Deep Learning Approaches for Detecting of Nascent Geographic Atrophy in Age-Related Macular Degeneration

Heming Yao; Zhichao Wu; Simon S Gao; Robyn H Guymer; Verena Steffen; Hao Chen; Mohsen Hejrati; Miao Zhang

doi:10.1016/j.xops.2023.100428

Deep Learning Approaches for Detecting of Nascent Geographic Atrophy in Age-Related Macular Degeneration

Ophthalmol Sci. 2023 Nov 17;4(3):100428. doi: 10.1016/j.xops.2023.100428. eCollection 2024 May-Jun.

Authors

Heming Yao¹, Zhichao Wu^{2

3}, Simon S Gao¹, Robyn H Guymer^{2

3}, Verena Steffen¹, Hao Chen¹, Mohsen Hejrati¹, Miao Zhang¹

Affiliations

¹ gRED Computational Science, Genentech, Inc., South San Francisco, California.
² Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.
³ Ophthalmology Division, Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia.

Abstract

Purpose: Nascent geographic atrophy (nGA) refers to specific features seen on OCT B-scans, which are strongly associated with the future development of geographic atrophy (GA). This study sought to develop a deep learning model to screen OCT B-scans for nGA that warrant further manual review (an artificial intelligence [AI]-assisted approach), and to determine the extent of reduction in OCT B-scan load requiring manual review while maintaining near-perfect nGA detection performance.

Design: Development and evaluation of a deep learning model.

Participants: One thousand eight hundred and eighty four OCT volume scans (49 B-scans per volume) without neovascular age-related macular degeneration from 280 eyes of 140 participants with bilateral large drusen at baseline, seen at 6-monthly intervals up to a 36-month period (from which 40 eyes developed nGA).

Methods: OCT volume and B-scans were labeled for the presence of nGA. Their presence at the volume scan level provided the ground truth for training a deep learning model to identify OCT B-scans that potentially showed nGA requiring manual review. Using a threshold that provided a sensitivity of 0.99, the B-scans identified were assigned the ground truth label with the AI-assisted approach. The performance of this approach for detecting nGA across all visits, or at the visit of nGA onset, was evaluated using fivefold cross-validation.

Main outcome measures: Sensitivity for detecting nGA, and proportion of OCT B-scans requiring manual review.

Results: The AI-assisted approach (utilizing outputs from the deep learning model to guide manual review) had a sensitivity of 0.97 (95% confidence interval [CI] = 0.93-1.00) and 0.95 (95% CI = 0.87-1.00) for detecting nGA across all visits and at the visit of nGA onset, respectively, when requiring manual review of only 2.7% and 1.9% of selected OCT B-scans, respectively.

Conclusions: A deep learning model could be used to enable near-perfect detection of nGA onset while reducing the number of OCT B-scans requiring manual review by over 50-fold. This AI-assisted approach shows promise for substantially reducing the current burden of manual review of OCT B-scans to detect this crucial feature that portends future development of GA.

Financial disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Keywords: Deep learning; Multi-instance learning; nGA.