LAUSR

Discrimination indices for TCMR, ABMR and mixed rejection ranged from 0.67 – 0.77, respectively, and improved to 0.69 – 0.82. Noteworthy, the CNN was able to discriminate viral nephropathies from the rejection classes (AUROC = 0.71), even though no additional immunohistochemistry was used as input for the networks. Discussion: We have shown the potential of a deep learning algorithm to discriminate between clinically relevant diagnoses only from histopathological images. The integration of several histological stainings improved the performance. Our study provides the first example of a large multicenter trial examining the potential of artificial intelligence on non-tumor pathology. Next, we will further extend and refine the algorithm in even larger cohorts and using additional data integration. Conclusion: Deep convolutional neural networks have the potential to reproducibly classify transplant histopathology, even without human expert annotation of individual lesions.