Development and validation of a deep‐learning algorithm for the detection of neovascular age‐related macular degeneration from colour fundus photographs

Importance Detection of early onset neovascular age‐related macular degeneration (AMD) is critical to protecting vision. Background To describe the development and validation of a deep‐learning algorithm (DLA) for the detection of neovascular age‐related macular degeneration. Design Development and...

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Vydané v:Clinical & experimental ophthalmology Ročník 47; číslo 8; s. 1009 - 1018
Hlavní autori: Keel, Stuart, Li, Zhixi, Scheetz, Jane, Robman, Liubov, Phung, James, Makeyeva, Galina, Aung, KhinZaw, Liu, Chi, Yan, Xixi, Meng, Wei, Guymer, Robyn, Chang, Robert, He, Mingguang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Melbourne John Wiley & Sons Australia, Ltd 01.11.2019
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Predmet:
Age
age‐related macular degeneration
Algorithms
Datasets
Deep learning
deep‐learning algorithm
Macular degeneration
Retina
retinal‐imaging
age-related macular degeneration
deep-learning algorithm
retinal-imaging
ISSN:1442-6404, 1442-9071, 1442-9071
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Shrnutí:Importance Detection of early onset neovascular age‐related macular degeneration (AMD) is critical to protecting vision. Background To describe the development and validation of a deep‐learning algorithm (DLA) for the detection of neovascular age‐related macular degeneration. Design Development and validation of a DLA using retrospective datasets. Participants We developed and trained the DLA using 56 113 retinal images and an additional 86 162 images from an independent dataset to externally validate the DLA. All images were non‐stereoscopic and retrospectively collected. Methods The internal validation dataset was derived from real‐world clinical settings in China. Gold standard grading was assigned when consensus was reached by three individual ophthalmologists. The DLA classified 31 247 images as gradable and 24 866 as ungradable (poor quality or poor field definition). These ungradable images were used to create a classification model for image quality. Efficiency and diagnostic accuracy were tested using 86 162 images derived from the Melbourne Collaborative Cohort Study. Neovascular AMD and/or ungradable outcome in one or both eyes was considered referable. Main Outcome Measures Area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results In the internal validation dataset, the AUC, sensitivity and specificity of the DLA for neovascular AMD was 0.995, 96.7%, 96.4%, respectively. Testing against the independent external dataset achieved an AUC, sensitivity and specificity of 0.967, 100% and 93.4%, respectively. More than 60% of false positive cases displayed other macular pathologies. Amongst the false negative cases (internal validation dataset only), over half (57.2%) proved to be undetected detachment of the neurosensory retina or RPE layer. Conclusions and Relevance This DLA shows robust performance for the detection of neovascular AMD amongst retinal images from a multi‐ethnic sample and under different imaging protocols. Further research is warranted to investigate where this technology could be best utilized within screening and research settings.
Bibliografia:ObjectType-Article-1
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ISSN:1442-6404
1442-9071
1442-9071
DOI:10.1111/ceo.13575