The University of Massachusetts Amherst


Worldwide, there is a critical shortage of kidneys available for transplantation, mainly because we have no reliable means to establish the viability of kidneys before they are actually transplanted. In the U.S. alone, according to the National Kidney Foundation, 100,791 people remain on the waitlist for kidney transplants, and some 13 persons die each day waiting for compatible kidneys. Now, Biomedical Engineering Associate Professor Yu Chen is the principal investigator (PI) on a $679,945 R01 grant to UMass Amherst from the National Institutes of Health (NIH) that will develop a groundbreaking optical coherence tomography (OCT) device for the preassessment of kidneys and their viability as potential transplant organs.

Chen says that his new device is aimed at answering the vital clinical need to decrease the more than 8,000 deaths that occur worldwide each year from failure to find compatible kidney transplant matches, to reduce the nearly four-year lag time on donor waitlists, and to cut down the number of failed transplants.

According to Chen, the current process for screening donor kidneys employs two methods: pathological scores based on anatomical features from a biopsy; and the Kidney Donor Profile Index (KDPI), derived from the donor’s medical history, which includes data about hypertension, diabetes, weight, and other factors.

However, clinical research indicates that these two procedures have limited predictive effectiveness when trying to predetermine the compatibility of a kidney for transplant into a particular patient.

One solution is Chen’s new methodology. As Chen explains, “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 on the kidney that is impossible to obtain using any other known procedure.”

In addition, as Chen says, “We have demonstrated that OCT imaging of human kidney histopathology both prior to and following 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.”

As Chen says about his NIH-supported research, “In this proposal, we will develop a novel [robot-assisted, automatic, 3D] OCT device with intelligent scanning and deep learning to evaluate donor kidney viability before transplant. By scanning the whole kidney surface, our device [is intended] to eliminate the uncertainty created by the biopsy/KDPI paradigm.”

According to Chen, “Our central hypothesis is that more comprehensive morphological parameters as measured by OCT can be used to determine post-transplantation renal function.”

Chen’s team will also create deep-learning-based image processing algorithms to assess the OCT parameters as indicators of the functional status of kidneys, develop the diagnostic criteria for predicting post-transplant kidney function, and perform clinical studies to prove the accuracy of these methods.

Chen notes that this NIH project is a collaborative proposal involving interdisciplinary skills in OCT, robotics, deep learning, surgery, and nephrology; all employed “to facilitate the translation from the bench to the bedside.”

Chen acknowledges the seed grant support from a UMass Interdisciplinary Faculty Research Award and the UMass Center for Clinical and Translational Sciences Pilot Project Program.

At UMass, Chen is working with Assistant Professor of Mechanical and Industrial Engineering Xian Du. Chen and Du are collaborating with the Worcester Polytechnic Institutethe Georgetown Medical Center, and the University of Oklahoma “to translate this exciting and highly promising technology into clinics and organ-procurement settings,” explains Chen about the NIH grant, totaling $2.5 million dispersed among all four organizations.

Chen concludes that “This technology can increase the number of healthy kidneys available for transplantation by making the most efficient use of available donor kidneys, eliminate the possible use of bad donor kidneys, provide an [accurate] measure of expected post-transplant renal function, and allow better distinction between post-transplant immunological rejection and ischemic-induced acute renal failure.” (November 2022)

Article posted in Research