Abstract
Background: In the end stage of kidney disease, abnormal levels of blood calcium, phosphorus, and parathyroid hormone lead to bone metabolism disorders, manifesting as osteoporosis or fibrocystic osteoarthritis. X-ray, CT, and MR are useful for detecting bone lesions in dialysis patients, but currently, computer vision has not yet been used for this purpose.
Methods: ResNet is a powerful deep CNN model, which has not yet been used to distinguish between the bones of dialysis patients and healthy people. Therefore, this study aimed to investigate the ability of the Resnet50 model to identify the bone of dialysis patients from normal bone.
Results: CT images of 200 cases (100 dialysis patients and 100 healthy people aged 31-72 years with male:female ratio of 51:49) were randomly divided into the training and testing groups at the ratio of 8:2. The module of ‘torch’ was used to train the model of Resnet50 for the current task of image classification. In the test cohort, the accuracy, sensitivity, and specificity with hyper-parameter=0 were 60%, 65%, and 55%, respectively. When the hyper-parameter was 0.6 or 0.7 versus 0, the accuracy was significantly higher (P<0.05). When the hyper-parameter was another number, the accuracy was not significantly different from that with no hyper-parameter (P>0.05).
Conclusion: This study has indicated computer vision to be suitable for identifying bone changes caused by dialysis; a hyper-parameter has been found necessary for improving model accuracy. The ResNet50 model with hyper-parameter = 0.7 has exhibited 90% sensitivity in identifying the bone of dialysis patients.
Keywords: Dialysis, Osteoporosis, Computer vision, Convolution neural networks, ResNet, Kidney disease.
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