Researchers, including one of Indian origin, have developed a new computer model based on artificial intelligence (AI) that can quantify the extent of kidney damage and predict the life remaining in the organ.
The findings can help make predictions at the point-of-care and assist clinical decision-making, especially in low-resource regions.
"While the trained eyes of expert pathologists are able to gauge the severity of disease and detect nuances of kidney damage with remarkable accuracy, such expertise is not available in all locations, especially at a global level," said Vijaya B. Kolachalama, PhD, Assistant Professor at the Boston University.
The machine learning framework based on convolutional neural networks (CNN) relied on pixel density of digitised images, while the severity of disease was determined by several clinical laboratory measures and renal survival.
CNN model performance was then compared with that of the models generated using the amount of fibrosis reported by a nephropathologist as the sole input and corresponding lab measures and renal survival as the outputs.
"In essence, our model has the potential to act as a surrogate nephropathologist, especially in resource-limited settings," Kolachalama added.
Each year, kidney disease kills more people than breast or prostate cancer, and the overall prevalence of chronic kidney disease in the general population is approximately 14 per cent.
The new model, appearing in the journal Kidney International Reports, has both diagnostic and prognostic applications and may lead to the development of a software application for diagnosing kidney disease and predicting kidney survival, the researchers said.
To test the feasibility of applying this technology to the analysis of routinely obtained kidney biopsies, the researchers performed a proof of principle study on kidney biopsy sections with various amounts of kidney fibrosis (also commonly known as scarring of tissue).