Automated Computational Detection of Interstitial Fibrosis, Tubular Atrophy, and Glomerulosclerosis

Brandon Ginley, Kuang Yu Jen, Seung Seok Han, Luís Rodrigues, Sanjay Jain, Agnes B. Fogo, Jonathan Zuckerman, Vighnesh Walavalkar, Jeffrey C. Miecznikowski, Yumeng Wen, Felicia Yen, Donghwan Yun, Kyung Chul Moon, Avi Rosenberg, Chirag Parikh, Pinaki Sarder

Research output: Contribution to journalArticlepeer-review


BACKGROUND: Interstitial fibrosis, tubular atrophy (IFTA), and glomerulosclerosis are indicators of irrecoverable kidney injury. Modern machine learning (ML) tools have enabled robust, automated identification of image structures that can be comparable with analysis by human experts. ML algorithms were developed and tested for the ability to replicate the detection and quantification of IFTA and glomerulosclerosis that renal pathologists perform. METHODS: A renal pathologist annotated renal biopsy specimens from 116 whole-slide images (WSIs) for IFTA and glomerulosclerosis. A total of 79 WSIs were used for training different configurations of a convolutional neural network (CNN), and 17 and 20 WSIs were used as internal and external testing cases, respectively. The best model was compared against the input of four renal pathologists on 20 new testing slides. Further, for 87 testing biopsy specimens, IFTA and glomerulosclerosis measurements made by pathologists and the CNN were correlated to patient outcome using classic statistical tools. RESULTS: The best average performance across all image classes came from a DeepLab version 2 network trained at 40× magnification. IFTA and glomerulosclerosis percentages derived from this CNN achieved high levels of agreement with four renal pathologists. The pathologist- and CNN-based analyses of IFTA and glomerulosclerosis showed statistically significant and equivalent correlation with all patient-outcome variables. CONCLUSIONS: ML algorithms can be trained to replicate the IFTA and glomerulosclerosis assessment performed by renal pathologists. This suggests computational methods may be able to provide a standardized approach to evaluate the extent of chronic kidney injury in situations in which renal-pathologist time is restricted or unavailable.

Original languageEnglish (US)
Pages (from-to)837-850
Number of pages14
JournalJournal of the American Society of Nephrology : JASN
Issue number4
StatePublished - Apr 1 2021


  • convolutional neural network
  • diabetes
  • eGFR
  • glomerulosclerosis
  • interstitial fibrosis
  • machine learning
  • prognostication
  • transplant
  • tubular atrophy
  • whole slide segmentation

ASJC Scopus subject areas

  • Medicine(all)


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