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Differentiating Glaucomatous Optic Neuropathy From Non-glaucomatous Optic Neuropathies Using Deep Learning Algorithms
Mahsa Vali1, Massood Mohammadi2, Nasim Zarei2
1From the Department of Electrical and Computer Engineering (M.V.), Isfahan University of Technology, Isfahan, Iran.
American Journal of Ophthalmology
|March 3, 2023
Summary
A deep learning algorithm effectively distinguishes glaucomatous optic neuropathy (GON) from non-glaucomatous optic neuropathies (NGONs). This AI tool shows higher sensitivity than human specialists, offering promising results for diagnosing optic disc changes.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Differentiating glaucomatous optic neuropathy (GON) from non-glaucomatous optic neuropathies (NGONs) is crucial for accurate diagnosis and treatment.
- Optic disc changes can be subtle and challenging to distinguish, necessitating advanced diagnostic tools.
Purpose of the Study:
- To develop and evaluate a deep learning framework for differentiating glaucomatous optic disc changes (GON) from non-glaucomatous optic disc changes (NGONs).
Main Methods:
- A deep-learning system was trained and validated on 2183 digital color fundus photographs, utilizing an optic disc segmentation network and transfer learning.
- The system was tested on a single-center dataset and four external datasets to assess its classification performance for normal, GON, and NGON optic discs.
Main Results:
- The best-performing algorithm, DenseNet121, achieved high sensitivity (95.36%) and specificity (92.19%) on the single-center dataset.
- On external validation, the network demonstrated a sensitivity of 85.53% and specificity of 89.02% for differentiating GON from NGON, outperforming a glaucoma specialist's diagnostic accuracy.
Conclusions:
- The proposed deep learning algorithm shows superior sensitivity compared to glaucoma specialists in differentiating GON from NGON.
- The algorithm's strong performance on unseen data indicates its significant potential as a diagnostic aid in ophthalmology.
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