AIMC Topic: Kidney

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Comparative analysis of kidney function prediction: traditional statistical methods vs. deep learning techniques.

Clinical and experimental nephrology
BACKGROUND: Chronic kidney disease (CKD) represents a significant public health challenge, with rates consistently on the rise. Enhancing kidney function prediction could contribute to the early detection, prevention, and management of CKD in clinica...

Exploring the subtle and novel renal pathological changes in diabetic nephropathy using clustering analysis with deep learning.

Scientific reports
To decrease the number of chronic kidney disease (CKD), early diagnosis of diabetic kidney disease is required. We performed invariant information clustering (IIC)-based clustering on glomerular images obtained from nephrectomized kidneys of patients...

Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types.

Scientific reports
Recently, as the number of cancer patients has increased, much research is being conducted for efficient treatment, including the use of artificial intelligence in genitourinary pathology. Recent research has focused largely on the classification of ...

The effect of renal function on the clinical outcomes and management of patients hospitalized with hyperglycemic crises.

Frontiers in endocrinology
BACKGROUND: The global prevalence of diabetes has been rising rapidly in recent years, leading to an increase in patients experiencing hyperglycemic crises like diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS). Patients with imp...

Machine learning assisted classification RASAR modeling for the nephrotoxicity potential of a curated set of orally active drugs.

Scientific reports
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...

Dual-Stage AI Model for Enhanced CT Imaging: Precision Segmentation of Kidney and Tumors.

Tomography (Ann Arbor, Mich.)
OBJECTIVES: Accurate kidney and tumor segmentation of computed tomography (CT) scans is vital for diagnosis and treatment, but manual methods are time-consuming and inconsistent, highlighting the value of AI automation. This study develops a fully au...

Multi-stain deep learning prediction model of treatment response in lupus nephritis based on renal histopathology.

Kidney international
The response of the kidney after induction treatment is one of the determinants of prognosis in lupus nephritis, but effective predictive tools are lacking. Here, we sought to apply deep learning approaches on kidney biopsies for treatment response p...

A novel automated method for comprehensive renal cast quantification from rat kidney sections using QuPath.

American journal of physiology. Renal physiology
The presence of tubular casts within the kidney serves as an important feature when assessing the degree of renal injury. Quantification of renal tubular casts has been historically difficult due to varying cast morphologies, protein composition, and...

Unveiling the intricate interplay: Exploring biological bridges between renal ischemia-reperfusion injury and T cell-mediated immune rejection in kidney transplantation.

PloS one
UNLABELLED: Although the link between ischemia-reperfusion injury (IRI) and T cell-mediated rejection (TCMR) in kidney transplantation (KT) is well known, the mechanism remains unclear. We investigated essential genes and biological processes involve...

Interpretable Deep-learning Model Based on Superb Microvascular Imaging for Noninvasive Diagnosis of Interstitial Fibrosis in Chronic Kidney Disease.

Academic radiology
RATIONALE AND OBJECTIVES: To develop an interpretable deep learning (XDL) model based on superb microvascular imaging (SMI) for the noninvasive diagnosis of the degree of interstitial fibrosis (IF) in chronic kidney disease (CKD).