Development of a deep-learning system for detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus images: a pilot study
| Autor: | Chenxi Zhang, Weihong Yu, Youxin Chen, Xixi He, Bing Li, Xirong Li, Hao Wang, Feng He |
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| Rok vydání: | 2021 |
| Předmět: |
Retinal breaks
Fundus Oculi Computer science Pilot Projects Fundus (eye) Convolutional neural network 03 medical and health sciences Cellular and Molecular Neuroscience chemistry.chemical_compound Deep Learning 0302 clinical medicine medicine Humans Computer vision 030304 developmental biology 0303 health sciences business.industry Deep learning Retinal Degeneration Retinal Detachment Retinal detachment Retinal Retinal Perforations medicine.disease Sensory Systems Ophthalmology chemistry Lattice degeneration 030221 ophthalmology & optometry Ultra wide field Artificial intelligence business |
| Zdroj: | Graefe's Archive for Clinical and Experimental Ophthalmology. 259:2225-2234 |
| ISSN: | 1435-702X 0721-832X |
| DOI: | 10.1007/s00417-021-05105-3 |
| Popis: | To investigate the detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus imaging system (Optos) with convolutional neural network technology. This study included 1500 Optos color images for tessellated fundus confirmation and peripheral retinal lesion (lattice degeneration, retinal breaks, and retinal detachment) assessment. Three retinal specialists evaluated all images and proposed the reference standard when an agreement was achieved. Then, 722 images were used to train and verify a combined deep-learning system of 3 optimal binary classification models trained using seResNext50 algorithm with 2 preprocessing methods (original resizing and cropping), and a test set of 189 images were applied to verify the performance compared to the reference standard. With optimal preprocessing approach (original resizing method for lattice degeneration and retinal detachment, cropping method for retinal breaks), the combined deep-learning system exhibited an area under curve of 0.888, 0.953, and 1.000 for detection of lattice degeneration, retinal breaks, and retinal detachment respectively in tessellated eyes. The referral accuracy of this system was 79.8% compared to the reference standard. A deep-learning system is feasible to detect lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field images. And this system may be considered for screening and telemedicine. |
| Databáze: | OpenAIRE |
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