Saudi journal of kidney diseases and transplantation : an official publication of the Saudi Center for Organ Transplantation, Saudi Arabia
35946287
Clinical presentation, kidney biopsy findings, and clinical outcomes of immunoglobulin A nephropathy (IgAN) are highly variable. The objective of this study is to study the clinical presentation, histologic patterns, and outcomes of IgAN in the Pakis...
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...
Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association
36792061
BACKGROUND: Although the MEST-C classification is among the best prognostic tools in immunoglobulin A nephropathy (IgAN), it has a wide interobserver variability between specialized pathologists and others. Therefore we trained and evaluated a tool u...
Computer methods and programs in biomedicine
37722311
BACKGROUND AND OBJECTIVE: The Oxford Classification for IgA nephropathy is the most successful example of an evidence-based nephropathology classification system. The aim of our study was to replicate the glomerular components of Oxford scoring with ...
The billing database of the universal healthcare system in Japan potentially includes large-cohort data of patients with immunoglobulin A nephropathy, diagnosis codes aimed at billing should not be directly used for clinical research because of the r...
IgA nephropathy progresses to kidney failure, making early detection important. However, definitive diagnosis depends on invasive kidney biopsy. This study aimed to develop non-invasive prediction models for IgA nephropathy using machine learning. We...
Clinical journal of the American Society of Nephrology : CJASN
38728096
KEY POINTS: A dynamic model predicts IgA nephropathy prognosis based on deep learning. Longitudinal clinical data and deep learning improve predictive accuracy and interpretability in GN.
BACKGROUND: Machine learning (ML) models can help assisting diagnosis by rapidly localising and classifying regions of interest (ROIs) within whole slide images (WSIs). Effective ML models for clinical decision support require a substantial dataset o...
BACKGROUND: IgA nephropathy (IgAN) is a leading cause of renal failure, characterized by significant clinical and pathological heterogeneity. Accurate subtype classification remains challenging due to overlapping clinical manifestations and the multi...