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2000
Volume 20, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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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 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.

© 2024 The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2024-01-01
2025-07-15

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/content/journals/cmir/10.2174/0115734056295090240329051158
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The Value of Computer Vision in Identifying the Bone of Dialysis Patients
Curr. Med. Imaging 20, e15734056295090 (2024); https://doi.org/10.2174/0115734056295090240329051158
/content/journals/cmir/10.2174/0115734056295090240329051158
/content/journals/cmir/10.2174/0115734056295090240329051158
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  • Article Type:     Research Article
Keyword(s): Computer vision; Convolution neural networks; Dialysis; Kidney disease; Osteoporosis; ResNet

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