Automated detection of chronic kidney disease using image fusion and graph embedding techniques with ultrasound images

• Published in: Biomedical signal processing and control 2021-07, Vol.68, p.102733, Article 102733
• Main Authors: Gudigar, Anjan, U, Raghavendra, Samanth, Jyothi, Gangavarapu, Mokshagna Rohit, Kudva, Abhilash, Paramasivam, Ganesh, Nayak, Krishnananda, Tan, Ru-San, Molinari, Filippo, Ciaccio, Edward J., Rajendra Acharya, U.
• Format: Article
• Language: English
• Subjects: Chronic kidney disease; Fusion; Graph embedding; Support vector machine; Ultrasound image
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2021.102733

•Developed an efficient computer-aided diagnosis model to predict chronic kidney disease using ultrasound images.•Four-chamber heart Ultrasound images are employed to predict CKD stages.•Image fusion and graph embedding techniques are utilized.•The proposed method achieved an accuracy of 100 %, and 99.09 % for two-class and multi-class categorization respectively. Chronic Kidney disease (CKD) is a progressive disease affecting more than twenty million individuals in the United States. Disease progression is often characterized by complications such as cardiovascular diseases, anemia, hyperlipidemia and metabolic bone diseases etc., Based on estimated GFR values, the disease is categorized in 5 stages which significantly influence patient outcome. Cardiovascular ultrasound (US) (echocardiography) imagery demonstrate significant hemodynamic alterations that are secondary to CKD in the form of volume/ pressure overload. As the CKD pathology directly impacts cardiovascular disease, the US imaging shows structural and hemodynamic adaptation. Hence, the development of a computer-aided diagnosis (CAD) model to predict CKD would be desirable, and can potentially improve treatment. Several prior studies have utilized kidney features for quantitative analysis. In this paper, acquisition of the four-chamber heart US image is employed to predict CKD stage. The method combines image and feature fusion techniques under a graph embedding framework to characterize heart chamber properties. Moreover, a support vector machine is incorporated to classify heart US images. The proposed method achieved 100 % accuracy for a two-class system, and 99.09 % accuracy for a multi-class categorization scenario. Hence, our proposed CAD tool is deployable in both clinic and hospital settings for computer-aided screening of CKD.