Stuart Keel1, Zhixi Li2, Jane Scheetz1, Liubov Robman1,3, James Phung3, Galina Makeyeva1, KhinZaw Aung1, Chi Liu4, Xixi Yan1, Wei Meng4, Robyn Guymer1, Robert Chang5, Mingguang He1,2. 1. Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, Melbourne, Victoria, Australia. 2. State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, China. 3. Monash University Melbourne, Melbourne, Victoria, Australia. 4. Healgoo Interactive Medical Technology Co. Ltd., Guangzhou, China. 5. Department of Ophthalmology, Byers Eye Institute at Stanford University, Palo Alto, California.
Abstract
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.
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.
Authors: John P Campbell; Ciku Mathenge; Hunter Cherwek; Konstantinos Balaskas; Louis R Pasquale; Pearse A Keane; Michael F Chiang Journal: Transl Vis Sci Technol Date: 2021-03-01 Impact factor: 3.283
Authors: Ravi Aggarwal; Viknesh Sounderajah; Guy Martin; Daniel S W Ting; Alan Karthikesalingam; Dominic King; Hutan Ashrafian; Ara Darzi Journal: NPJ Digit Med Date: 2021-04-07