Freerk G Venhuizen1, Bram van Ginneken2, Freekje van Asten3, Mark J J P van Grinsven1, Sascha Fauser4, Carel B Hoyng3, Thomas Theelen3, Clara I Sánchez1. 1. Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands 2Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands. 2. Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands. 3. Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands. 4. Cologne University Eye Clinic, Cologne, Germany 4Roche Pharma Research and Early Development, F. Hoffmann-La Roche Ltd, Basel, Switzerland.
Abstract
Purpose: To evaluate a machine learning algorithm that automatically grades age-related macular degeneration (AMD) severity stages from optical coherence tomography (OCT) scans. Methods: A total of 3265 OCT scans from 1016 patients with either no signs of AMD or with signs of early, intermediate, or advanced AMD were randomly selected from a large European multicenter database. A machine learning system was developed to automatically grade unseen OCT scans into different AMD severity stages without requiring retinal layer segmentation. The ability of the system to identify high-risk AMD stages and to assign the correct severity stage was determined by using receiver operator characteristic (ROC) analysis and Cohen's κ statistics (κ), respectively. The results were compared to those of two human observers. Reproducibility was assessed in an independent, publicly available data set of 384 OCT scans. Results: The system achieved an area under the ROC curve of 0.980 with a sensitivity of 98.2% at a specificity of 91.2%. This compares favorably with the performance of human observers who achieved sensitivities of 97.0% and 99.4% at specificities of 89.7% and 87.2%, respectively. A good level of agreement with the reference was obtained (κ = 0.713) and was in concordance with the human observers (κ = 0.775 and κ = 0.755, respectively). Conclusions: A machine learning system capable of automatically grading OCT scans into AMD severity stages was developed and showed similar performance as human observers. The proposed automatic system allows for a quick and reliable grading of large quantities of OCT scans, which could increase the efficiency of large-scale AMD studies and pave the way for AMD screening using OCT.
Purpose: To evaluate a machine learning algorithm that automatically grades age-related macular degeneration (AMD) severity stages from optical coherence tomography (OCT) scans. Methods: A total of 3265 OCT scans from 1016 patients with either no signs of AMD or with signs of early, intermediate, or advanced AMD were randomly selected from a large European multicenter database. A machine learning system was developed to automatically grade unseen OCT scans into different AMD severity stages without requiring retinal layer segmentation. The ability of the system to identify high-risk AMD stages and to assign the correct severity stage was determined by using receiver operator characteristic (ROC) analysis and Cohen's κ statistics (κ), respectively. The results were compared to those of two human observers. Reproducibility was assessed in an independent, publicly available data set of 384 OCT scans. Results: The system achieved an area under the ROC curve of 0.980 with a sensitivity of 98.2% at a specificity of 91.2%. This compares favorably with the performance of human observers who achieved sensitivities of 97.0% and 99.4% at specificities of 89.7% and 87.2%, respectively. A good level of agreement with the reference was obtained (κ = 0.713) and was in concordance with the human observers (κ = 0.775 and κ = 0.755, respectively). Conclusions: A machine learning system capable of automatically grading OCT scans into AMD severity stages was developed and showed similar performance as human observers. The proposed automatic system allows for a quick and reliable grading of large quantities of OCT scans, which could increase the efficiency of large-scale AMD studies and pave the way for AMD screening using OCT.
Authors: Jan H Terheyden; Frank G Holz; Steffen Schmitz-Valckenberg; Anna Lüning; Matthias Schmid; Gary S Rubin; Hannah Dunbar; Adnan Tufail; David P Crabb; Alison Binns; Clara I Sánchez; Carel Hoyng; Philippe Margaron; Nadia Zakaria; Mary Durbin; Ulrich Luhmann; Parisa Zamiri; José Cunha-Vaz; Cecília Martinho; Sergio Leal; Robert P Finger Journal: Trials Date: 2020-07-18 Impact factor: 2.279
Authors: Harpal Singh Sandhu; Ahmed Eltanboly; Ahmed Shalaby; Robert S Keynton; Schlomit Schaal; Ayman El-Baz Journal: Invest Ophthalmol Vis Sci Date: 2018-06-01 Impact factor: 4.799
Authors: Hyun-Lim Yang; Jong Jin Kim; Jong Ho Kim; Yong Koo Kang; Dong Ho Park; Han Sang Park; Hong Kyun Kim; Min-Soo Kim Journal: PLoS One Date: 2019-04-05 Impact factor: 3.240