Digital Eye Imaging for Remote Thyroid Eye Disease Scoring

The patent uses image data captured by a digital camera to create a visual copy of the eye condition, replacing the need for a doctor's direct observation. The system processes this image copy through machine learning models to automatically assess clinical activity score components, enabling remote monitoring without requiring the patient to visit a hospital for physical examination.

The patent replaces the mechanical/physical examination process (doctor's naked eye observation and interview) with an automated digital imaging and machine learning system. The machine learning model processes digital images to automatically evaluate symptoms like conjunctival hyperemia, edema, eyelid redness, and lacrimal edema, substituting the manual medical examination with an automated electronic system.

The patent employs a digital camera, which is a simple and inexpensive device that ordinary people can use, to capture eye condition images. This replaces the need for complex professional medical diagnostic equipment. The camera captures images that are then processed by machine learning models, enabling early diagnosis capability using affordable, accessible technology rather than expensive specialized equipment.

The system enables individuals to perform self-monitoring of their thyroid eye disease condition by capturing images with a digital camera and automatically processing them through machine learning models. This self-service capability allows patients to independently track their clinical activity score components without requiring professional medical equipment or expertise, thereby reducing system complexity while maintaining early diagnosis reliability.