Screening, Classification, and Outcome Prediction Ensemble Using Orbital MRI for Thyroid Eye Disease: The TED SCOPE National Multicenter Registry Study

Sponsor
Shanghai Changzheng Hospital
Study ID
NCT07716397
Status
Recruiting
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Conditions

  • Graves Disease
  • Graves Ophthalmopathy
  • Thyroid Associated Ophthalmopathies

Eligibility Criteria

Sex
ALL
Age
18 Years - 75 Years
Healthy Volunteers
Accepted

Interventions

  • MRI scan，ocular ultrasound — DIAGNOSTIC_TEST
    Participants will undergo a standardized, study - specific multimodal orbital MRI scan (sequences include T1WI, T2WI, STIR, and research sequences such as Magic, IDEAL - IQ, DWI, ASL, CEST).

Study Details

The goal of this observational study is to prospectively validate the efficacy of an AI multimodal model constructed based on multi - sequence orbital MRI in the diagnosis, activity and severity assessment, and prognosis prediction of thyroid - associated ophthalmopathy (TAO) in real - world clinical scenarios. The main questions it aims to answer are: Can AI models accurately assess the presence, activity, and severity of Thyroid-Associated Orbitopathy (TAO)? Can AI models predict the prognosis of TAO? Researchers will compare the diagnostic accuracy of the AI model for TAO patients and healthy subjects to evaluate its diagnostic performance. Participants will undergo a standardized, study - specific multimodal orbital MRI scan (sequences include T1WI, T2WI, STIR, and research sequences such as Magic, IDEAL - IQ, DWI, ASL, CEST). And will systematically acquire ocular ultrasound images from TED patients (active and inactive stages), non-TED ophthalmic disease controls, and healthy volunteers. AI-driven deep learning techniques (convolutional neural networks) will be applied to achieve automatic segmentation of key structures (extraocular muscles, optic nerve, lacrimal gland, and retrobulbar soft tissue). High-throughput radiomic features encompassing morphological parameters and gray-level texture patterns will be extracted. Machine learning algorithms will then be employed to construct objective prediction models for TED screening and activity staging, with MRI findings and CAS scores serving as the reference standards for external validation.

Key Dates

First listed
Jul 21, 2026
Start date
Jan 1, 2026
Status verified
Jul 2026
Primary completion
Dec 31, 2028
Completion
Dec 31, 2028

Study Design

Enrollment
1,200 participants (estimated)

Arms

  • Arm: TAO
    Participants with Thyroid Associated Ophthalmopathy
  • Arm: Normal
    healthy individuals without thyroid-associated ophthalmopathy

Primary Outcome Measure

Diagnostic accuracy (AUC) of the AI model for TED [ Time Frame: From enrollment to the end of follow-up at 18 months ]

Central Contacts

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