ArticleScientific reports2022
Neural network-based method for diagnosis and severity assessment of Graves' orbitopathy using orbital computed tomography.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Optical coherence tomography angiography in thyroid associated ophthalmopathy: a systematic review.BMC ophthalmology · 2024Pooled it
- The therapeutic revolution in thyroid eye disease: from orbital radiotherapy to teprotumumab and AI.Frontiers in medicine · 2026Review
- Deep learning model for differentiating thyroid eye disease and orbital myositis on computed tomography (CT) imaging.Orbit (Amsterdam, Netherlands) · 2025Article
- Deep learning methods for diagnosis of graves' ophthalmopathy using magnetic resonance imaging.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
- Application of artificial intelligence in the assessment of thyroid eye disease (TED) - a scoping review.Frontiers in endocrinology · 2023Article
- Research progress and application of artificial intelligence in thyroid associated ophthalmopathy.Frontiers in cell and developmental biology · 2023Review
- 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
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computed tomography (CT) has been widely used to diagnose Graves' orbitopathy, and the utility is gradually increasing. To develop a neural network (NN)-based method for diagnosis and severity assessment of Graves' orbitopathy (GO) using orbital CT, a specific type of NN optimized for diagnosing GO was developed and trained using 288 orbital CT scans obtained from patients with mild and moderate-to-severe GO and normal controls. The developed NN was compared with three conventional NNs [GoogleNet Inception v1 (GoogLeNet), 50-layer Deep Residual Learning (ResNet-50), and 16-layer Very Deep Convolutional Network from Visual Geometry group (VGG-16)]. The diagnostic performance was also compared with that of three oculoplastic specialists. The developed NN had an area under receiver operating curve (AUC) of 0.979 for diagnosing patients with moderate-to-severe GO. Receiver operating curve (ROC) analysis yielded AUCs of 0.827 for GoogLeNet, 0.611 for ResNet-50, 0.540 for VGG-16, and 0.975 for the oculoplastic specialists for diagnosing moderate-to-severe GO. For the diagnosis of mild GO, the developed NN yielded an AUC of 0.895, which is better than the performances of the other NNs and oculoplastic specialists. This study may contribute to NN-based interpretation of orbital CTs for diagnosing various orbital diseases.
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