AI Medical Compendium Journal:
Renal failure

Showing 21 to 24 of 24 articles

Development of a novel combined nomogram model integrating deep learning radiomics to diagnose IgA nephropathy clinically.

Renal failure
This study aimed to develop and validate a combined nomogram model based on superb microvascular imaging (SMI)-based deep learning (DL), radiomics characteristics, and clinical factors for noninvasive differentiation between immunoglobulin A nephropa...

Development and deployment of interpretable machine-learning model for predicting in-hospital mortality in elderly patients with acute kidney disease.

Renal failure
BACKGROUND: Acute kidney injury (AKI) is more likely to develop in the elderly admitted to the intensive care unit (ICU). Acute kidney disease (AKD) affects ∼45% of patients with AKI and increases short-term mortality. However, there are no studies o...

Artificial intelligence in peritoneal dialysis: general overview.

Renal failure
OBJECTIVE: This article is a general overview about artificial intelligence/machine learning (AI/ML) algorithms in the domain of peritoneal dialysis (PD).

Machine learning for the prediction of acute kidney injury in critical care patients with acute cerebrovascular disease.

Renal failure
PURPOSE: Acute kidney injury (AKI) is a common complication and associated with a poor clinical outcome. In this study, we developed and validated a model for predicting the risk of AKI through machine learning methods in critical care patients with ...