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Graves' Disease I: Introduction

Graves' Disease I: Introduction

Graves' disease is an autoimmune disorder that causes hyperthyroidism, or overactivity of the thyroid gland. It results from autoantibodies called thyroid-stimulating immunoglobulins (TSIs), which bind to thyroid-stimulating hormone (TSH) receptors, leading to overstimulation of hormone production and a hypermetabolic state.EtiologyAlthough considered idiopathic, Graves’ disease has well-established contributing factors. There is a strong genetic component, with increased prevalence in...

Graves Disease II: Pathophysiology

Graves Disease II: Pathophysiology

Graves’ disease is an autoimmune disorder characterized by the production of thyroid-stimulating immunoglobulins (TSI) that activate TSH receptors, leading to excessive synthesis and release of thyroid hormones (T3 and T4) and resulting in hyperthyroidism.Among all causes of hyperthyroidism, Graves’ disease is the most common and can happen at any age, though it is more frequent in women. It produces a hypermetabolic state with features such as weight loss, tachycardia, tremor, and heat...

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Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

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Artificial intelligence in thyroid eye disease imaging: A systematic review.

Haiyang Zhang¹, Ziyuan Li¹, Hoi Chi Chan¹

¹State Key Laboratory of Eye Health, Department of Ophthalmology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Survey of Ophthalmology

|July 24, 2025

Summary

Artificial intelligence (AI) shows promise for diagnosing and treating thyroid eye disease (TED). This review of 41 studies highlights AI

Keywords:

Artificial intelligence Medical imaging Thyroid eye disease

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Thyroid eye disease (TED) is a complex orbital disorder with visible soft-tissue changes.
Current research lacks a systematic review of artificial intelligence (AI) applications in TED.

Purpose of the Study:

To systematically review and characterize the research landscape of AI in TED diagnosis and treatment.
To identify key challenges and future directions for AI in TED research.

Main Methods:

PRISMA guidelines followed for systematic database searches until January 2025.
Included 41 studies applying AI to CT, MRI, and other imaging modalities in TED patients.
Assessed study quality using the APPRAISE-AI tool.

Main Results:

Predominantly East Asian participants (33-2288 per study); CT and facial imaging most common modalities.
AI applications focused on diagnosis, activity assessment, severity classification, and treatment prediction.
Deep learning models (ResNet, VGG) were frequently used; most studies were of moderate quality.

Conclusions:

Image-based AI holds significant potential for enhancing diagnostic accuracy in TED.
AI can guide personalized treatment strategies for thyroid eye disease.
Future research needs robust designs, public datasets, multimodal imaging, and collaboration for clinical translation.