Abstract
The chronic pain of knee osteoarthritis in the elderly is investigated in detail in this paper, as well as the complexity of chronic pain utilising neuroimaging recognition techniques. Chronic pain in knee osteoarthritis (KOA) has a major effect on patients' quality of life and functional activities; therefore, understanding the causes of KOA pain and the analgesic advantages of different therapies is important. In recent years, neuroimaging techniques have become increasingly important in basic and clinical pain research. Thanks to the application and development of neuroimaging techniques in the study of chronic pain in KOA, researchers have found that chronic pain in KOA contains both injury-receptive and neuropathic pain components. The neuropathic pain mechanism that causes KOA pain is complicated, and it may be produced by peripheral or central sensitization, but it has not gotten enough attention in clinical practice, and there is no agreement on how to treat combination neuropathic pain KOA. As a result, using neuroimaging techniques such as magnetic resonance imaging (MRI), electroencephalography (EEG), magnetoencephalography (MEG), and near-infrared spectroscopy (NIRS), this review examines the changes in brain pathophysiology-related regions caused by KOA pain, compares the latest results in pain assessment and prediction, and clarifies the central brain analgesic mechanistic. The capsule network model is introduced in this paper from the perspective of deep learning network structure to construct an information-complete and reversible image low-level feature bridge using isotropic representation, predict the corresponding capsule features from MRI voxel responses, and then, complete the accurate reconstruction of simple images using inverse transformation. The proposed model improves the structural similarity index by about 10%, improves the reconstruction performance of low-level feature content in simple images by about 10%, and achieves feature interpretation and analysis of low-level visual cortical fMRI voxels by visualising capsule features, according to the experimental results.
Highlights
80% of the information that humans obtain from the outside world comes from vision; vision plays an irreplaceable and crucial role in the process of knowing, understanding, and transforming the external world
Recent studies have shown that knee inward moment magnitude is influenced by altered trunk motion in addition to significant hip abductor muscle weakness in KOA patients, which has been suggested as a risk factor for the development of knee osteoarthritis, and our results suggest that there is lateralization of the pelvis during the support phase of walking, with some findings suggesting that lateral pelvic tilt may increase the magnitude of the internal knee moment; other research teams have suggested that hip abductor muscle strength has little effect on the internal hip and knee moments in gait, and the results of our subsequent regression analysis may explain the existence of this contradictory result
The diagnostic approach of correctly identifying pain components in KOA patients using neuroimaging methods has not been widely used in clinical practice, which may be due to the complexity of index processing and the high cost of neuroimaging examinations
Summary
80% of the information that humans obtain from the outside world comes from vision; vision plays an irreplaceable and crucial role in the process of knowing, understanding, and transforming the external world. The eye, retina, optic nerve, lateral geniculate body, and visual cortex of the brain make up the human visual system, which is an essential component of the nervous system. The eye projects visible picture information onto the retina, which is translated into electrical impulses and transferred from the optic nerve to the brain’s visual cortex through the lateral geniculate body [1]. The processing and management of visual information in the visual cortex, which is made up of numerous nerve cells, is based on very complicated neural activity. The absence of a large-scale, systematic, crosspopulation epidemiological study of bone and joint health has posed a number of difficulties to disease’s treatment. It is critical to address the disease burden issues based on patient requirements, so that more patients may benefit from standardised therapy, enhance bone and joint health, and improve their quality of life
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