ArticleBMC ophthalmology2021
Detection of active and inactive phases of thyroid-associated ophthalmopathy using deep convolutional neural network.
Article in BMC ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Optical coherence tomography angiography in thyroid associated ophthalmopathy: a systematic review.BMC ophthalmology · 2024Pooled it
- Current insights of applying MRI in Graves' ophthalmopathy.Frontiers in endocrinology · 2022Pooled it
- Integrating Robotic Bilateral Axillo-Breast Approach Thyroidectomy with Molecular Diagnostics and Artificial Intelligence in Thyroid Cancer Care.Biomolecules & therapeutics · 2026Review
- Interpretable machine learning for severity classification of thyroid eye disease using orbital anatomical features.Frontiers in medicine · 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
- AI-based assessment of Clinical Activity Score and detection of active thyroid eye disease using facial images: validation of Glandy CAS.BMJ open ophthalmology · 2025Article
- Predictive modeling of graves' orbitopathy activity based on meibomian glands analysis using in vivo confocal microscopy.BMC endocrine disorders · 2025Article
- Artificial Intelligence in Oculoplastics: A Survey-Based Study on Provider Perspective.Cureus · 2025Article
- Advancements in imaging research in thyroid-associated ophthalmopathy.Frontiers in endocrinology · 2025Review
- Cerium oxide nanoparticles attenuates fibrosis and inflammation in thyroid-associated ophthalmopathy via JNK pathway.Frontiers in molecular biosciences · 2025Article
- The application of artificial intelligence-generated content in ophthalmology education.Frontiers in medicine · 2025Review
- Multimodal MRI radiomics-clinical fusion model predicts intravenous glucocorticoid response in thyroid eye disease.Frontiers in endocrinology · 2025Article
- Applications of Artificial Intelligence in Ophthalmology: Glaucoma, Cornea, and Oculoplastics.Cureus · 2024Review
- Advances in artificial intelligence in thyroid-associated ophthalmopathy.Frontiers in endocrinology · 2024Review
- Facial expression of patients with Graves' orbitopathy.Journal of endocrinological investigation · 2023Article
- Deep learning-based diagnosis of disease activity in patients with Graves' orbitopathy using orbital SPECT/CT.European journal of nuclear medicine and molecular imaging · 2023Article
- Neural network application for assessing thyroid-associated orbitopathy activity using orbital computed tomography.Scientific reports · 2023Article
- Artificial Intelligence in Thyroidology: A Narrative Review of the Current Applications, Associated Challenges, and Future Directions.Thyroid : official journal of the American Thyroid Association · 2023Review
- Application of artificial intelligence in the assessment of thyroid eye disease (TED) - a scoping review.Frontiers in endocrinology · 2023Article
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Authors and funding
8 authors.
Funding
Abstract
backgroundThis study aimed to establish a deep learning system for detecting the active and inactive phases of thyroid-associated ophthalmopathy (TAO) using magnetic resonance imaging (MRI). This system could provide faster, more accurate, and more objective assessments across populations.
methodsA total of 160 MRI images of patients with TAO, who visited the Ophthalmology Clinic of the Ninth People's Hospital, were retrospectively obtained for this study. Of these, 80% were used for training and validation, and 20% were used for testing. The deep learning system, based on deep convolutional neural network, was established to distinguish patients with active phase from those with inactive phase. The accuracy, precision, sensitivity, specificity, F1 score and area under the receiver operating characteristic curve were analyzed. Besides, visualization method was applied to explain the operation of the networks.
resultsNetwork A inherited from Visual Geometry Group network. The accuracy, specificity and sensitivity were 0.863±0.055, 0.896±0.042 and 0.750±0.136 respectively. Due to the recurring phenomenon of vanishing gradient during the training process of network A, we added parts of Residual Neural Network to build network B. After modification, network B improved the sensitivity (0.821±0.021) while maintaining a good accuracy (0.855±0.018) and a good specificity (0.865±0.021).
conclusionsThe deep convolutional neural network could automatically detect the activity of TAO from MRI images with strong robustness, less subjective judgment, and less measurement error. This system could standardize the diagnostic process and speed up the treatment decision making for TAO.
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