Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence
PurposeTo predict visual outcomes and treatment needs in a treat & extend (T&E) regimen in neovascular age-related macular degeneration (nAMD) using a machine learning model based on quantitative optical coherence tomography (OCT) imaging biomarkers.Materials and methodsStudy eyes of...
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Frontiers Media S.A.
2022-08-01
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Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2022.958469/full |
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author | Hrvoje Bogunović Virginia Mares Gregor S. Reiter Ursula Schmidt-Erfurth |
author_facet | Hrvoje Bogunović Virginia Mares Gregor S. Reiter Ursula Schmidt-Erfurth |
author_sort | Hrvoje Bogunović |
collection | DOAJ |
description | PurposeTo predict visual outcomes and treatment needs in a treat & extend (T&E) regimen in neovascular age-related macular degeneration (nAMD) using a machine learning model based on quantitative optical coherence tomography (OCT) imaging biomarkers.Materials and methodsStudy eyes of 270 treatment-naïve subjects, randomized to receiving ranibizumab therapy in the T&E arm of a randomized clinical trial were considered. OCT volume scans were processed at baseline and at the first follow-up visit 4 weeks later. Automated image segmentation was performed, where intraretinal (IRF), subretinal (SRF) fluid, pigment epithelial detachment (PED), hyperreflective foci, and the photoreceptor layer were delineated using a convolutional neural network (CNN). A set of respective quantitative imaging biomarkers were computed across an Early Treatment Diabetic Retinopathy Study (ETDRS) grid to describe the retinal pathomorphology spatially and its change after the first injection. Lastly, using the computed set of OCT features and available clinical and demographic information, predictive models of outcomes and retreatment intervals were built using machine learning and their performance evaluated with a 10-fold cross-validation.ResultsData of 228 evaluable patients were included, as some had missing scans or were lost to follow-up. Of those patients, 55% reached and maintained long (8, 10, 12 weeks) and another 45% stayed at short (4, 6 weeks) treatment intervals. This provides further evidence for a high disease activity in a major proportion of patients. The model predicted the extendable treatment interval group with an AUROC of 0.71, and the visual outcome with an AUROC of up to 0.87 when utilizing both, clinical and imaging features. The volume of SRF and the volume of IRF, remaining at the first follow-up visit, were found to be the most important predictive markers for treatment intervals and visual outcomes, respectively, supporting the important role of quantitative fluid parameters on OCT.ConclusionThe proposed Artificial intelligence (AI) methodology was able to predict visual outcomes and retreatment intervals of a T&E regimen from a single injection. The result of this study is an urgently needed step toward AI-supported management of patients with active and progressive nAMD. |
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language | English |
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publishDate | 2022-08-01 |
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spelling | doaj.art-7e6cd6d50cfd46d5a1f6238e45e3a8462022-12-22T04:00:39ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2022-08-01910.3389/fmed.2022.958469958469Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligenceHrvoje Bogunović0Virginia Mares1Gregor S. Reiter2Ursula Schmidt-Erfurth3Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology, Medical University of Vienna, Vienna, AustriaDepartment of Ophthalmology, Federal University of Minas Gerais, Belo Horizonte, BrazilLaboratory for Ophthalmic Image Analysis, Department of Ophthalmology, Medical University of Vienna, Vienna, AustriaLaboratory for Ophthalmic Image Analysis, Department of Ophthalmology, Medical University of Vienna, Vienna, AustriaPurposeTo predict visual outcomes and treatment needs in a treat & extend (T&E) regimen in neovascular age-related macular degeneration (nAMD) using a machine learning model based on quantitative optical coherence tomography (OCT) imaging biomarkers.Materials and methodsStudy eyes of 270 treatment-naïve subjects, randomized to receiving ranibizumab therapy in the T&E arm of a randomized clinical trial were considered. OCT volume scans were processed at baseline and at the first follow-up visit 4 weeks later. Automated image segmentation was performed, where intraretinal (IRF), subretinal (SRF) fluid, pigment epithelial detachment (PED), hyperreflective foci, and the photoreceptor layer were delineated using a convolutional neural network (CNN). A set of respective quantitative imaging biomarkers were computed across an Early Treatment Diabetic Retinopathy Study (ETDRS) grid to describe the retinal pathomorphology spatially and its change after the first injection. Lastly, using the computed set of OCT features and available clinical and demographic information, predictive models of outcomes and retreatment intervals were built using machine learning and their performance evaluated with a 10-fold cross-validation.ResultsData of 228 evaluable patients were included, as some had missing scans or were lost to follow-up. Of those patients, 55% reached and maintained long (8, 10, 12 weeks) and another 45% stayed at short (4, 6 weeks) treatment intervals. This provides further evidence for a high disease activity in a major proportion of patients. The model predicted the extendable treatment interval group with an AUROC of 0.71, and the visual outcome with an AUROC of up to 0.87 when utilizing both, clinical and imaging features. The volume of SRF and the volume of IRF, remaining at the first follow-up visit, were found to be the most important predictive markers for treatment intervals and visual outcomes, respectively, supporting the important role of quantitative fluid parameters on OCT.ConclusionThe proposed Artificial intelligence (AI) methodology was able to predict visual outcomes and retreatment intervals of a T&E regimen from a single injection. The result of this study is an urgently needed step toward AI-supported management of patients with active and progressive nAMD.https://www.frontiersin.org/articles/10.3389/fmed.2022.958469/fullneovascular age related macular degenerationoptical coherence tomographyanti-VEGF (vascular endothelial growth factor)image analysisretinamachine learning |
spellingShingle | Hrvoje Bogunović Virginia Mares Gregor S. Reiter Ursula Schmidt-Erfurth Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence Frontiers in Medicine neovascular age related macular degeneration optical coherence tomography anti-VEGF (vascular endothelial growth factor) image analysis retina machine learning |
title | Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence |
title_full | Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence |
title_fullStr | Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence |
title_full_unstemmed | Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence |
title_short | Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence |
title_sort | predicting treat and extend outcomes and treatment intervals in neovascular age related macular degeneration from retinal optical coherence tomography using artificial intelligence |
topic | neovascular age related macular degeneration optical coherence tomography anti-VEGF (vascular endothelial growth factor) image analysis retina machine learning |
url | https://www.frontiersin.org/articles/10.3389/fmed.2022.958469/full |
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