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Related Concept Videos

Peritoneal Dialysis II: Peritoneal Dialysis Systems and Complications01:25

Peritoneal Dialysis II: Peritoneal Dialysis Systems and Complications

Peritoneal dialysis (PD) is a medical process that removes waste products and excess fluid from the body using the peritoneal membrane as a natural filter.Peritoneal Dialysis MethodsSeveral methods can be used for peritoneal dialysis, including Acute Intermittent Peritoneal Dialysis, Continuous Ambulatory Peritoneal Dialysis, and Automated Peritoneal Dialysis, also known as Continuous Cyclic Peritoneal Dialysis.Acute Intermittent Peritoneal Dialysis (AIPD) is used for patients with uremic...
Peritoneal Dialysis I: Introduction and Procedure01:30

Peritoneal Dialysis I: Introduction and Procedure

Peritoneal dialysis (PD) is a procedure that facilitates the exchange of solutes, waste products, electrolytes, and excess fluid between the blood in the peritoneal capillaries and a dialysis solution introduced into the peritoneal cavity.Principles of Peritoneal Dialysis (PD)Diffusion: Waste products such as urea and electrolytes move from high concentrations in the blood to low concentrations in the dialysate across the peritoneal membrane. This mechanism is driven by the concentration...
Peritoneal Dialysis III: Nursing Management01:25

Peritoneal Dialysis III: Nursing Management

Peritoneal dialysis, or PD, utilizes the peritoneal membrane as a filter to eliminate excess fluid and waste products. Effective nursing management is essential for ensuring patient safety, preventing complications, and promoting optimal function of the peritoneal dialysis process.Assessment and MonitoringNurses must thoroughly assess the patient before, during, and after each dialysis session. Regular monitoring includes vital signs, daily weight, fluid intake and output, and laboratory values...
Dialysis01:27

Dialysis

Renal failure occurs when the kidneys lose their ability to filter waste products from the blood effectively. It can be classified into two types: acute renal failure (ARF) and chronic renal failure (CRF).
Acute kidney injury develops suddenly and can be caused by pre-renal causes (e.g., hypovolemia, shock), intrinsic renal causes (e.g., acute tubular necrosis), or post-renal causes (e.g., urinary obstruction). In contrast, chronic renal failure progresses gradually over time and is often...
Dialysis01:15

Dialysis

Dialysis is a diffusion-based purification process that separates analyte molecules from a complex matrix. This is accomplished by allowing molecules in the solution to pass through a semipermeable membrane into a liquid on the other side. The membrane is usually made of cellulose acetate or cellulose nitrate, and the second liquid must be miscible with the solution. Ions (e.g., chloride or sodium) or organic molecules (e.g., glucose) can pass through the membrane pores, which generally have...
Extracorporeal Removal of Drugs: Peritoneal Dialysis and Hemodialysis01:30

Extracorporeal Removal of Drugs: Peritoneal Dialysis and Hemodialysis

Patients with end-stage renal disease (ESRD) or those experiencing drug overdose often require extracorporeal methods to eliminate accumulated drugs and metabolites. Hemoperfusion, hemofiltration, and dialysis are the primary techniques to rapidly remove harmful substances without disrupting the patient's fluid and electrolyte balance. For those with compromised renal function, dosage adjustments of concurrent medications may be necessary during extracorporeal drug removal.Dialysis is a process...

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Related Experiment Video

Updated: Jul 12, 2026

A Retrograde Implantation Approach for Peritoneal Dialysis Catheter Placement in Mice
06:27

A Retrograde Implantation Approach for Peritoneal Dialysis Catheter Placement in Mice

Published on: July 20, 2022

Artificial Intelligence in Peritoneal Dialysis: Applications, Algorithms, and Future Directions.

Jose Arriola-Montenegro, Tamar Ratishvili, Andrea Kattah

    Blood Purification
    |July 10, 2026
    PubMed
    Summary

    Artificial intelligence (AI) shows great promise for improving peritoneal dialysis (PD) care by enabling early risk identification and personalized treatment. Further validation and implementation are needed to fully realize AI

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    Surgical Techniques for Catheter Placement and 5/6 Nephrectomy in Murine Models of Peritoneal Dialysis
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    Published on: July 19, 2018

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    Last Updated: Jul 12, 2026

    A Retrograde Implantation Approach for Peritoneal Dialysis Catheter Placement in Mice
    06:27

    A Retrograde Implantation Approach for Peritoneal Dialysis Catheter Placement in Mice

    Published on: July 20, 2022

    Surgical Techniques for Catheter Placement and 5/6 Nephrectomy in Murine Models of Peritoneal Dialysis
    07:11

    Surgical Techniques for Catheter Placement and 5/6 Nephrectomy in Murine Models of Peritoneal Dialysis

    Published on: July 19, 2018

    Area of Science:

    • Nephrology
    • Biomedical Informatics
    • Artificial Intelligence in Healthcare

    Background:

    • Peritoneal dialysis (PD) generates extensive, structured data suitable for artificial intelligence (AI) integration.
    • PD outcomes are influenced by modifiable factors like dialysate composition and fluid balance, allowing for actionable predictions.

    Purpose of the Study:

    • To review the current applications and potential of AI in peritoneal dialysis care.
    • To highlight AI's role in patient stratification, outcome prediction, and monitoring.

    Main Methods:

    • Review of recent investigations utilizing machine learning (ML) and deep learning (DL) in PD.
    • Analysis of AI applications in patient stratification, technique failure prediction, dialysis adequacy, fluid status, peritonitis detection, and mortality prediction.

    Main Results:

    • AI models demonstrate potential in various PD domains, often outperforming traditional statistical methods.
    • AI-driven tools like chatbots show promise for enhancing patient education and adherence.
    • Current studies are mostly exploratory and require external validation.

    Conclusions:

    • AI offers significant promise for improving PD outcomes through early risk identification and personalized therapy.
    • Future directions include EHR integration, remote monitoring, and multi-omics data, requiring careful validation and ethical deployment.