Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence
Abstract This study investigates the efficacy of predicting age-related macular degeneration (AMD) activity through deep neural networks (DNN) using a cross-instrument training dataset composed of Optical coherence tomography-angiography (OCTA) images from two different manufacturers. A retrospectiv...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-78327-0 |
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| author | Anna Heinke Haochen Zhang Krzysztof Broniarek Katarzyna Michalska-Małecka Wyatt Elsner Carlo Miguel B. Galang Daniel N. Deussen Alexandra Warter Fritz Kalaw Ines Nagel Akshay Agnihotri Nehal N. Mehta Julian Elias Klaas Valerie Schmelter Igor Kozak Sally L. Baxter Dirk-Uwe Bartsch Lingyun Cheng Cheolhong An Truong Nguyen William R. Freeman |
| author_facet | Anna Heinke Haochen Zhang Krzysztof Broniarek Katarzyna Michalska-Małecka Wyatt Elsner Carlo Miguel B. Galang Daniel N. Deussen Alexandra Warter Fritz Kalaw Ines Nagel Akshay Agnihotri Nehal N. Mehta Julian Elias Klaas Valerie Schmelter Igor Kozak Sally L. Baxter Dirk-Uwe Bartsch Lingyun Cheng Cheolhong An Truong Nguyen William R. Freeman |
| author_sort | Anna Heinke |
| collection | DOAJ |
| description | Abstract This study investigates the efficacy of predicting age-related macular degeneration (AMD) activity through deep neural networks (DNN) using a cross-instrument training dataset composed of Optical coherence tomography-angiography (OCTA) images from two different manufacturers. A retrospective cross-sectional study analyzed 2D vascular en-face OCTA images from Heidelberg Spectralis (1478 samples: 1102 training, 276 validation, 100 testing) and Optovue Solix (1003 samples: 754 training, 189 validation, 60 testing). OCTA scans were labeled based on clinical diagnoses and adjacent B-scan OCT fluid information, categorizing activity into normal, dry AMD, active wet AMD, and wet AMD in remission. Experiments explored cross-instrument disease classification using separate and combined datasets for training the DNN. Testing involved 100 Heidelberg and 60 Optovue samples. Training on Heidelberg data alone yielded 73% accuracy on Heidelberg images and 60% on Optovue images. Training on Optovue data alone resulted in 34% accuracy on Heidelberg and 85% on Optovue images. Combined training data from both instruments achieved 78% accuracy on Heidelberg and 76% on Optovue test sets. Results indicate that cross-instrument classifier training demonstrates high classification prediction accuracy, making cross-instrument training viable for future clinical applications. This implies that vascular morphology in OCTA can predict disease progression. |
| format | Article |
| id | doaj-art-f0b1c73441e648c8969d26e7c6921c1c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f0b1c73441e648c8969d26e7c6921c1c2024-11-10T12:22:30ZengNature PortfolioScientific Reports2045-23222024-11-011411910.1038/s41598-024-78327-0Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligenceAnna Heinke0Haochen Zhang1Krzysztof Broniarek2Katarzyna Michalska-Małecka3Wyatt Elsner4Carlo Miguel B. Galang5Daniel N. Deussen6Alexandra Warter7Fritz Kalaw8Ines Nagel9Akshay Agnihotri10Nehal N. Mehta11Julian Elias Klaas12Valerie Schmelter13Igor Kozak14Sally L. Baxter15Dirk-Uwe Bartsch16Lingyun Cheng17Cheolhong An18Truong Nguyen19William R. Freeman20Jacobs Retina CenterDepartment of Electrical and Computer Engineering, University of California San DiegoDepartment of Ophthalmology, Medical University of GdańskDepartment of Ophthalmology, Medical University of GdańskThe Department of Cognitive Science, University of California San DiegoJacobs Retina CenterJacobs Retina CenterJacobs Retina CenterJacobs Retina CenterJacobs Retina CenterJacobs Retina CenterJacobs Retina CenterDepartment of Ophthalmology, LMU University Hospital, LMU MunichDepartment of Ophthalmology, LMU University Hospital, LMU MunichMoorfields Eye HospitalViterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San DiegoJacobs Retina CenterJacobs Retina CenterDepartment of Electrical and Computer Engineering, University of California San DiegoDepartment of Electrical and Computer Engineering, University of California San DiegoJacobs Retina CenterAbstract This study investigates the efficacy of predicting age-related macular degeneration (AMD) activity through deep neural networks (DNN) using a cross-instrument training dataset composed of Optical coherence tomography-angiography (OCTA) images from two different manufacturers. A retrospective cross-sectional study analyzed 2D vascular en-face OCTA images from Heidelberg Spectralis (1478 samples: 1102 training, 276 validation, 100 testing) and Optovue Solix (1003 samples: 754 training, 189 validation, 60 testing). OCTA scans were labeled based on clinical diagnoses and adjacent B-scan OCT fluid information, categorizing activity into normal, dry AMD, active wet AMD, and wet AMD in remission. Experiments explored cross-instrument disease classification using separate and combined datasets for training the DNN. Testing involved 100 Heidelberg and 60 Optovue samples. Training on Heidelberg data alone yielded 73% accuracy on Heidelberg images and 60% on Optovue images. Training on Optovue data alone resulted in 34% accuracy on Heidelberg and 85% on Optovue images. Combined training data from both instruments achieved 78% accuracy on Heidelberg and 76% on Optovue test sets. Results indicate that cross-instrument classifier training demonstrates high classification prediction accuracy, making cross-instrument training viable for future clinical applications. This implies that vascular morphology in OCTA can predict disease progression.https://doi.org/10.1038/s41598-024-78327-0Optical coherence tomography-angiography (OCTA)Cross-instrument trainingArtificial intelligence (AI)Deep neural networks (DNN)Age-related macular degeneration (AMD) |
| spellingShingle | Anna Heinke Haochen Zhang Krzysztof Broniarek Katarzyna Michalska-Małecka Wyatt Elsner Carlo Miguel B. Galang Daniel N. Deussen Alexandra Warter Fritz Kalaw Ines Nagel Akshay Agnihotri Nehal N. Mehta Julian Elias Klaas Valerie Schmelter Igor Kozak Sally L. Baxter Dirk-Uwe Bartsch Lingyun Cheng Cheolhong An Truong Nguyen William R. Freeman Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence Scientific Reports Optical coherence tomography-angiography (OCTA) Cross-instrument training Artificial intelligence (AI) Deep neural networks (DNN) Age-related macular degeneration (AMD) |
| title | Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence |
| title_full | Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence |
| title_fullStr | Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence |
| title_full_unstemmed | Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence |
| title_short | Cross-instrument optical coherence tomography-angiography (OCTA)-based prediction of age-related macular degeneration (AMD) disease activity using artificial intelligence |
| title_sort | cross instrument optical coherence tomography angiography octa based prediction of age related macular degeneration amd disease activity using artificial intelligence |
| topic | Optical coherence tomography-angiography (OCTA) Cross-instrument training Artificial intelligence (AI) Deep neural networks (DNN) Age-related macular degeneration (AMD) |
| url | https://doi.org/10.1038/s41598-024-78327-0 |
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