Precision Dialysis: Leveraging Big Data and Artificial Intelligence

Document Type

Journal Article

Publication Date

9-1-2024

Journal

Kidney medicine

Volume

6

Issue

9

DOI

10.1016/j.xkme.2024.100868

Keywords

Dialysis; artificial intelligence; hemodialysis; machine learning; precision dialysis

Abstract

The long-term mortality of patients with kidney failure remains unacceptably high. There are a multitude of reasons for the unfavorable status quo of dialysis care, such as the inadequate and suboptimal pattern of uremic toxin removal resulting in a metabolic and hemodynamic "roller coaster" induced by thrice-weekly in-center hemodialysis. Innovation in dialysis delivery systems is needed to build an adaptive and self-improving process to change the status quo of dialysis care with the aim of transforming it from being reactive to being proactive. The introduction of more physiologic and smart dialysis systems using artificial intelligence (AI) incorporating real-time data into the process of dialysis delivery is a realistic target. This would enable machine learning from both individual and collective patient treatment data. This has the potential to shift the paradigm from the practice of population-driven, evidence-based data to precision medicine. In this review, we describe the different components of an AI system, discuss the studied applications of AI in the field of dialysis, and outline parameters that can be used for future smart, adaptive dialysis delivery systems. The desired output is precision dialysis; a self-improving process that has the ability to prognosticate and develop instant and individualized predictive models.

Department

Medicine

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