Precise diagnosis and treatment system for thyroid-associated eye disease
Construction and Application of Precise Diagnosis and Treatment System for Thyroid-associated Ophthalmopathy
Second Affiliated Hospital, School of Medicine, Zhejiang University · NCT07124572
This project will test whether a deep-learning system using eye images and exams can automatically diagnose, stage, and help plan treatment for people with thyroid-associated ophthalmopathy.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 500 (estimated) |
| Ages | 7 Years to 90 Years |
| Sex | All |
| Sponsor | Second Affiliated Hospital, School of Medicine, Zhejiang University (other) |
| Locations | 1 site (Hangzhou, Zhejiang) |
| Trial ID | NCT07124572 on ClinicalTrials.gov |
What this trial studies
The study will develop a deep learning model that integrates multimodal data from imaging and ocular examinations to provide automated diagnosis, staging, grading, treatment suggestions, and prognosis predictions for thyroid-associated ophthalmopathy (TAO). Researchers will collect standardized eye images and clinical exam data from both patients with TAO and control participants without eyelid disorders to train and validate the system. Observational methods will be used to compare model outputs to clinical labels and current diagnostic standards, with cases meeting exclusion criteria (pupil/cornea disorders, prior eye injury) omitted. The goal is to create a tool that supports clinicians by synthesizing image and exam data into consistent, actionable reports.
Who should consider this trial
Good fit: Ideal candidates are cooperative adults with thyroid-associated ophthalmopathy who can undergo imaging and ocular examinations, alongside control participants without eyelid disorders for comparison.
Not a fit: People with pupil or corneal disorders or a history of eye injury are excluded and are unlikely to benefit from this program.
Why it matters
Potential benefit: If successful, the system could speed and standardize diagnosis and treatment decisions for TAO, potentially leading to earlier and more consistent care.
How similar studies have performed: Deep learning has shown promise in several ophthalmic diagnoses, but applying multimodal AI specifically to thyroid-associated ophthalmopathy is relatively new.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * normal participants without eyelid disorders (control group) * patients with thyroid-associated ophthalmopathy (TAO group) * cooperative participants Exclusion Criteria: * participants with pupil or cornea disorders * participants with history of eye injury
Where this trial is running
Hangzhou, Zhejiang
- Juan Ye — Hangzhou, Zhejiang, China (RECRUITING)
Study contacts
- Study coordinator: Juan Ye
- Email: yejuan@zju.edu.cn
- Phone: +86-571-87783897
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.
Conditions: Thyroid Associated Ophthalmopathy