Validity and reliability of the full cup test in patients with chronic low back pain.

OBJECTIVES: This study aims to determine the validity and reliability of the full cup test (FCT), evaluating the pain severity in patients with chronic low back pain. METHODS: A total of 100 patients (70 women and 30 men) aged over 18 years with mechanical low back pain were enrolled in the study. Demographic and clinical characteristics were recorded. Pain severity was evaluated using the visual analog scale (VAS) and FCT, the functional state was assessed by the Oswestry Disability Index (ODI), and the quality of life was assessed using the Nottingham Health Profile (NHP). FCT was performed on the 1st day by two independent observers (G1 and G2) and 3 days after the first application, patients were readministered the FCT by G1. RESULTS: The mean age of participants was 56.04±12.33 years and mean body mass index was 27.7±4.3 kg/m². The reliability of the FCT and intraclass correlation coefficient (ICC) was found to be 0.989 for intrarater compliance, ICC was found to be 0.984 for inter-rater compliance, and Cronbach's alpha reliability coefficient was α=0.994. External construct validity of the scale was confirmed with expected correlations with all subgroups of NHP except for social isolation, VAS and ODI (p<0.01). CONCLUSION: This study concludes that the FCT provides a reliable and valid instrument for measuring pain severity and loss of the function in patients with chronic mechanical back pain. We consider that FCT is a simple and easy test in patients with low education and advanced age.

Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods.

OBJECTIVE: The incidence of osteoarthritis is gradually increasing in public due to aging and increase in obesity. Various imaging methods are used in the diagnosis of hip osteoarthritis, and plain pelvic radiography is the first preferred imaging method in the diagnosis of hip osteoarthritis. In this study, we aimed to develop a computer-aided diagnosis method that will help physicians for the diagnosis of hip osteoarthritis by interpreting plain pelvic radiographs. MATERIALS AND METHODS: In this retrospective study, convolutional neural networks were used and transfer learning was applied with the pre-trained VGG-16 network. Our dataset consisted of 221 normal hip radiographs and 213 hip radiographs with osteoarthritis. In this study, the training of the network was performed using a total of 426 hip osteoarthritis images and a total of 442 normal pelvic images obtained by flipping the raw data set. RESULTS: Training results were evaluated with performance metrics such as accuracy, sensitivity, specificity, and precision calculated by using the confusion matrix. We achieved accuracy, sensitivity, specificity and precision results at 90.2%, 97.6%, 83.0%, and 84.7% respectively. CONCLUSION: We achieved promising results with this computer-aided diagnosis method that we tried to develop using convolutional neural networks based on transfer learning. This method can help clinicians for the diagnosis of hip osteoarthritis while interpreting plain pelvic radiographs, also provides assistance for a second objective interpretation. It may also reduce the need for advanced imaging methods in the diagnosis of hip osteoarthritis.