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Related Concept Videos

Chronic Kidney Disease III: Interprofessional Care01:28

Chronic Kidney Disease III: Interprofessional Care

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Chronic kidney disease (CKD) requires collaborative and comprehensive management. CKD progresses through stages and can lead to end-stage kidney disease (ESKD) if untreated. Interprofessional collaboration and patient education are crucial, enabling patients to manage their health and improve their quality of life.Diagnostic approach for chronic kidney diseaseThe diagnosis of CKD primarily focuses on the glomerular filtration rate (GFR), which assesses kidney function by measuring how well...
243
Acute Kidney Injury IV: Diagnostic Studies and Prevention01:30

Acute Kidney Injury IV: Diagnostic Studies and Prevention

184
Accurate diagnosis and effective prevention are critical in managing Acute Kidney Injury (AKI), which is linked to high mortality rates ranging from 10% to 80%. Timely recognition of at-risk patients and careful monitoring can significantly reduce the likelihood of kidney damage.Diagnostic Assessments:The diagnostic process starts with a comprehensive medical history to identify prerenal, intrarenal, and postrenal causes.Prerenal causes, such as dehydration, hypotension, or blood loss, should...
184
Chronic Kidney Disease I: Introduction01:25

Chronic Kidney Disease I: Introduction

398
Chronic Kidney Disease (CKD) arises when the kidneys progressively lose their ability to function, ultimately leading to end-stage renal disease. At this advanced stage, the kidneys can no longer filter waste or maintain essential body functions, requiring renal replacement therapy (RRT) through dialysis or a kidney transplant for survival.Early-stage chronic kidney disease and detection challengesIn CKD's early stages, symptoms often remain absent because healthy nephrons compensate for...
398
Acute Kidney Injury V: Interprofessional Care01:20

Acute Kidney Injury V: Interprofessional Care

174
Acute Kidney Injury (AKI) requires a collaborative healthcare approach to restore renal function and prevent complications. Essential management strategies involve monitoring fluid and electrolyte balance, adjusting medications, initiating dialysis when necessary, and providing nutritional support.Fluid and Electrolyte ManagementFluid Monitoring: Regularly monitoring body weight, central venous pressure, and urine output helps detect fluid imbalances early. Patient intake and output are...
174
Kidney Transplant I: Introduction01:28

Kidney Transplant I: Introduction

205
A kidney transplant is a surgical approach that involves replacing a non-functioning kidney with a healthy one from a donor. This procedure is often a treatment option for end-stage renal disease (ESRD) patients. The method requires careful recipient selection, including evaluating various medical and psychosocial factors. These criteria vary between transplant centers but generally include assessments of the patient's overall health, adherence to medical recommendations, and lifestyle...
205
Acute Kidney Injury I: Introduction01:22

Acute Kidney Injury I: Introduction

362
Introduction:Acute Kidney Injury (AKI) describes a swift decrease in kidney function occurring over hours to days, characterized by the kidneys' failure to remove waste products from the bloodstream. This leads to dangerous complications like metabolic acidosis, fluid overload, and electrolyte imbalances, such as hyperkalemia, which can cause life-threatening arrhythmias. AKI is common in both hospital and outpatient settings, often triggered by dehydration, sepsis, or exposure to nephrotoxic...
362

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Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform
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Artificial intelligence enabled applications in kidney disease.

Sheetal Chaudhuri1,2, Andrew Long2, Hanjie Zhang3

  • 1Maastricht University Medical Center, Maastricht, The Netherlands.

Seminars in Dialysis
|September 14, 2020
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) offers new ways to manage kidney disease by predicting patient outcomes and aiding treatment decisions. While still developing, AI aims to support nephrologists in delivering personalized patient care.

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Area of Science:

  • Nephrology
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) represents an evolution of statistical methods, with growing applications in healthcare.
  • AI adoption is widespread in ophthalmology and cardiology but limited in nephrology research.
  • Kidney disease research has seen minimal reporting on AI applications.

Purpose of the Study:

  • To review the current state of AI in kidney disease research.
  • To explore future directions for AI in nephrology.
  • To categorize AI clinical applications in end-stage kidney disease and dialysis.

Main Methods:

  • Categorization of AI applications into three main areas: prediction, treatment/decision support, and pattern identification.
  • Review of existing literature on AI in nephrology.
  • Discussion of challenges and future prospects.

Main Results:

  • AI applications in nephrology include predicting mortality and hospitalization, automating drug prescriptions, and identifying patient phenotypical clusters or arteriovenous fistula aneurysms.
  • Current AI predictive models for kidney disease are in early stages, requiring more evidence for clinical value.
  • Implementation of AI in clinical settings necessitates addressing policy and regulatory frameworks.

Conclusions:

  • AI is poised to assist nephrologists, not replace them, in enhancing personalized patient care.
  • Further research and validation are crucial for integrating AI into routine nephrology practice.
  • Addressing regulatory and policy aspects is essential for the safe and effective deployment of AI in kidney disease management.