Grading and localization of histological features for bioengineered kidney constructs

Transplantation and dialysis remain the standard treatment for kidney failure. However, transplantation demands outpace demand, transplants may be rejected, and dialysis limits the patient quality of life. Advances in tissue engineering and regenerative medicine offer an alternative therapeutic option in which patient-derived renal cells can be biopsied, cultured, and expended with a scaffold into a new, therapeutic tissue construct. Histological analyses play an important role in validating such therapies. However, there is currently no method for measuring the histological similarity between bioengineered tissues and their genuine counterparts. Here, we present a preliminary method for scoring the similarity between native and newly formed tissues. Using a dataset of H&E-stained kidneys (n = 116) graded as low, medium, or high similarity to real tissue, a convolutional neural network was trained via 6-fold cross-validation to classify artificial tissues. Results indicate high accuracy across all classes – 98.44% for low, 92.86% for medium, and 84.12% for high. Furthermore, visualizations of these predictions superimposed over original H&E corroborate results. Further experimentation and refinement of our model through the inclusion of real tissue as a high similarity class resulted in 83.72%, 71.43%, and 84.00% accuracy for low, medium, and high similarity.