Artificial Intelligence for Chronic Kidney Disease Early Detection and Prognosis
Description
Abstract: The integration of Artificial Intelligence (AI) in the early detection and prognosis of Chronic Kidney Disease (CKD) is revolutionizing nephrology by providing enhanced diagnostic accuracy and improved patient outcomes. This research explores the transformative role of AI, particularly machine learning (ML) and deep learning (DL), in predicting CKD progression and facilitating timely interventions. AI-driven models analyze diverse patient data, including imaging, laboratory results, and genetic information, to identify subtle patterns often overlooked by traditional methods. These technologies allow for earlier identification of kidney dysfunction, potentially slowing disease progression and increasing life expectancy. The study highlights the importance of using ensemble learning techniques and feature selection methods to refine AI models, improving their predictive capabilities. Furthermore, the research emphasizes the potential of AI to support clinical decision-making by offering objective, data-driven risk assessments, which are crucial in the personalized management of CKD. The use of convolutional neural networks (CNNs) in renal imaging has shown promise in detecting early-stage kidney damage, while support vector machines (SVM) and artificial neural networks (ANN) have demonstrated high accuracy in diagnosing CKD. The growing integration of AI into healthcare workflows is expected to reduce diagnostic delays, enhance prognostic evaluations, and optimize treatment strategies. This study also discusses the interdisciplinary collaboration between medicine, computer science, and engineering, which is essential for advancing AI applications in CKD. With the increasing availability of high-quality data and computational tools, AI is poised to play a central role in transforming CKD management, offering a proactive approach to care that can lead to better patient outcomes and healthcare resource efficiency. Keyword: Artificial Intelligence (AI), Chronic Kidney Disease (CKD), Early Detection, Prognosis, Machine Learning, Diagnosis
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The methodology for this research follows a systematic approach to analyze the use of Artificial Intelligence (AI) in the early detection and prognosis of Chronic Kidney Disease (CKD). The study begins with a comprehensive literature search using Scopus, where the search string is specifically tailored to find articles related to AI techniques like machine learning and deep learning applied to CKD. The search parameters are set to include documents published between 2021 and 2026 in English and focused on peer-reviewed journal articles (ar). This search yields a dataset of 534 relevant documents that are further analyzed. To ensure a rigorous and transparent review of the literature, the study adopts the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework, which provides a structured method for systematic review and meta-analysis. The PRISMA framework aids in identifying, screening, and selecting relevant studies while minimizing bias. After the initial filtering of the documents, a deeper analysis of their content is conducted to assess the methodologies, AI models, and outcomes related to CKD detection and prognosis. For bibliometric analysis, the study uses VOSviewer to visualize the co-occurrence of keywords, authors, and citations, providing insights into trends and network structures within the field. Additionally, Harzing's Publish or Perish is used to evaluate the citation metrics of the most impactful studies in this domain, helping to identify key papers and influential authors. Finally, the data is organized and analyzed using MS Excel, where trends in AI application for CKD diagnosis and prognosis are mapped, categorized, and compared. This methodology ensures a thorough and objective analysis of current research trends, AI tools, and outcomes, contributing valuable insights to the field of CKD early detection and prognosis.
Institutions
- Cyberjaya University College of Medical Sciences