J Nephropathol. 2022;11(3): e17123.
doi: 10.34172/jnp.2022.17123

Scopus ID: 85134075748
  Abstract View: 467
  PDF Download: 120

Original Article

A deep-learning approach at automated detection of electron-dense immune deposits in medical renal biopsies

Alaa Alsadi 1* ORCID logo, Nasma K. Majeed 2, Dereen M. Saeed 2, Yash Dharmamer 2, Manmeet B. Singh 2, Tushar N. Patel 2 ORCID logo

1 Department of Pathology, University of Wisconsin, Madison, Wisconsin, USA
2 Department of Pathology University of Illinois, Chicago, Illinois, USA
*Corresponding Author: Corresponding author: Alaa Alsadi, Email: , Email: alsadi@wisc.edu


Introduction: Identification of electron-dense immune deposits in electron microscopy (EM) images is integral to the diagnosis of medical renal disease. Deep learning has the potential to augment this process, especially in areas with limited resources.

Objectives: Our study explores the feasibility of applying deep learning to detect electron dense immune deposits in electron microscopy images from medical renal biopsies.

Patients and Methods: EM images (N=900) from native and transplant kidney biopsies were processed into 4530 tiles (512 x 512 pixels). These tiles were reviewed and classified into one of three categories: deposits absent, deposits present, and indeterminate. This classification resulted in 1255 images with consensus agreement for deposits present and deposits absent. These 1255 images were then used to train a machine learning model, using 1006 images for training, and 249 images for testing.

Results: The overall accuracy on the test data was a competitive 78%, and the F1 scores for deposits absent and present was 0.76 and 0.79, respectively.

Conclusion: This study demonstrated the feasibility of creating and applying a machine learning model that performs competitively in identifying electron dense deposits in EM images.

Implication for health policy/practice/research/medical education:

This study demonstrates novel application of deep learning towards analysis of EM images in the diagnosis of renal disease. It also demonstrates feasibility of introducing artificial intelligence/machine learning concepts into pathology residency training programs, especially those with low resources.

Please cite this paper as:Alsadi A, Majeed NK, Saeed DM, Dharmamer Y, Singh MB, Patel TN. A deep-learning approach at automated detection of electron-dense immune deposits in medical renal biopsies. J Nephropathol. 2022;11(3):e17123. DOI: 10.34172/jnp.2021.17123.

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Submitted: 14 Oct 2020
Accepted: 16 Nov 2020
ePublished: 28 Nov 2020
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