Machine learning may augment diagnostics of kidney disease

Two new studies reveal that modern machine learning--a branch of artificial intelligence in which systems learn from data, identify patterns, and make decisions--may augment traditional diagnostics of kidney disease. The findings appear in an upcoming issue of JASN. Pathologists often classify various kidney diseases on the basis of visual assessments of biopsies from patients' kidneys; however, machine learning has the potential to automate and augment the accuracy of classifications. In one study, a team led by Pinaki Sarder, PhD and Brandon Ginley, BS (Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo) developed a computational algorithm to detect the severity of diabetic kidney disease without human intervention. The algorithm examines a digital image of a patient's kidney biopsy at the microscopic level and extracts information on glomeruli, the small blood vessels of the kidney that filter waste from the blood for excretion.