AIMC Topic: Kidney Diseases
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Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs.
Scientific reports
Jan 4, 2025
We have adopted the classification Read-Across Structure-Activity Relationship (c-RASAR) approach in the present study for machine-learning (ML)-based model development from a recently reported curated dataset of nephrotoxicity potential of orally ac...
Development, validation and economic evaluation of a machine learning algorithm for predicting the probability of kidney damage in patients with hyperuricaemia: protocol for a retrospective study.
BMJ open
Nov 28, 2024
INTRODUCTION: Accurate identification of the risk factors is essential for the effective prevention of hyperuricaemia (HUA)-related kidney damage. Previous studies have established the efficacy of machine learning (ML) methodologies in predicting kid...
Exploring the Potential of Claude 3 Opus in Renal Pathological Diagnosis: Performance Evaluation.
JMIR medical informatics
Nov 15, 2024
BACKGROUND: Artificial intelligence (AI) has shown great promise in assisting medical diagnosis, but its application in renal pathology remains limited.
Machine learning algorithms using the inflammatory prognostic index for contrast-induced nephropathy in NSTEMI patients.
Biomarkers in medicine
Nov 13, 2024
Inflammatory prognostic index (IPI), has been shown to be related with poor outcomes in cancer patients. We aimed to investigate the predictive role of IPI for contrast-induced nephropathy (CIN) development in non-ST segment elevation myocardial inf...
Research hotspots and frontiers of machine learning in renal medicine: a bibliometric and visual analysis from 2013 to 2024.
International urology and nephrology
Oct 30, 2024
BACKGROUND: The kidney, an essential organ of the human body, can suffer pathological damage that can potentially have serious adverse consequences on the human body and even affect life. Furthermore, the majority of kidney-induced illnesses are freq...
Self-Supervised Learning for Feature Extraction from Glomerular Images and Disease Classification with Minimal Annotations.
Journal of the American Society of Nephrology : JASN
Oct 9, 2024
BACKGROUND: Deep learning has great potential in digital kidney pathology. However, its effectiveness depends heavily on the availability of extensively labeled datasets, which are often limited because of the specialized knowledge and time required ...
Clinicopathological features for the prediction of immunosuppressive treatment responses in sarcoidosis-related kidney involvement: a single-center retrospective study.
Turkish journal of medical sciences
Oct 8, 2024
BACKGROUND/AIM: Sarcoidosis is a multisystem disorder that affects many organs, including the kidneys. This single-center retrospective study investigated the clinical, pathological, and laboratory findings of patients with kidney sarcoidosis who wer...
Integrating neural networks with advanced optimization techniques for accurate kidney disease diagnosis.
Scientific reports
Sep 18, 2024
Kidney diseases pose a significant global health challenge, requiring precise diagnostic tools to improve patient outcomes. This study addresses this need by investigating three main categories of renal diseases: kidney stones, cysts, and tumors. Uti...
Artificial Intelligence-Based Classification of CT Images Using a Hybrid SpinalZFNet.
Interdisciplinary sciences, computational life sciences
Aug 21, 2024
The kidney is an abdominal organ in the human body that supports filtering excess water and waste from the blood. Kidney diseases generally occur due to changes in certain supplements, medical conditions, obesity, and diet, which causes kidney functi...
Accurate classification of glomerular diseases by hyperspectral imaging and transformer.
Computer methods and programs in biomedicine
Jun 11, 2024
BACKGROUND AND OBJECTIVE: In renal disease research, precise glomerular disease diagnosis is crucial for treatment and prognosis. Currently reliant on invasive biopsies, this method bears risks and pathologist-dependent variability, yielding inconsis...
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