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Original ArticlesGlomerular and Tubulointerstitial Diseases
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Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks

Giulia Ligabue, Federico Pollastri, Francesco Fontana, Marco Leonelli, Luciana Furci, Silvia Giovanella, Gaetano Alfano, Gianni Cappelli, Francesca Testa, Federico Bolelli, Costantino Grana and Riccardo Magistroni
CJASN October 2020, 15 (10) 1445-1454; DOI: https://doi.org/10.2215/CJN.03210320
Giulia Ligabue
1Department of Surgery, Medicine, Dental Medicine and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy
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Federico Pollastri
2Department of Engineering “Enzo Ferrari,” University of Modena and Reggio Emilia, Modena, Italy
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Francesco Fontana
3Division of Nephrology, Dialysis and Renal Transplantation, Azienda Ospedaliera Universitaria Policlinico di Modena, Modena, Italy
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Marco Leonelli
3Division of Nephrology, Dialysis and Renal Transplantation, Azienda Ospedaliera Universitaria Policlinico di Modena, Modena, Italy
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Luciana Furci
3Division of Nephrology, Dialysis and Renal Transplantation, Azienda Ospedaliera Universitaria Policlinico di Modena, Modena, Italy
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Silvia Giovanella
1Department of Surgery, Medicine, Dental Medicine and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy
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Gaetano Alfano
3Division of Nephrology, Dialysis and Renal Transplantation, Azienda Ospedaliera Universitaria Policlinico di Modena, Modena, Italy
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  • ORCID record for Gaetano Alfano
Gianni Cappelli
1Department of Surgery, Medicine, Dental Medicine and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy
3Division of Nephrology, Dialysis and Renal Transplantation, Azienda Ospedaliera Universitaria Policlinico di Modena, Modena, Italy
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Francesca Testa
3Division of Nephrology, Dialysis and Renal Transplantation, Azienda Ospedaliera Universitaria Policlinico di Modena, Modena, Italy
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Federico Bolelli
2Department of Engineering “Enzo Ferrari,” University of Modena and Reggio Emilia, Modena, Italy
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  • ORCID record for Federico Bolelli
Costantino Grana
2Department of Engineering “Enzo Ferrari,” University of Modena and Reggio Emilia, Modena, Italy
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Riccardo Magistroni
1Department of Surgery, Medicine, Dental Medicine and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy
3Division of Nephrology, Dialysis and Renal Transplantation, Azienda Ospedaliera Universitaria Policlinico di Modena, Modena, Italy
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Abstract

Background and objectives Immunohistopathology is an essential technique in the diagnostic workflow of a kidney biopsy. Deep learning is an effective tool in the elaboration of medical imaging. We wanted to evaluate the role of a convolutional neural network as a support tool for kidney immunofluorescence reporting.

Design, setting, participants, & measurements High-magnification (×400) immunofluorescence images of kidney biopsies performed from the year 2001 to 2018 were collected. The report, adopted at the Division of Nephrology of the AOU Policlinico di Modena, describes the specimen in terms of “appearance,” “distribution,” “location,” and “intensity” of the glomerular deposits identified with fluorescent antibodies against IgG, IgA, IgM, C1q and C3 complement fractions, fibrinogen, and κ- and λ-light chains. The report was used as ground truth for the training of the convolutional neural networks.

Results In total, 12,259 immunofluorescence images of 2542 subjects undergoing kidney biopsy were collected. The test set analysis showed accuracy values between 0.79 (“irregular capillary wall” feature) and 0.94 (“fine granular” feature). The agreement test of the results obtained by the convolutional neural networks with respect to the ground truth showed similar values to three pathologists of our center. Convolutional neural networks were 117 times faster than human evaluators in analyzing 180 test images. A web platform, where it is possible to upload digitized images of immunofluorescence specimens, is available to evaluate the potential of our approach.

Conclusions The data showed that the accuracy of convolutional neural networks is comparable with that of pathologists experienced in the field.

  • renal pathology
  • renal biopsy
  • immunofluorescence
  • Convoluted Neural Network
  • artificial intelligence
  • Received March 12, 2020.
  • Accepted July 3, 2020.
  • Copyright © 2020 by the American Society of Nephrology
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Clinical Journal of the American Society of Nephrology: 15 (10)
Clinical Journal of the American Society of Nephrology
Vol. 15, Issue 10
October 07, 2020
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Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks
Giulia Ligabue, Federico Pollastri, Francesco Fontana, Marco Leonelli, Luciana Furci, Silvia Giovanella, Gaetano Alfano, Gianni Cappelli, Francesca Testa, Federico Bolelli, Costantino Grana, Riccardo Magistroni
CJASN Oct 2020, 15 (10) 1445-1454; DOI: 10.2215/CJN.03210320

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Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks
Giulia Ligabue, Federico Pollastri, Francesco Fontana, Marco Leonelli, Luciana Furci, Silvia Giovanella, Gaetano Alfano, Gianni Cappelli, Francesca Testa, Federico Bolelli, Costantino Grana, Riccardo Magistroni
CJASN Oct 2020, 15 (10) 1445-1454; DOI: 10.2215/CJN.03210320
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Keywords

  • renal pathology
  • renal biopsy
  • immunofluorescence
  • Convoluted Neural Network
  • artificial intelligence

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