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Related Experiment Video

Updated: Sep 11, 2025

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

Published on: June 18, 2020

Chronic Changes on Kidney Histology by a Multiclass Artificial Intelligence Model.

Aleksandar Denic¹, Muhammad S Asghar¹, Lucas Stetzik²

¹Division of Nephrology and Hypertension, Mayo Clinic, Rochester, Minnesota, USA.

Kidney International Reports

|August 15, 2025

Summary

An artificial intelligence (AI) model accurately quantifies chronic kidney disease changes from histology slides. This AI tool aids in predicting kidney failure and assessing chronic changes in kidney tissue.

Keywords:

artificial intelligence chronic kidney disease digital pathology kidney histology nephron size nephrosclerosis

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Last Updated: Sep 11, 2025

Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

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Published on: June 18, 2020

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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Published on: July 11, 2025

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion

A Large Animal Model for Acute Kidney Injury by Temporary Bilateral Renal Artery Occlusion

Published on: February 2, 2021

Area of Science:

Digital pathology
Artificial intelligence in medicine
Nephrology

Background:

Assessing chronic changes in kidney histology is crucial for predicting kidney disease outcomes.
Current methods relying on human interpretation are subjective and have limited accuracy.
Automated quantification of histological features can improve diagnostic consistency and prognostic value.

Purpose of the Study:

To develop and validate an artificial intelligence (AI) model for segmenting and quantifying chronic changes in kidney histology whole slide images (WSIs).
To assess the association between AI-derived chronic kidney disease (CKD) measures and clinical outcomes in kidney donors and patients with renal tumors.

Main Methods:

An AI model was trained using 20,509 annotations across 20 classes on kidney tissue WSIs.
The AI model's performance was validated against human annotations, comparing agreement levels.
The AI model quantified chronic changes, including nephron size and nephrosclerosis markers, in 1426 donors and 1699 tumor patients.

Main Results:

The AI model demonstrated comparable agreement to human pairs in most detections, with lower agreement for arteriolar hyalinosis (AH).
AI-derived chronic changes correlated with reduced glomerular filtration rate (GFR) post-donation and kidney failure post-nephrectomy.
A chronicity score derived from AI detections showed strong prognostic capability for kidney failure (C-statistic = 0.819).

Conclusions:

A multiclass AI model effectively automates the quantification of chronic kidney disease changes from WSIs.
AI-based histological assessment offers a reliable tool for predicting CKD progression and outcomes.
This technology has the potential to enhance diagnostic accuracy and patient management in nephrology.