About this trial
This study aims to develop the deep learning-based system that integrates multimodal information to automatically diagnose, stage, and grade thyroid-associated ophthalmopathy (TAO), assist in treatment decision-making, and predict patient prognosis, thereby providing novel tools to support clinical management.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
normal participants without eyelid disorders (control group)
patients with thyroid-associated ophthalmopathy (TAO group)
cooperative participants
Disqualifiers
participants with pupil or cornea disorders
participants with history of eye injury
Trial population
500 patients with thyroid-associated ophthalomopahty 500 patients without thyroid-associated ophthalomopahty
Trial design
Other
Prospective
Treatments tested in this trial
Imaging and ocular examinations
Other intervention2D and 3D Facial photography, blinking videos, orbital CT and MRI images, fundus images and images of nine gaze positions are taken. Clinical information is recorded. Quality of life is assessed. Ocular examinations, such as visual acuity, levator function, exophthalmos measurement, strabismus test, visual field examination, axial length measurement, OCT, OCTA are made.
Treatment groups
Trial outcomes
Primary outcomes
eye morphological parameters
Margin reflex distances(mm)
Blinking feature of patients with thyroid-associated ophthalmopathy
Blinking frequency
Ocular movement measurement
Ocular movement ability in nine gaze positions
Automatic orbital structures segmentation and measurement using deep learning methods
Automatic segmentation and measurement of orbital structures,such as eyeball, extraocular muscle, orbital fat, bony orbital region, optic nerves, etc
Sponsors and contacts
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