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Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
Published on: June 18, 2020
Two-Stage Deep Learning Networks for Diagnosing and Staging Membranous Glomerulonephritis From Electron Microscopy
Mihai Gabriel Constantin1, George Terinte-Balcan2, Ioana Maria Lambrescu2
1AI Multimedia Lab, National University of Science and Technology Politehnica Bucharest, Bucharest, Romania.
Purpose:
The purpose of this study was to use transmission electron miscroscopy images to develop a deep learning-based artificial intelligence model to detect and stage membranous glomerulonephritis (MN), a major cause of nephrotic syndrome in adults.
Materials And Methods:
A comprehensive data set of micrographs was used to train and test the proposed model. The architecture consisted of a 2-stage model based on vision transformer networks. The first stage detected membrane regions independent of disease stage; the second stage classified each region according to the stage of MN; and the final image-level classification was determined by a majority vote among the individual membrane regions. Model generalization was evaluated using an independent data set containing patient micrographs, acquired using a microscope and camera, and evaluated by different practitioners.
Results:
The proposed model achieved an accuracy of 0.9231, sensitivity of 0.9240, and specificity of 0.9167. The model maintained strong performance on the external data set (accuracy, 0.8905).
Conclusions:
The proposed 2-stage model enhances accuracy, robustness, and clinical interpretability in MN staging from transmission electron microscopy images, thus showing strong potential for integration into routine diagnostic workflows. Article History.