Observational studyScientific reports2022
Automated detection of enlarged extraocular muscle in Graves' ophthalmopathy with computed tomography and deep neural network.
Observational study in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Optical coherence tomography angiography in thyroid associated ophthalmopathy: a systematic review.BMC ophthalmology · 2024Pooled it
- A Deep Learning Framework for Predicting Teprotumumab Treatment Response in Thyroid Eye Disease.Ophthalmology science · 2026Article
- The Use of Deep Learning in Distinguishing Chalazion and Eyelid Mass.Journal of ophthalmology · 2026Article
- Deep learning model for differentiating thyroid eye disease and orbital myositis on computed tomography (CT) imaging.Orbit (Amsterdam, Netherlands) · 2025Article
- Oculoplastics and Augmented Intelligence: A Literature Review.Journal of clinical medicine · 2025Article
- Thyroid-stimulating immunoglobulin levels during low-dose antithyroid therapy predict Graves' disease relapse.Frontiers in endocrinology · 2025Article
- Deep learning methods for diagnosis of graves' ophthalmopathy using magnetic resonance imaging.Quantitative imaging in medicine and surgery · 2024Article
- Evolution and development of extraocular motor neurons, nerves and muscles in vertebrates.Annals of anatomy = Anatomischer Anzeiger : official organ of the Anatomische Gesellschaft · 2024Review
- Neural network application for assessing thyroid-associated orbitopathy activity using orbital computed tomography.Scientific reports · 2023Article
- Research progress and application of artificial intelligence in thyroid associated ophthalmopathy.Frontiers in cell and developmental biology · 2023Review
- Application of artificial intelligence in the assessment of thyroid eye disease (TED) - a scoping review.Frontiers in endocrinology · 2023Article
- Semantic Segmentation of Extraocular Muscles on Computed Tomography Images Using Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2022Article
- Orbital and eyelid diseases: The next breakthrough in artificial intelligence?Frontiers in cell and developmental biology · 2022Review
- A Deep Learning Model for Screening Computed Tomography Imaging for Thyroid Eye Disease and Compressive Optic Neuropathy.Ophthalmology scienceArticle
- Global research on artificial intelligence in thyroid-associated ophthalmopathy: A bibliometric analysis.Advances in ophthalmology practice and researchArticle
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to develop a diagnostic software system to evaluate the enlarged extraocular muscles (EEM) in patients with Graves' ophthalmopathy (GO) by a deep neural network.This prospective observational study involved 371 participants (199 EEM patients with GO and 172 controls with normal extraocular muscles) whose extraocular muscles were examined with orbital coronal computed tomography. When at least one rectus muscle (right or left superior, inferior, medial, or lateral) in the patients was 4.0 mm or larger, it was classified as an EEM patient with GO. We used 222 images of the data from patients as the training data, 74 images as the validation test data, and 75 images as the test data to "train" the deep neural network to judge the thickness of the extraocular muscles on computed tomography. We then validated the performance of the network. In the test data, the area under the curve was 0.946 (95% confidence interval (CI) 0.894-0.998), and receiver operating characteristic analysis demonstrated 92.5% (95% CI 0.796-0.984) sensitivity and 88.6% (95% CI 0.733-0.968) specificity. The results suggest that the deep learning system with the deep neural network can detect EEM in patients with GO.
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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.