# Lupus Nephritis Neural Network, LuNN

> **NIH NIH R56** · UNIVERSITY OF HOUSTON · 2020 · $100,750

## Abstract

Up to 60% of adults and 80% of children with systemic lupus erythematosus (SLE) develop
nephritis (LN), with 10–30% progressing to end-stage renal disease (ESRD). The gold standard
for diagnosis of LN is a renal biopsy. Histological parameters remain the best predictors of
ESRD. Despite being the gold standard, histological diagnosis of LN has several shortcomings.
In multiple inter-observer renal pathology assessment studies reported thus far, the inter-
pathologist correlation coefficients, or concordance, in assessing most histological parameters
have been sub-optimal. This has provided the impetus for the current proposal.
We propose to leverage the power of computer vision and deep learning to build a classifier that
rivals the best-trained renal pathologists in making a histological diagnosis of LN using current
diagnostic criteria. We propose to train a deep convolutional neural network to distinguish the
different LN classes, and to identify a full spectrum of histological attributes useful for diagnosis.
We will compare the performance of the newly generated neural network in scoring
glomerular/tubulo-interstitial features and LN classes, against a panel of human renal
pathologists. Finally, we propose to build a neural network that can predict clinical outcome
based on baseline renal pathology. Reliable and reproducible classification of LN could
dramatically improve patient management and long-term renal and patient survival.

## Key facts

- **NIH application ID:** 10246669
- **Project number:** 1R56DK122036-01A1
- **Recipient organization:** UNIVERSITY OF HOUSTON
- **Principal Investigator:** CHANDRA MOHAN
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $100,750
- **Award type:** 1
- **Project period:** 2020-09-15 → 2021-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10246669

## Citation

> US National Institutes of Health, RePORTER application 10246669, Lupus Nephritis Neural Network, LuNN (1R56DK122036-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10246669. Licensed CC0.

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