Authors:
Vishal Kumar Kanaujia, Awadhesh Kumar, Satya Prakash Yadav

Addresses:
Department of Computer Science and Engineering-Data Science, ABES Engineering College, Ghaziabad, Uttar Pradesh, India. Department of Computer Science and Engineering, Kamla Nehru Institute of Technology, Sultanpur, Uttar Pradesh, India. Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India.

Abstract:

Kidney health is deeply interwoven with general health, and earlier detection of kidney issues leads to better outcomes of treatment. A renal scan is a tool that helps doctors further refine their diagnosis of kidney problems. This diagnostic testing can provide a much clearer, more realistic visual picture of the kidney, thanks to advanced imaging techniques combined with specialized markers. Moreover, this provides an accurate estimate of renal function using DRS and DMSA scintigraphy to detect cortical defects and/or nephropathies. Consequently, it grades the kidney according to functional grades based on various parameters, such as SF, K, and KEi, ranging from a normal kidney with no dysfunctional renal unit to a kidney with severely compromised or dead segments. Renal Scan. These days, diagnostic techniques are insufficient to detect renal damage at an early stage. Renal scan fills this gap by ensuring the application of predictive models alongside precision segmentation, thereby providing timely diagnosis. It provides comprehensive information on kidney function through a Renal Scan, which helps the doctor better manage kidney-related issues and thereby reduce the risk of cardiac complications, including death. The ultimate aim of such a detail-oriented approach is to reduce the death rate and provide the best possible long-term treatment to patients with chronic kidney illness by providing accurate, quantifiable information about kidney function through Renal Scan, thereby enhancing kidney-associated care.

Keywords: Kidney Health; Dynamic Renal Scintigraphy (DRS); Nephropathy Detection; Predictive Segmentation; Image Processing; Automated Diagnosis; Treatment Outcomes.

Received on: 24/02/2025, Revised on: 21/04/2025, Accepted on: 26/07/2025, Published on: 11/01/2026

DOI: 10.69888/FTSCS.2026.000608

FMDB Transactions on Sustainable Computing Systems, 2026 Vol. 4 No. 1, Pages: 35-51

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