# Applications of artificial intelligence to the diagnostic evaluation of infectious keratitis

> **NIH NIH K23** · OREGON HEALTH & SCIENCE UNIVERSITY · 2022 · $267,092

## Abstract

PROJECT SUMMARY/ABSTRACT
This K23 proposal aims to develop and evaluate applications of artificial intelligence (AI) to the diagnostic
investigation of infectious keratitis, a major cause of blindness worldwide. This will be accomplished through
three specific aims: 1) Develop and evaluate an AI model to identify the etiology of culture-proven infectious
keratitis from an existing database of clinical photographs; 2) Externally validate model performance in a real-
world, population-based sample of corneal ulcers; and 3) Develop and evaluate an additional AI model for
automated microscopic diagnosis of fungal keratitis. The AI model developed in SA#1 will be trained using a
clinical photography database (the Culture Positive Ulcer Database) collated from several NIH funded clinical
trials for infectious keratitis (SCUT, MUTT I & II, CLAIR, and MALIN) conducted over the past several decades
as part of the international collaboration between the Francis I. Proctor Foundation and Aravind Eye Hospital in
India. This model's performance will be compared against human experts on culture-proven cases of infectious
keratitis. A second repository of imaging and clinical data from corneal ulcers (the MADURAI database)
currently in development will be used to externally validate the AI model developed in SA#1 (by estimating its
sensitivity and specificity in a real-world sample) and to train the AI model in SA#3. To accomplish these
research goals, we have established an international collaboration between the Casey Eye Institute, the
Proctor Foundation, and Aravind. This provides an unprecedented opportunity to leverage the expertise of my
mentors at Casey in artificial intelligence and computer vision-enabled diagnosis of ophthalmic diseases, the
expertise of the world-class faculty at Proctor in epidemiology, biostatistics, and infectious keratitis, and the
unparalleled volume of infectious keratitis and infrastructure for data collection at Aravind. This collaboration
will facilitate the development of carefully designed and validated AI models which will guide earlier directed
antimicrobial therapy and improve visual outcomes in infectious keratitis.
My primary career goals are to establish myself as an independent clinician scientist performing research at
the interface of technological innovation and international public health. My MPH, medical training, and
research experience have allowed me to develop a strong foundation in public health, the clinical and surgical
management of corneal infections, and medical informatics. Over the past nine months of K12 support I have
begun developing expertise in machine learning and data science, establishing a foundation which I will build
upon during this K23 award period. The successful application of AI to health care problems requires a
multidisciplinary approach involving clinicians, AI methodologists, informaticists, and public health experts. This
K23 will allow me to build skills and expertise in each ...

## Key facts

- **NIH application ID:** 10449624
- **Project number:** 1K23EY032639-01A1
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Travis Kenneth Redd
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $267,092
- **Award type:** 1
- **Project period:** 2022-07-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10449624

## Citation

> US National Institutes of Health, RePORTER application 10449624, Applications of artificial intelligence to the diagnostic evaluation of infectious keratitis (1K23EY032639-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10449624. Licensed CC0.

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