Deep learning identified pathological abnormalities predictive of graft loss in kidney transplant biopsies

Deep learning identified pathological abnormalities predictive of graft loss in kidney transplant biopsies

Interstitial fibrosis, tubular atrophy, and inflammation are major contributors to kidney allograft failure. Here we sought an objective, quantitative pathological assessment of these lesions to improve predictive utility and constructed a deep-learning–based pipeline recognizing normal vs. abnormal...

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Bibliographic Details
Main Authors: Banu, K. (Author), Colvin, R.B (Author), Cravedi, P. (Author), Farouk, S. (Author), Fredericks, S. (Author), Haroon Al Rasheed, M.R (Author), Huang, W. (Author), Hultin, S. (Author), Keung, K. (Author), Li, L. (Author), Lin, Q. (Author), Menon, M.C (Author), Murphy, B. (Author), O'Connell, P.J (Author), Rogers, N.M (Author), Rosales, I.A (Author), Salem, F. (Author), Shingde, M. (Author), Smith, R.N (Author), Su, F. (Author), Sun, Z. (Author), Wei, C. (Author), Wong, G. (Author), Xi, C. (Author), Yi, Z. (Author), Zhang, W. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2022
Subjects:
adverse event
biopsy
Biopsy
deep learning
Deep Learning
graft rejection
Graft Rejection
graft survival
Graft Survival
human
Humans
kidney
Kidney
kidney transplantation
Kidney Transplantation
pathology
renal pathology
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