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Updated: May 12, 2026

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

Deep Learning-Based Automated Imaging Classification of ADPKD.

Youngwoo Kim¹, Seonah Bu², Cheng Tao³

¹Department of Computer Software Engineering, Kumoh National Institute of Technology, Republic of Korea.

Kidney International Reports

|June 20, 2024

Summary

A new deep learning method accurately classifies kidney imaging classes 1 and 2 from MR images, matching expert performance. This automated approach aids clinical trials and patient management for autosomal dominant polycystic kidney disease (ADPKD).

Keywords:

atypical cyst deep learning explainable artificial intelligence polycystic kidney disease risk factors total kidney volume

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Last Updated: May 12, 2026

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Area of Science:

Medical Imaging
Artificial Intelligence
Nephrology

Background:

The Mayo imaging classification model (MICM) requires manual classification into class 1 (typical) or class 2 (atypical) before application.
Patients classified as class 2 are excluded from MICM, necessitating accurate pre-classification.

Purpose of the Study:

To develop and evaluate a deep learning-based automated method for classifying class 1 and 2 from abdominal T1-weighted MR images.
To assess the performance and explainability of the automated classification using explainable artificial intelligence (XAI).

Main Methods:

Utilized abdominal T1-weighted MR images from 486 subjects.
Applied transfer learning for deep learning-based classification.
Incorporated XAI to enhance classification result interpretability.

Main Results:

Achieved high classification performance: 97.7% for class 1, 100% for class 2, and 98.01% overall accuracy.
Demonstrated strong precision and recall for both classes, with F1-scores of 0.99 (class 1) and 0.93 (class 2).
XAI effectively highlighted image regions contributing to the classification decisions.

Conclusions:

The automated deep learning method achieves expert-level accuracy in classifying kidney imaging classes.
This tool can significantly aid clinical trials and patient management in autosomal dominant polycystic kidney disease (ADPKD).