Machine Learning-Driven Quantification of Pathological Fibrosis for Prognostic Relevance

SPRING 2017 RESEARCH INCUBATION AWARDEES 

PI: Vijaya Kolachalama, Medicine, MED
Co-PIs: Katya Ravid, Medicine, MED; Vipul Chitalia, Medicine, MED; David Salant, Medicine, MED

The project proposes to use artificial intelligence (AI) methods to derive quantitative information from the kidney biopsy images that can guide the development of effective treatments.  The burden of chronic kidney disease is enormous both for patients and the healthcare system, and yet we have few treatments to prevent patients from developing kidney failure and the need for dialysis or a kidney transplant. Currently, clinicians rely on a procedure called kidney biopsy (a tiny piece of kidney tissue for microscopic analysis) that is examined in a subjective fashion to identify diseases that might be amenable to treatment. Successful completion of the project can add an objective dimension to kidney biopsy analysis, with broad appeal to the entire renal community.

This work is funded by a Hariri Research Award made in January, 2017.