Summary
There is a worldwide shortage of kidneys for transplantation due mainly to the fact that there is no reliable means
to determine the viability of kidneys available for transplant. There are unmet clinical needs to reduce the >8,000
deaths that occur each year from failures of finding viable kidney matches for transplant, to reduce the nearly 4-
year time on donor waitlists, and to reduce the number of failed transplants. The current process for screening
deceased donor kidneys uses two methods: 1) pathological scores based on anatomical features from a biopsy
(tubules, glomeruli, etc.) and 2) the Kidney Donor Profile Index (KDPI) derived from the donor’s medical history
(hypertension, diabetes, weight, etc.). However, clinical research indicates that those current methods have
limited discriminative power. Optical coherence tomography (OCT) is an imaging technology that can obtain
high-resolution, non-invasive, cross-sectional images of biological tissues in situ and in real time. We have
demonstrated that OCT can provide non-invasive, real-time, histopathological information of the kidney that is
impossible to obtain using any other known procedure. We have demonstrated that OCT imaging of human
kidney histopathology both prior to and following their transplant can be used to predict post-transplant renal
function. Furthermore, these preliminary trials have demonstrated that OCT imaging of human donor kidneys
with a hand-held unit in the operating room is safe and that the entire kidney can be evaluated within a relatively
short period of time. From a recently finished clinical study with 169 human transplant kidneys, we found that in
the expanded criteria donor (ECD) kidneys (or marginal kidneys), increased tubular lumen diameter was able to
predict delayed graft function (DGF) prior to implantation. In this proposal, we will develop a novel OCT device
with intelligent scanning and deep learning to evaluate donor kidney viability before transplant. By scanning the
whole kidney surface, our device intends to eliminate the uncertainty created by the biopsy/KDPI paradigm. The
OCT imaging studies will be correlated with post-transplant renal function in order to establish imaging algorithms
and guidelines for OCT imaging of kidneys prior to their transplant. Our central hypothesis is that more
comprehensive morphological parameters as measured by OCT can be used to determine post-transplantation
renal function. The specific aims of this proposal are: 1) Develop robot-assisted automatic 3D scanning OCT
imaging device to image human kidneys prior to their transplantation. 2) Develop deep-learning-based image
processing algorithms to quantitatively assess the OCT parameters as indicators of the functional status of
kidneys, using histology scoring system as the gold standard. 3) Derive the diagnostic criteria for assessing
transplant kidney function and perform prospective clinical studies to assess the accuracy of predicting post-
transplant function using OCT.
