Deep Learning Morphometric Analysis on Protocol Biopsies Predicts Future Graft Function.

Introduction: The predictive value of Banff classification in protocol transplant biopsies without specific lesions is limited. Morphometry provides precise data on microstructures, surpassing semiquantitative scores but is time-consuming. This study evaluates whether automated morphometric analysis with deep learning can predict glomerular filtration rate at 3 years using machine learning. Methods: This retrospective study included kidney transplant recipients who underwent protocol biopsy without specific lesion. The models were trained and tested on the training/test cohort, with external validation on the application cohort. Eight deep learning algorithms extracted 23 morphometric parameters from whole-slide images (WSI). Ten machine learning models were tested for 3 years glomerular filtration rates prediction. Results: A total of 367 patients were included. The means of the 3-year estimated glomerular filtration rates (eGFR) were 53 ± 23 and 53 ± 22 ml/min per 1.73 m Conclusion: Integrating automated morphometric analyses into machine learning models may help predict glomerular filtration rates 3 years after protocol biopsies.