Quantification of Key Retinal Features in Early and Late Age-Related Macular Degeneration Using Deep Learning

• Published in: American journal of ophthalmology 2021-06, Vol.226, p.1-12
• Main Authors: Liefers, Bart, Taylor, Paul, Alsaedi, Abdulrahman, Bailey, Clare, Balaskas, Konstantinos, Dhingra, Narendra, Egan, Catherine A., Rodrigues, Filipa Gomes, Gonzalo, Cristina González, Heeren, Tjebo F.C., Lotery, Andrew, Müller, Philipp L., Olvera-Barrios, Abraham, Paul, Bobby, Schwartz, Roy, Thomas, Darren S., Warwick, Alasdair N., Tufail, Adnan, Sánchez, Clara I.
• Format: Article
• Language: English
• Subjects: Algorithms; Annotations; Automation; Deep learning; Diabetic retinopathy; Disease; Human performance; Macular degeneration; Vascular endothelial growth factor
• ISSN: 0002-9394; 1879-1891
• DOI: 10.1016/j.ajo.2020.12.034

We sought to develop and validate a deep learning model for segmentation of 13 features associated with neovascular and atrophic age-related macular degeneration (AMD). Development and validation of a deep-learning model for feature segmentation. Data for model development were obtained from 307 optical coherence tomography volumes. Eight experienced graders manually delineated all abnormalities in 2712 B-scans. A deep neural network was trained with these data to perform voxel-level segmentation of the 13 most common abnormalities (features). For evaluation, 112 B-scans from 112 patients with a diagnosis of neovascular AMD were annotated by 4 independent observers. The main outcome measures were Dice score, intraclass correlation coefficient, and free-response receiver operating characteristic curve. On 11 of 13 features, the model obtained a mean Dice score of 0.63 ± 0.15, compared with 0.61 ± 0.17 for the observers. The mean intraclass correlation coefficient for the model was 0.66 ± 0.22, compared with 0.62 ± 0.21 for the observers. Two features were not evaluated quantitatively because of a lack of data. Free-response receiver operating characteristic analysis demonstrated that the model scored similar or higher sensitivity per false positives compared with the observers. The quality of the automatic segmentation matches that of experienced graders for most features, exceeding human performance for some features. The quantified parameters provided by the model can be used in the current clinical routine and open possibilities for further research into treatment response outside clinical trials.