Related Experiment Video
Updated: Jul 11, 2026
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
Deep Learning-Based Prediction of Individual Geographic Atrophy Progression from a Single Baseline OCT
Julia Mai1, Dmitrii Lachinov1, Gregor S Reiter1
1OPTIMA - Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria.
Objective:
To identify the individual progression of geographic atrophy (GA) lesions from baseline OCT images of patients in routine clinical care.
Design:
Clinical evaluation of a deep learning-based algorithm.
Subjects:
One hundred eighty-four eyes of 100 consecutively enrolled patients.
Methods:
OCT and fundus autofluorescence (FAF) images (both Spectralis, Heidelberg Engineering) of patients with GA secondary to age-related macular degeneration in routine clinical care were used for model validation. Fundus autofluorescence images were annotated manually by delineating the GA area by certified readers of the Vienna Reading Center. The annotated FAF images were anatomically registered in an automated manner to the corresponding OCT scans, resulting in 2-dimensional en face OCT annotations, which were taken as a reference for the model performance. A deep learning-based method for modeling the GA lesion growth over time from a single baseline OCT was evaluated. In addition, the ability of the algorithm to identify fast progressors for the top 10%, 15%, and 20% of GA growth rates was analyzed.
Main Outcome Measures:
Dice similarity coefficient (DSC) and mean absolute error (MAE) between manual and predicted GA growth.
Results:
The deep learning-based tool was able to reliably identify disease activity in GA using a standard OCT image taken at a single baseline time point. The mean DSC for the total GA region increased for the first 2 years of prediction (0.80-0.82). With increasing time intervals beyond 3 years, the DSC decreased slightly to a mean of 0.70. The MAE was low over the first year and with advancing time slowly increased, with mean values ranging from 0.25 mm to 0.69 mm for the total GA region prediction. The model achieved an area under the curve of 0.81, 0.79, and 0.77 for the identification of the top 10%, 15%, and 20% growth rates, respectively.
Conclusions:
The proposed algorithm is capable of fully automated GA lesion growth prediction from a single baseline OCT in a time-continuous fashion in the form of en face maps. The results are a promising step toward clinical decision support tools for therapeutic dosing and guidance of patient management because the first treatment for GA has recently become available.
Financial Disclosures:
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
