Quantification of key retinal features in early and late age-related macular degeneration using deep learning

B. Liefers, P. Taylor, A. Alsaedi, C. Bailey, K. Balaskas, N. Dhingra, C. Egan, F. Rodrigues, C. González-Gonzalo, T. Heeren, A. Lotery, P. Muller, A. Olvera-Barrios, B. Paul, R. Schwartz, D. Thomas, A. Warwick, A. Tufail and C. Sánchez

American Journal of Ophthalmology 2021.

DOI

Purpose:

To develop and validate a deep learning model for segmentation of 13 features associated with neovascular and atrophic age-related macular degeneration (AMD).

Design:

Development and validation of a deep-learning model for feature segmentation.

Methods:

Data for model development were obtained from 307 optical coherence tomography volumes. Eight experienced graders manually delineated all abnormalities in 2,712 B-scans. A deep neural network was trained with this 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 four independent observers. Main outcome measures were Dice score, intra-class correlation coefficient (ICC), and free-response receiver operating characteristic (FROC) curve.

Results:

On 11 of the 13 features, the model obtained a mean Dice score of 0.63 ± 0.15, compared to 0.61 ± 0.17 for the observers. The mean ICC for the model was 0.66 ± 0.22, compared to 0.62 ± 0.21 for the observers. Two features were not evaluated quantitatively due to lack of data. FROC analysis demonstrated that the model scored similar or higher sensitivity per false positives compared to the observers.

Conclusions:

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.