Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks

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 ( 7400) 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 \u201cappearance,\u201d \u201cdistribution,\u201d \u201clocation,\u201d and \u201cintensity\u201d of the glomerular deposits identified with fluorescent antibodies against IgG, IgA, IgM, C1q and C3 complement fractions, fibrinogen, and \u3ba- and \u3bb-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 (\u201cirregular capillary wall\u201d feature) and 0.94 (\u201cfine granular\u201d 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