ReviewFrontiers in cell and developmental biology2022
Orbital and eyelid diseases: The next breakthrough in artificial intelligence?
Review in Frontiers in cell and developmental biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 24 citations in OpenAlex.
- Artificial Intelligence in Ocular Surface Tumors: Current Advances, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- Management of Thyroid Eye Disease: A Comparison Between Three Recent Clinical Guidelines.Ophthalmology and therapy · 2026Review
- Deep Learning-based Assessment of Eyelid and Periorbital Parameters: Assisting Diagnosis and Treatment Planning in Blepharoptosis.Journal of medical systems · 2026Article
- Deep learning model for differentiating thyroid eye disease and orbital myositis on computed tomography (CT) imaging.Orbit (Amsterdam, Netherlands) · 2025Article
- Artificial Intelligence in Oculoplastics: A Survey-Based Study on Provider Perspective.Cureus · 2025Article
- AI-assisted facial analysis in healthcare: From disease detection to comprehensive management.Patterns (New York, N.Y.) · 2025Review
- Modern Technologies inSovremennye tekhnologii v meditsine · 2025Review
- Applications of Artificial Intelligence in Ophthalmology: Glaucoma, Cornea, and Oculoplastics.Cureus · 2024Review
- Multidimensional quantitative characterization of periocular morphology: distinguishing esotropia from epicanthus by deep learning network.Quantitative imaging in medicine and surgery · 2024Article
- Nonspecific Orbital Inflammation (NSOI): Unraveling the Molecular Pathogenesis, Diagnostic Modalities, and Therapeutic Interventions.International journal of molecular sciences · 2024Review
- Advances in artificial intelligence in thyroid-associated ophthalmopathy.Frontiers in endocrinology · 2024Review
- End-to-End Deep-Learning-Based Diagnosis of Benign and Malignant Orbital Tumors on Computed Tomography Images.Journal of personalized medicine · 2023Article
- A deep learning-based image analysis for assessing the extent of abduction in abducens nerve palsy patients before and after strabismus surgery.Advances in ophthalmology practice and researchArticle
- A Deep Learning Model for Screening Computed Tomography Imaging for Thyroid Eye Disease and Compressive Optic Neuropathy.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Orbital and eyelid disorders affect normal visual functions and facial appearance, and precise oculoplastic and reconstructive surgeries are crucial. Artificial intelligence (AI) network models exhibit a remarkable ability to analyze large sets of medical images to locate lesions. Currently, AI-based technology can automatically diagnose and grade orbital and eyelid diseases, such as thyroid-associated ophthalmopathy (TAO), as well as measure eyelid morphological parameters based on external ocular photographs to assist surgical strategies. The various types of imaging data for orbital and eyelid diseases provide a large amount of training data for network models, which might be the next breakthrough in AI-related research. This paper retrospectively summarizes different imaging data aspects addressed in AI-related research on orbital and eyelid diseases, and discusses the advantages and limitations of this research field.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.