Deep Learning to Predict the Future Growth of Geographic Atrophy from Fundus Autofluorescence
Purpose: The region of growth (ROG) of geographic atrophy (GA) throughout the macular area has an impact on visual outcomes. Here, we developed multiple deep learning models to predict the 1-year ROG of GA lesions using fundus autofluorescence (FAF) images. Design: In this retrospective analysis, 3...
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Elsevier
2025-03-01
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| Series: | Ophthalmology Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666914524001714 |
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| author | Anish Salvi, MS Julia Cluceru, PhD Simon S. Gao, PhD Christina Rabe, PhD Courtney Schiffman, PhD Qi Yang, PhD Aaron Y. Lee, MD, MSCI Pearse A. Keane, MD, FRCOphth Srinivas R. Sadda, MD Frank G. Holz, MD Daniela Ferrara, MD, PhD Neha Anegondi, MTech |
| author_facet | Anish Salvi, MS Julia Cluceru, PhD Simon S. Gao, PhD Christina Rabe, PhD Courtney Schiffman, PhD Qi Yang, PhD Aaron Y. Lee, MD, MSCI Pearse A. Keane, MD, FRCOphth Srinivas R. Sadda, MD Frank G. Holz, MD Daniela Ferrara, MD, PhD Neha Anegondi, MTech |
| author_sort | Anish Salvi, MS |
| collection | DOAJ |
| description | Purpose: The region of growth (ROG) of geographic atrophy (GA) throughout the macular area has an impact on visual outcomes. Here, we developed multiple deep learning models to predict the 1-year ROG of GA lesions using fundus autofluorescence (FAF) images. Design: In this retrospective analysis, 3 types of models were developed using FAF images collected 6 months after baseline to predict the GA lesion area (segmented lesion mask) at 1.5 years, FAF images collected at baseline and 6 months to predict the GA lesion at 1.5 years, and FAF images collected 6 months after baseline to predict the GA lesion at 1 and 1.5 years. The 1-year ROG from the 6-month visit was derived by taking the difference between the GA lesion area (segmented lesion mask) at the 1.5-year and 6-month visits. Participants: Patients enrolled in the following lampalizumab clinical trials and prospective observational studies: NCT02247479, NCT02247531, NCT02479386, and NCT02399072. Methods: Datasets of study eyes from 597 patients were split into model training (310), validation (78), and test sets (209), stratified by baseline or initial lesion area, lesion growth rate, foveal involvement, and focality. Deep learning experiments were performed using the 2-dimensional U-Net; whole-lesion and multiclass models were developed. Main Outcome Measures: The performance of the models was evaluated by calculating the Dice score, coefficient of determination (R2), and the squared Pearson correlation coefficient (r2) between the true and derived GA lesion 1-year ROG. Results: The model using baseline and 6-month FAF images to predict GA lesion enlargement at 1.5 years had the best performance for the derived 1-year ROG. Mean Dice scores were 0.73, 0.68, and 0.70 in the training, validation, and test sets, respectively. The R2 (0.77, 0.53, and 0.79) and r2 (0.83, 0.61, and 0.79) showed similar trends across the 3 sets. Conclusions: These findings show the potential of using baseline and/or 6-month visit FAF images to predict 1-year GA ROG using a deep learning approach. This work could potentially help support decision-making in clinical trials and more informed treatment decisions in clinical practice. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. |
| format | Article |
| id | doaj-art-073278975fa14f85a96add2f9ca2a345 |
| institution | OA Journals |
| issn | 2666-9145 |
| language | English |
| publishDate | 2025-03-01 |
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| series | Ophthalmology Science |
| spelling | doaj-art-073278975fa14f85a96add2f9ca2a3452025-08-20T02:38:21ZengElsevierOphthalmology Science2666-91452025-03-015210063510.1016/j.xops.2024.100635Deep Learning to Predict the Future Growth of Geographic Atrophy from Fundus AutofluorescenceAnish Salvi, MS0Julia Cluceru, PhD1Simon S. Gao, PhD2Christina Rabe, PhD3Courtney Schiffman, PhD4Qi Yang, PhD5Aaron Y. Lee, MD, MSCI6Pearse A. Keane, MD, FRCOphth7Srinivas R. Sadda, MD8Frank G. Holz, MD9Daniela Ferrara, MD, PhD10Neha Anegondi, MTech11Genentech, Inc., South San Francisco, CaliforniaGenentech, Inc., South San Francisco, CaliforniaGenentech, Inc., South San Francisco, CaliforniaGenentech, Inc., South San Francisco, CaliforniaGenentech, Inc., South San Francisco, CaliforniaGenentech, Inc., South San Francisco, CaliforniaDepartment of Ophthalmology, University of Washington, Seattle, WashingtonNational Institute for Health Research, Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, London, UKDoheny Image Reading Center, Doheny Eye Institute, Los Angeles, CaliforniaDepartment of Ophthalmology, University of Bonn, Bonn, GermanyGenentech, Inc., South San Francisco, CaliforniaGenentech, Inc., South San Francisco, California; Correspondence: Neha Anegondi, MTech, Genentech, Inc., 1 DNA Way, MS-44 #3B, South San Francisco, CA 94080.Purpose: The region of growth (ROG) of geographic atrophy (GA) throughout the macular area has an impact on visual outcomes. Here, we developed multiple deep learning models to predict the 1-year ROG of GA lesions using fundus autofluorescence (FAF) images. Design: In this retrospective analysis, 3 types of models were developed using FAF images collected 6 months after baseline to predict the GA lesion area (segmented lesion mask) at 1.5 years, FAF images collected at baseline and 6 months to predict the GA lesion at 1.5 years, and FAF images collected 6 months after baseline to predict the GA lesion at 1 and 1.5 years. The 1-year ROG from the 6-month visit was derived by taking the difference between the GA lesion area (segmented lesion mask) at the 1.5-year and 6-month visits. Participants: Patients enrolled in the following lampalizumab clinical trials and prospective observational studies: NCT02247479, NCT02247531, NCT02479386, and NCT02399072. Methods: Datasets of study eyes from 597 patients were split into model training (310), validation (78), and test sets (209), stratified by baseline or initial lesion area, lesion growth rate, foveal involvement, and focality. Deep learning experiments were performed using the 2-dimensional U-Net; whole-lesion and multiclass models were developed. Main Outcome Measures: The performance of the models was evaluated by calculating the Dice score, coefficient of determination (R2), and the squared Pearson correlation coefficient (r2) between the true and derived GA lesion 1-year ROG. Results: The model using baseline and 6-month FAF images to predict GA lesion enlargement at 1.5 years had the best performance for the derived 1-year ROG. Mean Dice scores were 0.73, 0.68, and 0.70 in the training, validation, and test sets, respectively. The R2 (0.77, 0.53, and 0.79) and r2 (0.83, 0.61, and 0.79) showed similar trends across the 3 sets. Conclusions: These findings show the potential of using baseline and/or 6-month visit FAF images to predict 1-year GA ROG using a deep learning approach. This work could potentially help support decision-making in clinical trials and more informed treatment decisions in clinical practice. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.http://www.sciencedirect.com/science/article/pii/S2666914524001714Age-related macular degenerationArtificial intelligenceDeep learningFundus autofluorescence imagingGeographic atrophy |
| spellingShingle | Anish Salvi, MS Julia Cluceru, PhD Simon S. Gao, PhD Christina Rabe, PhD Courtney Schiffman, PhD Qi Yang, PhD Aaron Y. Lee, MD, MSCI Pearse A. Keane, MD, FRCOphth Srinivas R. Sadda, MD Frank G. Holz, MD Daniela Ferrara, MD, PhD Neha Anegondi, MTech Deep Learning to Predict the Future Growth of Geographic Atrophy from Fundus Autofluorescence Ophthalmology Science Age-related macular degeneration Artificial intelligence Deep learning Fundus autofluorescence imaging Geographic atrophy |
| title | Deep Learning to Predict the Future Growth of Geographic Atrophy from Fundus Autofluorescence |
| title_full | Deep Learning to Predict the Future Growth of Geographic Atrophy from Fundus Autofluorescence |
| title_fullStr | Deep Learning to Predict the Future Growth of Geographic Atrophy from Fundus Autofluorescence |
| title_full_unstemmed | Deep Learning to Predict the Future Growth of Geographic Atrophy from Fundus Autofluorescence |
| title_short | Deep Learning to Predict the Future Growth of Geographic Atrophy from Fundus Autofluorescence |
| title_sort | deep learning to predict the future growth of geographic atrophy from fundus autofluorescence |
| topic | Age-related macular degeneration Artificial intelligence Deep learning Fundus autofluorescence imaging Geographic atrophy |
| url | http://www.sciencedirect.com/science/article/pii/S2666914524001714 |
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