Dr. Qi Yang Profile

Dr. Qi Yang

at Genentech Inc

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 5 March 2021 Presentation + Paper

Predicting geographic atrophy growth rate from fundus autofluorescence images using deep neural networks

Neha Anegondi, Qi Yang, Michael Kawczynski, Verena Steffen, Simon Gao

Proceedings Volume 11634, 116340E (2021) https://doi.org/10.1117/12.2575898

KEYWORDS: Neural networks, Performance modeling, Visualization, Pathophysiology, Optical coherence tomography, Near infrared, Clinical trials

Read Abstract +

Geographic atrophy (GA) is late-stage dry age-related macular degeneration (AMD). Improved predictors of GA progression would be useful in clinical trial design and may be relevant for clinical practice. The purpose of this study was to accurately predict GA progression over time from baseline fundus autofluorescence (FAF) images (Heidelberg Engineering, Inc., Germany) using deep learning. Study eyes of patients (n = 1312) enrolled in the Lampalizumab trials^{1, 2} (NCT02479386, NCT02247479, NCT02247531) were included. The dataset was split by patient into training (n = 1047) and holdout sets (n = 265). GA progression, defined as GA lesion growth rate, was derived by a linear fit on all available measurements of GA lesion area (mm2 , measured from manually graded FAF images). The model performance was evaluated using 5-fold cross-validation (CV). Coefficient of determination (R2 ) computed as the square of Pearson correlation coefficient was used as the performance metric. Multiple modeling approaches were implemented, and the best performance was observed using cascade learning. In this approach, pre-trained weights on ImageNet were finetuned to predict GA lesion area followed by further fine-tuning to predict GA growth rate. The 5-folds had an average CV R² of 0.44, and the holdout showed R² of 0.50 (95% confidence interval: 0.41 - 0.61). In comparison, a linear model using only baseline GA lesion area in the same holdout showed an R² of 0.18. Further investigation with visualization techniques might help understand the pathophysiology behind the predictions. The predictions may be improved by combining with imaging modalities like near-infrared and/or optical coherence tomography.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years