drugs, together with non-invasive manual therapies, such as deep oscillation therapy. This systematic review aims to investigate and examine the scientific evidence of the effectiveness of deep oscillation therapy in reducing pain and clinical symptomatology in patients with low back pain, through the use of motion capture technology. To carry out this systematic review, the guidelines of the PRISMA guide were followed. A literature search was performed from 2013 to March 2022 in the PubMed, Elsevier, Science Director, Cochrane Library, and Springer Link databases to collect information on low back pain, deep oscillation, and motion capture. The risk of bias of the articles was assessed using the Cochrane risk of bias tool. Finally, they were included 16 articles and 5 clinical trials which met the eligibility criteria. These articles discussed the effectiveness of deep oscillation therapy in reducing pain, eliminating inflammation, and increasing lumbar range of motion, as well as analyzing the use of motion capture systems in the analysis, diagnosis, and evaluation of a patient with low back pain before, during and after medical treatment. There is no strong scientific evidence that demonstrates the high effectiveness of deep oscillation therapy in patients with low back pain, using motion capture systems. This review outlines the background for future research directed at the use of deep oscillation therapy as a treatment for other types of musculoskeletal injuries.
Asunto(s)
Dolor de la Región Lumbar , Rango del Movimiento Articular , Humanos , Dolor de la Región Lumbar/terapia , Rango del Movimiento Articular/fisiología , Modalidades de Fisioterapia , Captura de MovimientoRESUMEN
Melanoma is the most aggressive and prevalent form of skin cancer globally, with a higher incidence in men and individuals with fair skin. Early detection of melanoma is essential for the successful treatment and prevention of metastasis. In this context, deep learning methods, distinguished by their ability to perform automated and detailed analysis, extracting melanoma-specific features, have emerged. These approaches excel in performing large-scale analysis, optimizing time, and providing accurate diagnoses, contributing to timely treatments compared to conventional diagnostic methods. The present study offers a methodology to assess the effectiveness of an AlexNet-based convolutional neural network (CNN) in identifying early-stage melanomas. The model is trained on a balanced dataset of 10,605 dermoscopic images, and on modified datasets where hair, a potential obstructive factor, was detected and removed allowing for an assessment of how hair removal affects the model's overall performance. To perform hair removal, we propose a morphological algorithm combined with different filtering techniques for comparison: Fourier, Wavelet, average blur, and low-pass filters. The model is evaluated through 10-fold cross-validation and the metrics of accuracy, recall, precision, and the F1 score. The results demonstrate that the proposed model performs the best for the dataset where we implemented both a Wavelet filter and hair removal algorithm. It has an accuracy of 91.30%, a recall of 87%, a precision of 95.19%, and an F1 score of 90.91%.
RESUMEN
Low back pain (LBP) is a common issue that negatively affects a person's quality of life and imposes substantial healthcare expenses. In this study, we introduce the (Back-pain Movement) BackMov test, using inertial motion capture (MoCap) to assess lumbar movement changes in LBP patients. The test includes flexion-extension, rotation, and lateralization movements focused on the lumbar spine. To validate its reproducibility, we conducted a test-retest involving 37 healthy volunteers, yielding results to build a minimal detectable change (MDC) graph map that would allow us to see if changes in certain variables of LBP patients are significant in relation to their recovery. Subsequently, we evaluated its applicability by having 30 LBP patients perform the movement's test before and after treatment (15 received deep oscillation therapy; 15 underwent conventional therapy) and compared the outcomes with a specialist's evaluations. The test-retest results demonstrated high reproducibility, especially in variables such as range of motion, flexion and extension ranges, as well as velocities of lumbar movements, which stand as the more important variables that are correlated with LBP disability, thus changes in them may be important for patient recovery. Among the 30 patients, the specialist's evaluations were confirmed using a low-back-specific Short Form (SF)-36 Physical Functioning scale, and agreement was observed, in which all patients improved their well-being after both treatments. The results from the specialist analysis coincided with changes exceeding MDC values in the expected variables. In conclusion, the BackMov test offers sensitive variables for tracking mobility recovery from LBP, enabling objective assessments of improvement. This test has the potential to enhance decision-making and personalized patient monitoring in LBP management.