Drusen‐aware model for age‐related macular degeneration recognition
Introduction The purpose of this study was to build an automated age‐related macular degeneration (AMD) colour fundus photography (CFP) recognition method that incorporates confounders (other ocular diseases) and normal age‐related changes by using drusen masks for spatial feature supervision. Metho...
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| Published in | Ophthalmic & physiological optics Vol. 43; no. 4; pp. 668 - 679 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Wiley Subscription Services, Inc
01.07.2023
John Wiley and Sons Inc |
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| Online Access | Get full text |
| ISSN | 0275-5408 1475-1313 1475-1313 |
| DOI | 10.1111/opo.13108 |
Cover
| Abstract | Introduction
The purpose of this study was to build an automated age‐related macular degeneration (AMD) colour fundus photography (CFP) recognition method that incorporates confounders (other ocular diseases) and normal age‐related changes by using drusen masks for spatial feature supervision.
Methods
A range of clinical sources were used to acquire 7588 CFPs. Contrast limited adaptive histogram equalisation was used for pre‐processing. ResNet50 was used as the backbone network, and a spatial attention block was added to integrate prior knowledge of drusen features into the backbone. The evaluation metrics used were sensitivity, specificity and F1 score, which is the harmonic mean of precision and recall (sensitivity) and area under the receiver‐operating characteristic (AUC). Fivefold cross‐validation was performed, and the results compared with four other methods.
Results
Excellent discrimination results were obtained with the algorithm. On the public dataset (n = 6565), the proposed method achieved a mean (SD) sensitivity of 0.54 (0.09), specificity of 0.99 (0.00), F1 score of 0.62 (0.06) and AUC of 0.92 (0.02). On the private dataset (n = 1023), the proposed method achieved a sensitivity of 0.92 (0.02), specificity of 0.98 (0.01), F1 score of 0.92 (0.01) and AUC of 0.98 (0.01).
Conclusion
The proposed drusen‐aware model outperformed baseline and other vessel feature‐based methods in F1 and AUC on the AMD/normal CFP classification task and achieved comparable results on datasets that included other diseases that often confound classification. The method also improved results when a five‐category grading protocol was used, thereby reflecting discriminative ability of the algorithm within a real‐life clinical setting. |
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| AbstractList | The purpose of this study was to build an automated age-related macular degeneration (AMD) colour fundus photography (CFP) recognition method that incorporates confounders (other ocular diseases) and normal age-related changes by using drusen masks for spatial feature supervision.INTRODUCTIONThe purpose of this study was to build an automated age-related macular degeneration (AMD) colour fundus photography (CFP) recognition method that incorporates confounders (other ocular diseases) and normal age-related changes by using drusen masks for spatial feature supervision.A range of clinical sources were used to acquire 7588 CFPs. Contrast limited adaptive histogram equalisation was used for pre-processing. ResNet50 was used as the backbone network, and a spatial attention block was added to integrate prior knowledge of drusen features into the backbone. The evaluation metrics used were sensitivity, specificity and F1 score, which is the harmonic mean of precision and recall (sensitivity) and area under the receiver-operating characteristic (AUC). Fivefold cross-validation was performed, and the results compared with four other methods.METHODSA range of clinical sources were used to acquire 7588 CFPs. Contrast limited adaptive histogram equalisation was used for pre-processing. ResNet50 was used as the backbone network, and a spatial attention block was added to integrate prior knowledge of drusen features into the backbone. The evaluation metrics used were sensitivity, specificity and F1 score, which is the harmonic mean of precision and recall (sensitivity) and area under the receiver-operating characteristic (AUC). Fivefold cross-validation was performed, and the results compared with four other methods.Excellent discrimination results were obtained with the algorithm. On the public dataset (n = 6565), the proposed method achieved a mean (SD) sensitivity of 0.54 (0.09), specificity of 0.99 (0.00), F1 score of 0.62 (0.06) and AUC of 0.92 (0.02). On the private dataset (n = 1023), the proposed method achieved a sensitivity of 0.92 (0.02), specificity of 0.98 (0.01), F1 score of 0.92 (0.01) and AUC of 0.98 (0.01).RESULTSExcellent discrimination results were obtained with the algorithm. On the public dataset (n = 6565), the proposed method achieved a mean (SD) sensitivity of 0.54 (0.09), specificity of 0.99 (0.00), F1 score of 0.62 (0.06) and AUC of 0.92 (0.02). On the private dataset (n = 1023), the proposed method achieved a sensitivity of 0.92 (0.02), specificity of 0.98 (0.01), F1 score of 0.92 (0.01) and AUC of 0.98 (0.01).The proposed drusen-aware model outperformed baseline and other vessel feature-based methods in F1 and AUC on the AMD/normal CFP classification task and achieved comparable results on datasets that included other diseases that often confound classification. The method also improved results when a five-category grading protocol was used, thereby reflecting discriminative ability of the algorithm within a real-life clinical setting.CONCLUSIONThe proposed drusen-aware model outperformed baseline and other vessel feature-based methods in F1 and AUC on the AMD/normal CFP classification task and achieved comparable results on datasets that included other diseases that often confound classification. The method also improved results when a five-category grading protocol was used, thereby reflecting discriminative ability of the algorithm within a real-life clinical setting. The purpose of this study was to build an automated age-related macular degeneration (AMD) colour fundus photography (CFP) recognition method that incorporates confounders (other ocular diseases) and normal age-related changes by using drusen masks for spatial feature supervision. A range of clinical sources were used to acquire 7588 CFPs. Contrast limited adaptive histogram equalisation was used for pre-processing. ResNet50 was used as the backbone network, and a spatial attention block was added to integrate prior knowledge of drusen features into the backbone. The evaluation metrics used were sensitivity, specificity and F1 score, which is the harmonic mean of precision and recall (sensitivity) and area under the receiver-operating characteristic (AUC). Fivefold cross-validation was performed, and the results compared with four other methods. Excellent discrimination results were obtained with the algorithm. On the public dataset (n = 6565), the proposed method achieved a mean (SD) sensitivity of 0.54 (0.09), specificity of 0.99 (0.00), F1 score of 0.62 (0.06) and AUC of 0.92 (0.02). On the private dataset (n = 1023), the proposed method achieved a sensitivity of 0.92 (0.02), specificity of 0.98 (0.01), F1 score of 0.92 (0.01) and AUC of 0.98 (0.01). The proposed drusen-aware model outperformed baseline and other vessel feature-based methods in F1 and AUC on the AMD/normal CFP classification task and achieved comparable results on datasets that included other diseases that often confound classification. The method also improved results when a five-category grading protocol was used, thereby reflecting discriminative ability of the algorithm within a real-life clinical setting. Introduction The purpose of this study was to build an automated age‐related macular degeneration (AMD) colour fundus photography (CFP) recognition method that incorporates confounders (other ocular diseases) and normal age‐related changes by using drusen masks for spatial feature supervision. Methods A range of clinical sources were used to acquire 7588 CFPs. Contrast limited adaptive histogram equalisation was used for pre‐processing. ResNet50 was used as the backbone network, and a spatial attention block was added to integrate prior knowledge of drusen features into the backbone. The evaluation metrics used were sensitivity, specificity and F1 score, which is the harmonic mean of precision and recall (sensitivity) and area under the receiver‐operating characteristic (AUC). Fivefold cross‐validation was performed, and the results compared with four other methods. Results Excellent discrimination results were obtained with the algorithm. On the public dataset (n = 6565), the proposed method achieved a mean (SD) sensitivity of 0.54 (0.09), specificity of 0.99 (0.00), F1 score of 0.62 (0.06) and AUC of 0.92 (0.02). On the private dataset (n = 1023), the proposed method achieved a sensitivity of 0.92 (0.02), specificity of 0.98 (0.01), F1 score of 0.92 (0.01) and AUC of 0.98 (0.01). Conclusion The proposed drusen‐aware model outperformed baseline and other vessel feature‐based methods in F1 and AUC on the AMD/normal CFP classification task and achieved comparable results on datasets that included other diseases that often confound classification. The method also improved results when a five‐category grading protocol was used, thereby reflecting discriminative ability of the algorithm within a real‐life clinical setting. IntroductionThe purpose of this study was to build an automated age‐related macular degeneration (AMD) colour fundus photography (CFP) recognition method that incorporates confounders (other ocular diseases) and normal age‐related changes by using drusen masks for spatial feature supervision.MethodsA range of clinical sources were used to acquire 7588 CFPs. Contrast limited adaptive histogram equalisation was used for pre‐processing. ResNet50 was used as the backbone network, and a spatial attention block was added to integrate prior knowledge of drusen features into the backbone. The evaluation metrics used were sensitivity, specificity and F1 score, which is the harmonic mean of precision and recall (sensitivity) and area under the receiver‐operating characteristic (AUC). Fivefold cross‐validation was performed, and the results compared with four other methods.ResultsExcellent discrimination results were obtained with the algorithm. On the public dataset (n = 6565), the proposed method achieved a mean (SD) sensitivity of 0.54 (0.09), specificity of 0.99 (0.00), F1 score of 0.62 (0.06) and AUC of 0.92 (0.02). On the private dataset (n = 1023), the proposed method achieved a sensitivity of 0.92 (0.02), specificity of 0.98 (0.01), F1 score of 0.92 (0.01) and AUC of 0.98 (0.01).ConclusionThe proposed drusen‐aware model outperformed baseline and other vessel feature‐based methods in F1 and AUC on the AMD/normal CFP classification task and achieved comparable results on datasets that included other diseases that often confound classification. The method also improved results when a five‐category grading protocol was used, thereby reflecting discriminative ability of the algorithm within a real‐life clinical setting. |
| Author | Pan, Junjun Ho, Sharon Ly, Angelica Sowmya, Arcot Kalloniatis, Michael |
| AuthorAffiliation | 3 School of Optometry and Vision Science University of New South Wales Kensington New South Wales Australia 4 School of Medicine (Optometry) Deakin University Waurn Ponds Victoria Australia 1 School of Computer Science and Engineering University of New South Wales Kensington New South Wales Australia 2 Centre for Eye Health University of New South Wales Kensington New South Wales Australia |
| AuthorAffiliation_xml | – name: 4 School of Medicine (Optometry) Deakin University Waurn Ponds Victoria Australia – name: 3 School of Optometry and Vision Science University of New South Wales Kensington New South Wales Australia – name: 1 School of Computer Science and Engineering University of New South Wales Kensington New South Wales Australia – name: 2 Centre for Eye Health University of New South Wales Kensington New South Wales Australia |
| Author_xml | – sequence: 1 givenname: Junjun surname: Pan fullname: Pan, Junjun organization: University of New South Wales – sequence: 2 givenname: Sharon orcidid: 0000-0002-1975-828X surname: Ho fullname: Ho, Sharon organization: University of New South Wales – sequence: 3 givenname: Angelica orcidid: 0000-0001-7881-1522 surname: Ly fullname: Ly, Angelica organization: University of New South Wales – sequence: 4 givenname: Michael orcidid: 0000-0002-5264-4639 surname: Kalloniatis fullname: Kalloniatis, Michael organization: Deakin University – sequence: 5 givenname: Arcot orcidid: 0000-0001-9236-5063 surname: Sowmya fullname: Sowmya, Arcot email: a.sowmya@unsw.edu.au organization: University of New South Wales |
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| Keywords | deep learning image classification drusen machine learning age-related macular degeneration artificial intelligence automated recognition |
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The purpose of this study was to build an automated age‐related macular degeneration (AMD) colour fundus photography (CFP) recognition method that... The purpose of this study was to build an automated age-related macular degeneration (AMD) colour fundus photography (CFP) recognition method that incorporates... IntroductionThe purpose of this study was to build an automated age‐related macular degeneration (AMD) colour fundus photography (CFP) recognition method that... |
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| SubjectTerms | Age age‐related macular degeneration Algorithms artificial intelligence automated recognition Classification deep learning drusen Eye diseases Humans image classification machine learning Macular degeneration Macular Degeneration - diagnosis Original Photography Retina Retinal Drusen - diagnosis ROC Curve |
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| Title | Drusen‐aware model for age‐related macular degeneration recognition |
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