Accepted for/Published in: JMIR Human Factors
Date Submitted: Aug 22, 2025
Date Accepted: Feb 21, 2026
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Glorbit: A Modular, Web-Based Platform for AI Based Periorbital Measurement in Low-Resource Settings
ABSTRACT
Background:
Periorbital measurements such as margin reflex distances (MRD1/2), palpebral fissure height, and scleral show are critical in diagnosing and managing conditions like ptosis and disorders of the eyelid.
Objective:
We developed and evaluated Glorbit, a lightweight, browser-based application for automated periorbital distance measurement using artificial intelligence (AI), designed for deployment in low-resource clinical environments. The goal was to assess its usability, cross-platform functionality, and readiness for real-world field deployment.
Methods:
The application integrates a DeepLabV3 segmentation model into a modular image processing pipeline with secure, site-specific Google Cloud storage. Glorbit supports offline mode, local preprocessing, and cloud upload through Firebase-authenticated logins. The full workflow—metadata entry, facial image capture, segmentation, and upload—was tested. Post-session, participants completed a Likert-style usability survey.
Results:
Glorbit successfully ran on all tested platforms, including laptops, tablets, and mobile phones across major browsers. A total of 15 volunteers were enrolled in this study where the app completed the full workflow without error on 100% of patients. The segmentation model succeeded on all images, and average session duration was 101.7 ± 17.5 seconds. Usability scores on a 5-point Likert scale were uniformly high: intuitiveness and efficiency (5.0 ± 0.0), workflow clarity (4.8 ± 0.4), output confidence (4.9 ± 0.3), and clinical usability (4.9 ± 0.3).
Conclusions:
Glorbit is a functional, cross-platform solution for standardized periorbital measurement in clinical and low-resource settings. By combining a local image processing with secure, modular data storage and offline compatibility, the tool enables scalable deployment and secure data collection. These features support broader efforts in AI-driven oculoplastics including future development of real-time triage tools and multimodal datasets for personalized ophthalmic care. Clinical Trial: STUDY2025-0731
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.