Authors:
S. Rubin Bose, J. Angelin Jeba, R. Regin, S. Tarunraj, Edwin Shalom Soji, S. Suman Rajest

Addresses:
Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Electronics and Communication Engineering, S.A. Engineering College, Chennai, Tamil Nadu, India. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India. Department of Research and Development & International Student Affairs, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India.

Abstract:

Chronic kidney disease (CKD) is a progressive condition affecting over 12% of the global population and can lead to end-stage renal disease (ESRD) if not managed effectively. Early prediction of renal failure in CKD patients is crucial for timely intervention, which can slow or prevent disease progression and reduce the need for costly treatments like dialysis or transplantation. However, traditional diagnostic methods, such as serum creatinine tests and nuclear imaging, often lack precision and are invasive. This paper examines the application of optimized deep learning models, particularly those utilizing computed tomography (CT) imaging, for the early detection of renal failure. Key approaches include AI-assisted segmentation of renal-enhanced CT images and advanced models like YOLOv8, which have shown promise in accurately identifying kidney abnormalities and assessing risks. By leveraging these cutting-edge technologies, the goal is to improve early detection, enhance patient outcomes, and reduce healthcare costs, addressing the global burden of CKD. The proposed YOLOv8 model obtained a precision up to 0.986, recall up to 0.969, mAP50 up to 0.989, and mAP50-95 up to 0.972 across BOX and MASK predictions.

Keywords: Chronic Kidney Disease; Kidney Diagnostics; Machine Learning; Renal Segmentation; Non-Invasive Diagnosis; Healthcare Systems; Artificial Intelligence; Medical Imaging; Kidney Health.

Received on: 28/03/2024, Revised on: 02/06/2024, Accepted on: 27/07/2024, Published on: 01/09/2024

DOI: 10.69888/FTSHSL.2024.000251

FMDB Transactions on Sustainable Health Science Letters, 2024 Vol. 2 No. 3, Pages: 152-163

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