Objective:To study the clinical efficacy of ultrasound-guided radiofrequency acupotomy in early and middle-stage knee osteoarthritis (KOA).Methods:A total of 62 patients with KOA were enrolled and then randomly divided into the radiofrequency acupuncture group and the control group. The two groups were treated with radiofrequency acupotomy and conventional acupotomy under ultrasound guidance, respectively. The treatments were conducted once a week, twice in total. The Western Ontario and McMaster Universities Arthritis Index (WOMAC) of all the patients was evaluated before the treatment as well as the day, 2 weeks, and 1 month after the treatment.Results:Before the treatment, the differences between the two groups in gender, age, body mass index (BMI), WOMAC pain score, WOMAC stiffness score, WOMAC function score, and WOMAC total score were not statistically significant (all P>0.05), indicating the two groups were comparable. On the day, 2 weeks, and 1 month after the treatment, the above WOMAC scores of the two groups were lower than those before the treatment, and the differences were statistically significant (all P<0.01). The WOMAC scores of the radiofrequency acupotomy group were lower than those of the control group at the same period, and the differences were statistically significant (all P<0.05). Conclusions:For patients with early and middle-stage KOA, ultrasound-guided radiofrequency acupotomy therapy has proven clinical efficacy in relieving pain and improving knee joint function.

BACKGROUND:Based on different algorithms of machine learning,how to carry out clinical research on lumbar disc herniation with the help of various algorithmic models has become a trend and hot spot in the development of intelligent medicine at present. OBJECTIVE:To review the characteristics of different algorithmic models of machine learning in the diagnosis and treatment of lumbar disc herniation,and summarize the respective advantages and application strategies of algorithmic models for the same purpose. METHODS:The computer searched PubMed,Web of Science,EMBASE,CNKI,WanFang,VIP and China Biomedical(CBM)databases to extract the relevant articles on machine learning in the diagnosis and treatment of lumbar disc herniation.Finally,96 articles were included for analysis. RESULTS AND CONCLUSION:(1)Different algorithm models of machine learning provide intelligent and accurate application strategies for clinical diagnosis and treatment of lumbar disc herniation.(2)Traditional statistical methods and decision trees in supervised learning are simple and efficient in exploring risk factors and establishing diagnostic and prognostic models.Support vector machine is suitable for small data sets with high-dimensional features.As a nonlinear classifier,it can be applied to the recognition,segmentation and classification of normal or degenerative intervertebral discs,and to establish diagnostic and prognostic models.Ensemble learning can make up for the shortcomings of a single model.It has the ability to deal with high-dimensional data and improve the precision and accuracy of clinical prediction models.Artificial neural network improves the learning ability of the model,and can be applied to intervertebral disc recognition,classification and making clinical prediction models.On the basis of the above uses,deep learning can also optimize images and assist surgical operations.It is the most widely used model with the best performance in the diagnosis and treatment of lumbar disc herniation.The clustering algorithm in unsupervised learning is mainly used for disc segmentation and classification of different herniated segments.However,the clinical application of semi-supervised learning is relatively less.(3)At present,machine learning has certain clinical advantages in the identification and segmentation of lumbar intervertebral discs,classification and grading of the degenerative intervertebral discs,automatic clinical diagnosis and classification,construction of the clinical predictive model and auxiliary operation.(4)In recent years,the research strategy of machine learning has changed to the neural network and deep learning,and the deep learning algorithm with stronger learning ability will be the key to realizing intelligent medical treatment in the future.