Prediction of Kidney Function from Biopsy Images Using Convolutional Neural Networks
This work addresses the need for early prediction of kidney disease progression to improve patient outcomes, but it appears incremental as it applies an existing method (CNNs) to a new dataset without claiming major breakthroughs.
The researchers tackled the problem of predicting future kidney function in patients with chronic kidney disease using convolutional neural networks on high-resolution digital pathology scans from the NEPTUNE study, aiming to identify high-risk patients and influence treatment decisions.
A Convolutional Neural Network was used to predict kidney function in patients with chronic kidney disease from high-resolution digital pathology scans of their kidney biopsies. Kidney biopsies were taken from participants of the NEPTUNE study, a longitudinal cohort study whose goal is to set up infrastructure for observing the evolution of 3 forms of idiopathic nephrotic syndrome, including developing predictors for progression of kidney disease. The knowledge of future kidney function is desirable as it can identify high-risk patients and influence treatment decisions, reducing the likelihood of irreversible kidney decline.