# TRACHOMA SURVEILLANCE AT SCALE: AUTOMATIC DISEASE GRADING OF EYELID PHOTOS

> **NIH NIH R21** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $242,250

## Abstract

PROJECT SUMMARY
Trachoma is the leading cause of infectious blindness worldwide. The WHO has set a goal of controlling
trachoma to a low enough level that blindness from the disease is no longer a public health concern. Control is
defined as a district-level prevalence of follicular trachomatous inflammation (TF) in the upper tarsal conjunctiva
of less than 5% in children, currently determined by clinical examination. While not required for the current
definition, intense trachomatous inflammation (TI) correlates better with presence of the causative agent,
Chlamydia trachomatis. Grading of both TF and TI vary widely between individuals, and even in the same
individual over time. As cases become rarer, training new graders becomes more difficult. As areas become
controlled, trachoma budgets are being cut, and the institutional knowledge of grading lost, making detection of
remaining cases and potential resurgence difficult. One of the greatest obstacles to reaching our trachoma goals
is an inadequate diagnostic test. The WHO relies on field grading of TF; human inconsistency, grader bias, and
training costs are becoming major obstacles, but they do not need to be. We propose to test the central
hypothesis that a fully automatic, deep learning grader can perform as well as trained physicians in detecting
and grading trachoma. The hypothesis will be tested in the following Specific aims: 1) Automatic identification of
follicles and grading of TF and 2) Automatic tarsal blood vessels detection and grading of TI. Our approach
includes the development, training and testing of novel image processing pipelines based on semantic
segmentation and disease classification using deep learning neural networks and state-of-the-art object
detection. All of the data to be used in this study is secondary data from NEI-funded and other trachoma clinical
trials conducted by our study team. We aim to facilitate widespread adoption of these novel tools across the
trachoma research and grading community, by open source availability of generated code and interoperability of
generated machine learning models across programming languages through use of the open neural networks
exchange format. Our proposed research addresses the problem of subjectivity, cost and reliability of human
trachoma grading. Successful completion of the proposed specific aims will also be a key step forward towards
future study and development of providing health organizations and research teams with a novel, efficient and
extensible tool to ensure objective, automated, scalable trachoma grading in the field to enhance, or in some
cases replace, traditional field grading during the critical endgame of trachoma control, as well surveillance for
potential resurgence.

## Key facts

- **NIH application ID:** 10196816
- **Project number:** 1R21EY032567-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** Luca Della Santina
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $242,250
- **Award type:** 1
- **Project period:** 2021-05-01 → 2021-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10196816

## Citation

> US National Institutes of Health, RePORTER application 10196816, TRACHOMA SURVEILLANCE AT SCALE: AUTOMATIC DISEASE GRADING OF EYELID PHOTOS (1R21EY032567-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10196816. Licensed CC0.

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