# iLIGN - Artificial Intelligence Driven Strabismus Pre-Screening System

> **NIH NIH R43** · THER-AI LLC · 2024 · $295,766

## Abstract

SUMMARY
The overall goal of this project is to develop a rapid, non-invasive, accurate, and low-cost smartphone
application (app), called iLIGN, to perform automated pre-screening using artificial intelligence for diagnosing
ocular misalignment (Strabismus) by individuals without specialty expertise or training. Strabismus, commonly
referred to as "crossed eyes" or "squint," is an ocular condition whereby the eyes are not properly aligned. The
worldwide estimated prevalence in children is ~2%, being higher in underdeveloped countries (5.7%) than
developed countries (1.3%). In adults, the lifetime risk of being diagnosed with new-onset strabismus is
approximately 1-in-25 subjects. To prevent irreversible vision loss, it is critical to evaluate children of all
populations for ocular alignment as early as possible. Poor academic performance, diminished workplace
achievement, esthetic dissatisfaction, low self-esteem, delayed developmental milestones, and familial and
societal low acceptance are well-known outcomes. Therefore, early diagnosis and therapy restores binocular
vision, eliminates permanent vision loss, and lessens the need for repeated lifetime treatments. Once diagnosed,
therapy can be as simple as prescribing glasses and covering the normal eye with a patch while the weaker eye
improves, or for severe cases surgical correction and re-alignment.
 Even in developed nations, strabismus screening requires not only qualified healthcare professionals but
also specialized devices. The American Academy of Pediatrics recommends a visual acuity screen for 4- and 5-
year-olds and cooperative 3-year-olds. Traditional eye charts can detect visual impairments from ages 3 to 5 but
with limitations. These are often tedious and result in inaccurate findings. Due to this reason, instrument-based
screening can be utilized for children up until 5 years of age. The American Academy of Pediatrics and American
Academy of Ophthalmology currently recommended photo-screening for children under 3 years old. All current
methods require qualified healthcare personnel as operators. A mobile app is in development for strabismus
screening but, again, for use only by trained technicians. Our solution will utilize AI models on current photos to
identify strabismus without the requirement for specialists with specialized training. In Aim 1 we will develop an
image capture protocol and data curation and in Aim 2 we will develop an automated image analysis software
for deep learning-based strabismus diagnosis. iLIGN’s key innovation is in allowing untrained end-users with
automated software assessment to diagnose and thereby prevent a condition which often geos undiagnosed
without requisite specialty training and healthcare resources, and untreated carries enormous financial and
societal impact. The expected outcome of this project is a software platform (smartphone app), iLIGN, a
paradigm shift in the pre-screening of vision-threatening strabismus, which is a leading caus...

## Key facts

- **NIH application ID:** 10921579
- **Project number:** 1R43EY035599-01A1
- **Recipient organization:** THER-AI LLC
- **Principal Investigator:** Saad Ainuddin Shaikh
- **Activity code:** R43 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $295,766
- **Award type:** 1
- **Project period:** 2024-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10921579

## Citation

> US National Institutes of Health, RePORTER application 10921579, iLIGN - Artificial Intelligence Driven Strabismus Pre-Screening System (1R43EY035599-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10921579. Licensed CC0.

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