Computer-aided renal allograft assessment using ultrasound elastography and machine learning

Abstract

Rheological tissue parameters have been shown to correlate with specific histological characteristics related to different pathologies and specifically to kidney rejection. For decades, kidney function tests and biopsy have been used as the main assessment methods for allograft health. With this work we are creating novel approaches for reliable and non-invasive allograft assessment tools by using shear wave elastography measurements with different machine learning algorithms to model the mechanical properties and the pathological changes in the tissue. We also propose to interpret the findings leveraging game theory analysis of the model inputs and outputs to understand what parameters are contributing most for the model prediction. We also intend to better comprehend the progress of kidney rejection from microscopic to macroscopic scales using histology-based models of shear wave propagation. Finally, we propose to create a fast, reliable, and non-invasive allograft assessment method by analyzing the shear wave propagation with minimal signal processing, leveraging convolutional neural networks architectures to retrieve the features from two-dimensional Fourier transform analysis of shear wave data, without the use of complex mathematical and physical models.