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Detection of active and inactive phases of thyroid-associated ophthalmopathy using deep convolutional neural network
- Source :
- BMC Ophthalmology, Vol 21, Iss 1, Pp 1-9 (2021), BMC Ophthalmology
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
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.
- Subjects :
- Convolutional neural network
030218 nuclear medicine & medical imaging
03 medical and health sciences
Magnetic resonance imaging
0302 clinical medicine
lcsh:Ophthalmology
Robustness (computer science)
Machine learning
medicine
Humans
Sensitivity (control systems)
Thyroid-associated ophthalmopathy
Retrospective Studies
medicine.diagnostic_test
Receiver operating characteristic
business.industry
Deep learning
Pattern recognition
General Medicine
Visualization
Graves Ophthalmopathy
Ophthalmology
ROC Curve
lcsh:RE1-994
030221 ophthalmology & optometry
Neural Networks, Computer
Artificial intelligence
business
F1 score
Research Article
Subjects
Details
- ISSN :
- 14712415
- Volume :
- 21
- Database :
- OpenAIRE
- Journal :
- BMC Ophthalmology
- Accession number :
- edsair.doi.dedup.....c31bcb9db71e0d89cb7b4c28acf98830