Artificial intelligence approaches may improve diagnostics of kidney disease

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. These structures are known to become progressively damaged and scarred over the course of diabetes. There are typically 10 to 20 individual glomeruli per biopsy, and the algorithm detects the location of each glomerular sub-component in the digital images, and then makes many measurements on each sub-component.