# AI training module for Vision Science

> **NIH NIH T32** · OREGON HEALTH & SCIENCE UNIVERSITY · 2021 · $86,400

## Abstract

Project Summary
 The development of machine learning (ML) models for health care applications has become a highly
active and rapidly evolving area of research, particularly in ophthalmology, which relies heavily upon pattern
recognition. ML models trained to interpret medical data have demonstrated dramatically improved
performance in the past decade, driven largely by the advent of deep learning. Multiple models have now
received FDA approval and are being implemented in the clinical setting, making artificial intelligence (AI) a
priority for the American Academy of Ophthalmology and the field in general.
 Compared to traditional ML learning algorithms, deep learning leverages massively large training
datasets to generate prediction models capable of achieving unprecedented performance in pattern recognition
within structured or unstructured data. Assembling correctly labeled datasets, which are representative of the
target patient population and are large enough to train a deep learning model, is challenging and remains the
primary barrier to continued advancement in this field. Because these data are scarce, it is crucial to maximize
their utility by making them broadly available in a useable format. Researchers spend significant time and effort
curating the databases used to successfully train their ML models, but rarely are these datasets subsequently
shared in a manner that is FAIR (findable, accessible, interoperable, and reusable). Emphasis on structuring
these data in such a manner while protecting subjects' private health information would enhance
interdisciplinary collaboration and promote advancement of the field.
 In this supplement, we propose a web-based, publicly available data science module designed to
provide vision science researchers from a variety of backgrounds with the conceptual and practical knowledge
necessary to produce FAIR, ML-ready data. The AI module will accomplish this goal through a combination of
recorded video lectures, reading materials, knowledge assessments, and hands-on assignments with
immediate feedback. The module will be developed and integrated into an existing predoctoral curriculum and
hosted by Oregon Health & Science University, but will be freely available online to a global audience.
Instructors will include an interdisciplinary team with experience operating at the interface of AI and
ophthalmology, including experts in data science, medical informatics, machine learning methodology, image
processing, and public health.

## Key facts

- **NIH application ID:** 10405897
- **Project number:** 3T32EY023211-08S1
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Kate E Keller
- **Activity code:** T32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $86,400
- **Award type:** 3
- **Project period:** 2013-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10405897

## Citation

> US National Institutes of Health, RePORTER application 10405897, AI training module for Vision Science (3T32EY023211-08S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10405897. Licensed CC0.

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