| Title: |
Exploration of a Framework for the Identification of Chronic Kidney Disease Based on 2D Ultrasound Images: A Survey |
| Authors: |
Alex, Deepthy Mary; Chandy, D. Abraham |
| Source: |
Current Medical Imaging Formerly Current Medical Imaging Reviews ; volume 17, issue 4, page 464-478 ; ISSN 1573-4056 |
| Publisher Information: |
Bentham Science Publishers Ltd. |
| Publication Year: |
2021 |
| Description: |
Background: Chronic kidney disease (CKD) is a fatal disease that ultimately results in kidney failure. The primary threat is the aetiology of CKD. Over the years, researchers have proposed various techniques and methods to detect and diagnose the disease. The conventional method of detecting CKD is the determination of the estimated glomerular filtration rate by measuring creatinine levels in blood or urine. Conventional methods for the detection and classification of CKD are tedious; therefore, several researchers have suggested various alternative methods. Recently, the research community has shown keen interest in developing methods for the early detection of this disease using imaging modalities such as ultrasound, magnetic resonance imaging, and computed tomography. Discussion: The study aimed to conduct a systematic review of various existing techniques for the detection and classification of different stages of CKD using 2D ultrasound imaging of the kidney. The review was confined to 2D ultrasound images alone, considering the feasibility of implementation even in underdeveloped countries because 2D ultrasound scans are more cost effective than other modalities. The techniques and experimentation in each work were thoroughly studied and discussed in this review. Conclusion: This review displayed the cutting-age research, challenges, and possibilities of further research and development in the detection and classification of CKD. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.2174/1573405616666200923162600 |
| Availability: |
https://doi.org/10.2174/1573405616666200923162600; https://eurekaselect.com/article/download/186230; https://www.eurekaselect.com/186230/article |
| Accession Number: |
edsbas.7FF89CF1 |
| Database: |
BASE |