Prahs, P. and Maerker, D. and Mayer, C. and Helbig, H. (2018) Deep learning to support therapy decisions for intravitreal injections. OPHTHALMOLOGE, 115 (9). pp. 722-727. ISSN 0941-293X, 1433-0423
Full text not available from this repository. (Request a copy)Abstract
Significant progress has been made in artificial intelligence and computer vision research in recent years. Machine learning methods excel in awide variety of tasks where sufficient data are available. We describe the application of adeep convolutional neural network for the prediction of treatment indication with anti-vascular endothelial growth factor (VEGF) medications based on central retinal optical coherence tomography (OCT) scans. The neural network classifier was trained with OCT images acquired during routine treatment at the University of Regensburg over the years 2008-2016. In over 95% of the cases the treatment indication was accurately predicted based on asingular OCTB scan without human intervention. Despite promising classification the results of deep learning techniques, should always be controlled by the treating physician because false classification can never be excluded due to the probabilistic nature of the method.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | OPTICAL COHERENCE TOMOGRAPHY; MACULAR DEGENERATION; ALGORITHM; Optical coherence tomography; Age-related macular degeneration; Diabetic retinopathy; Anti-vascular endothelial growth factor; Artificial intelligence |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Augenheilkunde |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 09 Jan 2020 12:41 |
| Last Modified: | 09 Jan 2020 12:41 |
| URI: | https://pred.uni-regensburg.de/id/eprint/13965 |
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