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Updated: Jul 14, 2025
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Whole-Kidney Three-Dimensional Staining with CUBIC
Published on: July 18, 2022
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Automated Tubular Morphometric Visualization for Whole Kidney Biopsy
Neil Kavthekar1, Brandon Ginley2, Samuel Border2
1Departments of Biomedical Engineering, University at Buffalo, the State University of New York.
Summary
Artificial intelligence accurately quantifies interstitial fibrosis and tubular atrophy (IFTA) in kidney biopsies. This high-throughput analysis aids in estimating functional kidney area, improving upon pathologist estimations for chronic kidney disease prognosis.
Area of Science:
- Nephrology
- Digital Pathology
- Artificial Intelligence
Background:
- Interstitial fibrosis and tubular atrophy (IFTA) is a key prognostic indicator for chronic kidney disease (CKD).
- Current pathological assessment of IFTA is subjective and time-consuming due to the large number of tubules in kidney biopsies.
- Accurate quantification of IFTA is crucial for estimating the remaining functional nephritic area.
Purpose of the Study:
- To develop and validate an artificial intelligence (AI) tool for high-throughput quantification of tubule morphometric features in kidney biopsies.
- To improve the precision and efficiency of IFTA assessment compared to manual pathologist evaluation.
- To create a tool for visualizing and analyzing tubule features within whole slide images (WSIs).
Main Methods:
- A custom panoptic convolutional neural network, inspired by Panoptic-DeepLab, was trained to detect tubules in 87 WSIs from native diabetic and transplant kidney biopsies.
- Over 200 morphometric features were measured for each detected tubule, including tubular basement membrane thickness and diameter.
- A visualization tool was developed to highlight tubules based on selected morphometric features and UMAP-based regions-of-interest.
Main Results:
- The AI model successfully detected tubules and quantified numerous morphometric features from kidney biopsy WSIs.
- The developed tool enables interactive highlighting of tubules based on specific feature ranges or combined feature analysis.
- This approach offers a significant advancement in the quantitative analysis of IFTA in kidney pathology.
Conclusions:
- AI-powered analysis offers a precise and efficient method for quantifying tubule morphometrics in kidney biopsies.
- This technology has the potential to enhance the prognostic accuracy of IFTA in chronic kidney disease.
- The developed tool facilitates a deeper understanding of tubule morphology and its relationship to kidney function.
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