Abstract
To explore the application of self-efficacy in X-ray image analysis based on deep convolutional neural network (DCNN) in the care and treatment of osteoporosis patients with rheumatoid arthritis. In this study, 90 patients with osteoporosis were divided into the control group and the experimental group for DCNN combined with X-ray diagnosis. Patients in the control group were given routine nursing care, and those in the experimental group were given comprehensive nursing care. The bone mineral content, self-efficacy, anxiety, and depression in the femur and lumbar spine after care were compared. The results showed that the accuracy, sensitivity, and false-negative rate of X-ray image recognition of osteoporosis based on DCNN were 91%, 98%, and 2%, respectively. The bone mineral contents of femur and lumbar vertebra in the experimental group were significantly higher than those in the control group ( P < 0.05 ). The anxiety, depression, and self-efficacy scores of patients in the experimental group were significantly higher than those in the control group ( P < 0.05 ). In conclusion, the accuracy rate of DCNN combined with X-ray plain film imaging in the detection of osteoporosis is high. Comprehensive nursing intervention can improve the curative effect and self-efficacy of patients. The improvement of self-efficacy is a related factor for the improvement of patients’ negative emotions and quality of life.
Highlights
In recent years, computer technology has been widely used in the medical field, such as computed tomography (CT) and X-ray imaging technology [1]
It was found that in the experimental group, the bone mineral density T value and the self-efficacy score were higher (P < 0.05), and the T value was at a normal level. is was consistent with the research results of Picha et al [21], indicating that deep convolutional neural network (DCNN)-based X-ray plain film examination has high accuracy in detecting Rheumatoid arthritis (RA) combined with osteoporosis and comprehensive nursing such as social support and exercise guidance can improve patients’ clinical efficacy, positive emotions, and self-efficacy
Patients with rheumatoid arthritis and osteoporosis were selected as the research objects and were diagnosed by DCNN combined with X-ray plain film
Summary
Computer technology has been widely used in the medical field, such as computed tomography (CT) and X-ray imaging technology [1]. Different tissues can absorb different number of rays. Based on this principle, the technology displays three-dimensional images of internal structure of the human body, which provides convenience and basis for clinical diagnosis [2]. CNN is a kind of feedforward neural network, which can effectively reduce the complexity of the feedback neural network. It can identify some distorted but non-deformed two-dimensional images, such as those with displacement, scaling, etc., and is one of the representative algorithms of deep learning [3, 4]. Neural network recognition technology is widely used in image segmentation [5]. With the advancement of deep learning theory and the update of numerical computing equipment, the representation learning ability of convolutional neural networks has attracted extensive attention from all walks of life [10]
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