Prediction of chronic kidney disease stages by renal ultrasound imaging

ABSTRACT

To detect chronic kidney disease (CKD) at earlier stages, diagnosis through non-invasive ultrasonographic imaging techniques provides an auxiliary clinical approach for at-risk CKD patients. We have established a detection method based on imaging processing techniques and machine learning approaches for the diagnosis of different CKD stages. Decisive area-proportional and textural features and support-vector-machine techniques were applied for efficient and effective analyses. Several clustered collections of CKD patients were evaluated and compared according to the estimated glomerular filtration rates. Based on the findings of evolving changes from ultrasound images, the proposed approach could be used as complementary evidences to help differentiate between different clinical diagnoses.

KEYWORDS:

• Ultrasonography
• support vector machine
• feature extraction
• chronic kidney disease
• estimated glomerular filtration rate(eGFR)

Acknowledgments

The project is supported by National Taiwan Ocean University (NTOU 99529001K to T.W. Pai) and Keelung Chang-Gung Memorial Hospital (CMRPG 290191 to C.H. Lee and Y.C. Chen).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by National Taiwan Ocean University and National Taipei University of Technology [NTUT-MMH-108-02]; Chang-Gung Memorial Hospital [CMRPG 290191].