Deep Learning-Based Self-Efficacy X-Ray Images in the Evaluation of Rheumatoid Arthritis Combined with Osteoporosis Nursing.

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.