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Author Spotlight: Integrating Ultrasound Imaging with Biochemical Markers for Thyroid Disease Diagnosis
Published on: February 9, 2024
588
Advances in artificial intelligence in thyroid-associated ophthalmopathy.
Chenyuan Yi1, Geng Niu2, Yinghuai Zhang1
1Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China.
Frontiers in Endocrinology
|May 8, 2024
Summary
Artificial intelligence (AI) and deep learning (DL) show promise in diagnosing and managing Thyroid-Associated Ophthalmopathy (TAO), also known as Graves
Area of Science:
- Ophthalmology
- Endocrinology
- Medical Imaging
Background:
- Thyroid-associated ophthalmopathy (TAO), or Graves' ophthalmopathy, involves ocular complications from autoimmune thyroid disease.
- Diagnosis relies on imaging, ocular symptoms, and thyroid function/antibody tests, with established grading and staging systems.
Purpose of the Study:
- To review the application of Artificial Intelligence (AI), specifically deep learning (DL), in the diagnosis and management of TAO.
- To highlight AI's role in TAO diagnosis, staging, grading, and treatment decision-making.
Main Methods:
- Review of studies utilizing AI and DL for TAO.
- Analysis of AI applications in various aspects of TAO assessment and treatment.
Main Results:
- AI and DL are increasingly used in ophthalmic disease diagnosis and treatment.
- Specific studies demonstrate AI's utility in TAO diagnosis, staging, grading, and treatment planning.
Conclusions:
- AI, particularly DL, offers significant potential for improving TAO diagnosis and management.
- Further research is needed to address current limitations and explore future directions in AI for TAO.
