Abstract: SA-PO045
Machine Learning and Glomerular Remodeling
Session Information
- Engineering-Based Approaches to Problems in Nephrology
November 09, 2019 | Location: Exhibit Hall, Walter E. Washington Convention Center
Abstract Time: 10:00 AM - 12:00 PM
Category: Bioengineering
- 300 Bioengineering
Authors
- Pellicano, Anthony, Angion Biomedica Corp., Uniondale, New York, United States
- Goldberg, Itzhak D., Angion Biomedica Corp., Uniondale, New York, United States
- Narayan, Prakash, Angion Biomedica Corp., Uniondale, New York, United States
Background
Glomerular hypertrophy is an early biomarker of ongoing renal disease and informs glomerulosclerosis and proteinuria. Using machine learning we trained a computer to first identify glomeruli and then measure glomerular dimensions.
Methods
The training set comprised ~100 images (varying magnification) of hematoxylin-eosin (H-E) or periodic acid Schiff (PAS)-stained kidney tissue sourced from published literature. An open-source git-hub implementation of the mask region-convolutional neural network was used to generate a model and Keras HDF5 data used to identify, count, and measure areas of glomeruli present in the renal tissue on a Raspberry Pi 3 server. The test set comprised images (40X and 10X, n=16) of stained kidney tissue from rats sacrificed 14 days after puromycin aminonucleoside (167 mg/kg, intraperitoneal, n=3) administration, and a sham cohort of animals (n=3). Kidney sections comprising the test set had been stained with H-E or hematoxylin alone.
Results
The Raspberry Pi 3 machine was able to correctly identify glomeruli (A) while excluding non-glomerular structures in all test cases. Having correctly identified glomeruli, the machine was able to measure (B) glomerular area using a precalibrated tool.
Conclusion
Machine learning has enabled both recognition of glomeruli within renal sections and measurement of their dimensions, reducing time and labor while eliminating operator bias. This technology can be used in images from renal biopsies to diagnose ongoing glomerular kidney disease.
A glomerulus (A) amidist in a renal section was identified by the machine, greying out the surrounding nephrons. The machine was able to isolate several glomeruli and start quantifying their area (B).
Funding
- Private Foundation Support