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Simulated Workflow Feasibility Evaluation of a Web-Based Periorbital Measurement Platform: Development and Usability

George R Nahass^1,2, Jacob van der Ende³, Sasha Hubschman²

¹Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL, United States.

JMIR Human Factors

|April 30, 2026

Summary

Gorbit, an AI-powered app, streamlines periorbital measurements for eyelid condition research. It successfully captured and analyzed images across devices in simulated tests, proving its workflow feasibility.

Keywords:

artificial intelligence deployable applications global health periorbital distance simulation

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Periorbital measurements are crucial for diagnosing and managing eyelid disorders like ptosis.
Current automated measurement tools lack integrated data capture and management infrastructure for research.

Purpose of the Study:

To develop and evaluate Glorbit, a browser-based application for automated periorbital distance measurement using AI.
To assess the end-to-end workflow feasibility of Glorbit under simulated, operator-run conditions.

Main Methods:

Gorbit integrates a DeepLabV3 segmentation model with Google Cloud storage and Firebase authentication.
The application supports local preprocessing, cloud upload, and metadata entry.
The complete workflow was tested in simulated, operator-run sessions, followed by participant surveys.

Main Results:

Gorbit ran successfully on various platforms (laptops, tablets, mobile phones) and browsers.
The application completed all workflow steps in simulated sessions with 15 volunteers.
The AI model produced outputs for all images, with an average session duration of 101.7 seconds and high user satisfaction.

Conclusions:

Gorbit is a cross-platform application enabling structured periorbital image capture and automated analysis.
The platform demonstrated successful workflow execution across devices in simulated testing.
Gorbit shows technical feasibility as a research data collection framework for future evaluations.