# Quantifying Microbial Keratitis to Predict Outcomes: An Imaging and Epidemiologic Approach

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2021 · $397,484

## Abstract

PROJECT SUMMARY/ABSTRACT
For epidemiological studies, future clinical trials, and personalized patient care, there is a critical need
to create a risk-stratification system for microbial keratitis. Microbial keratitis (MK), a debilitating,
infectious corneal disease, is estimated to be the fourth-leading cause of blindness worldwide. MK
severity depends on a complex interaction of patient, organism, and environment, resulting in a
spectrum of clinical presentations and responses to treatment. Clinical presentations manifest with
unique morphology features and clinical symptoms. Morphology features are visible in the cornea, and
symptoms are measurable. But most patients are treated with non-specific broad-spectrum
antimicrobials, an approach that increases antimicrobial resistance. This non-specific treatment
approach lacks congruence with the unique MK presentations. There is a critical need for a new
strategy to personalize treatments for MK and measure treatment efficacy. With quantified MK
morphologic and clinical features, clinicians will have the tools to risk-stratify patients. The long-term
goal is to develop rapid, objective, personalized treatment plans for patients with MK. This proposal’s
objective is to quantify dynamic morphologic and clinical MK features using image and electronic health
record (EHR) analyses and then build a risk-stratification scoring system associated with MK outcomes.
The proposed research will test the hypothesis that morphologic and clinical features accurately risk-
stratify patients for corneal and vision outcomes. Our premise is supported by preliminary data
demonstrating that: (1) different organisms generate distinct morphologic and clinical features; (2)
clinicians quantify morphology less precisely than image-analysis methods, (3) an expert is able to use
MK features to tailor treatments; (4) the use of quantified features has improved outcomes in other
diseases, such as diabetic retinopathy, by helping providers to tailor treatments; (5) EHR data can be
used to quantify and classify clinical disease features accurately; and (6) EHR data can be used
effectively to risk-stratify patients. Aim 1 will develop objective image analysis tools to measure features
of MK with existing clinical equipment. Aim 2 will evaluate MK treatment efficacy using morphologic
image analysis and clinical features from prospective surveys. Aim 3 will risk-stratify patients with MK
by combining image analysis and EHR extracted data. The expected outcomes are: (1) characterized
databases of MK images and linked clinical data, (2) quantified MK features across a spectrum of
clinical presentations, (3) performance-tested, open-source imaging algorithms and surveys to measure
MK markers dynamically, and (4) a novel risk stratification model and scoring system. The resultant
work will have significant value to clinicians. Clinicians can use practical, low-cost technologies and
readily-available EHR data to quantify MK features and ...

## Key facts

- **NIH application ID:** 10113626
- **Project number:** 5R01EY031033-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Maria Anneke Woodward
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $397,484
- **Award type:** 5
- **Project period:** 2020-03-01 → 2024-02-29

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10113626

## Citation

> US National Institutes of Health, RePORTER application 10113626, Quantifying Microbial Keratitis to Predict Outcomes: An Imaging and Epidemiologic Approach (5R01EY031033-02). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10113626. Licensed CC0.

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