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

Condition(s) studied

Investigational drug(s) / intervention(s)

MRI scan，ocular ultrasound: 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 summary

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.

Eligibility

Primary outcome measure(s)

• Diagnostic accuracy (AUC) of the AI model for TED — From enrollment to the end of follow-up at 18 months
  using comprehensive clinical diagnosis (Bartley criteria) as the gold standard.
• Classification accuracy of the AI model for distinguishing active versus inactive TED — From enrollment to the end of follow-up at 18 months
• Classification accuracy of the AI model for distinguishing mild versus moderate to severe TED — From enrollment to the end of follow-up at 18 months

Trial sites (1)

More Shanghai Changzheng Hospital trials in China

Other trials for the same condition