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Article

An Automated Assessment Method for Chronic Kidney Disease–Mineral and Bone Disorder (CKD-MBD) Utilizing Metacarpal Cortical Percentage

1
College of Pharmacy & Health Care, Tajen University, Pingtung 90741, Taiwan
2
Department of Internal Medicine, Kaohsiung Veterans General Hospital Tainan Branch, Tainan City 71051, Taiwan
3
NanoRay Biotech Co., Ltd., Taipei City 11494, Taiwan
4
Department of Biomedical Engineering, National Cheng Kung University, Tainan City 70101, Taiwan
*
Author to whom correspondence should be addressed.
Submission received: 23 April 2024 / Revised: 12 June 2024 / Accepted: 14 June 2024 / Published: 18 June 2024

Abstract

Chronic kidney disease–mineral and bone disorder (CKD-MBD) frequently occurs in hemodialysis patients and is a common cause of osteoporosis. Regular dual-energy X-ray absorptiometry (DXA) scans are used to monitor these patients, but frequent, cost-effective, and low-dose alternatives are needed. This study proposes an automatic CKD-MBD assessment model using histogram equalization and a squeeze-and-excitation block-based residual U-Net (SER-U-Net) with hand diagnostic radiography for preliminary classification. The process involves enhancing image contrast with histogram equalization, extracting features with the SE-ResNet model, and segmenting metacarpal bones using U-Net. Ultimately, a correlation analysis is carried out between the calculated dual metacarpal cortical percentage (dMCP) and DXA T-scores. The model’s performance was validated by analyzing clinical data from 30 individuals, achieving a 93.33% accuracy in classifying bone density compared to DXA results. This automated method provides a rapid, effective tool for CKD-MBD assessment in clinical settings.
Keywords: chronic kidney disease–mineral bone disorder (CKD-MBD); dual-energy X-ray absorptiometry (DXA); squeeze-and-excitation block-based residual U-Net (SER-U-Net); dual metacarpal cortical percentage (dMCP) chronic kidney disease–mineral bone disorder (CKD-MBD); dual-energy X-ray absorptiometry (DXA); squeeze-and-excitation block-based residual U-Net (SER-U-Net); dual metacarpal cortical percentage (dMCP)

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MDPI and ACS Style

Wu, M.-J.; Tseng, S.-C.; Gau, Y.-C.; Ciou, W.-S. An Automated Assessment Method for Chronic Kidney Disease–Mineral and Bone Disorder (CKD-MBD) Utilizing Metacarpal Cortical Percentage. Electronics 2024, 13, 2389. https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122389

AMA Style

Wu M-J, Tseng S-C, Gau Y-C, Ciou W-S. An Automated Assessment Method for Chronic Kidney Disease–Mineral and Bone Disorder (CKD-MBD) Utilizing Metacarpal Cortical Percentage. Electronics. 2024; 13(12):2389. https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122389

Chicago/Turabian Style

Wu, Ming-Jui, Shao-Chun Tseng, Yan-Chin Gau, and Wei-Siang Ciou. 2024. "An Automated Assessment Method for Chronic Kidney Disease–Mineral and Bone Disorder (CKD-MBD) Utilizing Metacarpal Cortical Percentage" Electronics 13, no. 12: 2389. https://0-doi-org.brum.beds.ac.uk/10.3390/electronics13122389

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