Evaluating The Role Of Deep Learning In Enhancing Renal Health Assessment: A Comprehensive Review
DOI:
https://doi.org/10.65327/kidneys.v15i1.596Keywords:
Deep Learning; Renal Health Assessment; Chronic Kidney Disease; Artificial Intelligence in NephrologyAbstract
Kidney diseases, including chronic kidney disease (CKD) and acute kidney injury (AKI), pose a substantial global health burden and are associated with high morbidity, mortality, and healthcare costs. Conventional methods for renal health assessment are limited by delayed detection, inter-observer variability, and restricted capacity to integrate complex, longitudinal data. Recent advances in deep learning have created new opportunities to enhance renal disease assessment through data-driven, automated, and scalable approaches. This review aims to evaluate the role of deep learning in improving renal health assessment across structural, functional, and clinical domains, with a focus on its potential to support early diagnosis, risk stratification, and personalized renal care. A comprehensive narrative review of the literature was conducted, focusing on peer-reviewed studies that apply deep learning techniques to renal imaging, functional assessment, disease classification, renal replacement therapy, and transplantation. Key challenges related to data quality, interpretability, ethical considerations, and clinical implementation were also examined. Deep learning has demonstrated strong performance in structural renal assessment, including kidney segmentation, cyst quantification, and tumor classification. Functional applications include early prediction of AKI, estimation of renal function decline, and imaging-based glomerular filtration rate assessment. Integration with electronic health records has enabled improved disease classification, risk stratification, and outcome prediction. Emerging applications in dialysis and transplantation show promise for optimizing advanced renal care. Deep learning offers significant potential to enhance renal health assessment and clinical decision-making. Continued advances in explainability, data integration, and ethical deployment will be critical for successful clinical translation and widespread adoption in nephrology practice.
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