Quantification of interstitial fibrosis in digitized kidney biopsies using deep neural networks

Interstitial fibrosis in kidney biopsy is an established marker of disease prognosis and chronicity. Conventional manual grading by pathologists is subjective and variable. Computational methods may reduce this variability. We developed a deep learning framework using a ResNet-50 architecture with transfer learning to classify interstitial fibrosis on trichrome-stained renal biopsy images. A total of 2000 biopsies were screened; 162 were excluded due to poor slide quality, leaving 1838 cases (1154 native, 684 transplant). A single expert nephropathologist provided ground-truth fibrosis scores, categorized as < 5% (minimal), 5–25% (mild), 26–50% (moderate), and > 50% (severe). The model achieved an overall accuracy of 73% (95% CI: 65–81%) with substantial agreement with the pathologist (κ = 0.68). Stratified analysis showed 78% accuracy for native kidneys and 67% for transplant biopsies. Our CNN-based framework shows promise for standardizing fibrosis assessment in kidney biopsies. However, the absence of external validation remains a key limitation, and further multi-center studies are required.