Communications of the ACM
'PodoSighter' Uses AI to Identify Kidney Disease Indicator
Its very challenging to identify podocytes in an image, said University at Buffalo's Darshana Govind.
Credit: National Institute of Diabetes and Digestive and Kidney Diseases
The cloud-based PodoSighter tool developed by University at Buffalo (UB) researchers is engineered to identify early indicators of kidney disease by detecting and quantifying podocytes, a specialized type of cell in the kidney that is damaged during early stage kidney disease.
UB's Darshana Govind explained, "The tissue is prepared in the clinic and the AI [artificial intelligence]-based method detects it for you. You click a button and the podocytes are identified."
UB's Pinaki Sarder said the PodoSighter also estimates podocyte number and density in each capillary bundle, or glomerulus, containing the cells.
Sarder added increasing glomerulus size and declining podocyte count signal the progression of kidney disease.
From University at Buffalo News Center
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Abstracts Copyright © 2021 SmithBucklin, Washington, DC, USA
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