Session-by-Session Prediction of Anti-Endothelial Growth Factor Injection Needs in Neovascular Age-Related Macular Degeneration Using Optical-Coherence-Tomography-Derived Features and Machine Learning
Background/Objectives: Neovascular age-related macular degeneration (nAMD) is a retinal disorder leading to irreversible central vision loss. The pro-re-nata (PRN) treatment for nAMD involves frequent intravitreal injections of anti-VEGF medications, placing a burden on patients and healthcare syste...
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MDPI AG
2024-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/23/2609 |
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| author | Flavio Ragni Stefano Bovo Andrea Zen Diego Sona Katia De Nadai Ginevra Giovanna Adamo Marco Pellegrini Francesco Nasini Chiara Vivarelli Marco Tavolato Marco Mura Francesco Parmeggiani Giuseppe Jurman |
| author_facet | Flavio Ragni Stefano Bovo Andrea Zen Diego Sona Katia De Nadai Ginevra Giovanna Adamo Marco Pellegrini Francesco Nasini Chiara Vivarelli Marco Tavolato Marco Mura Francesco Parmeggiani Giuseppe Jurman |
| author_sort | Flavio Ragni |
| collection | DOAJ |
| description | Background/Objectives: Neovascular age-related macular degeneration (nAMD) is a retinal disorder leading to irreversible central vision loss. The pro-re-nata (PRN) treatment for nAMD involves frequent intravitreal injections of anti-VEGF medications, placing a burden on patients and healthcare systems. Predicting injections needs at each monitoring session could optimize treatment outcomes and reduce unnecessary interventions. Methods: To achieve these aims, machine learning (ML) models were evaluated using different combinations of clinical variables, including retinal thickness and volume, best-corrected visual acuity, and features derived from macular optical coherence tomography (OCT). A “Leave Some Subjects Out” (LSSO) nested cross-validation approach ensured robust evaluation. Moreover, the SHapley Additive exPlanations (SHAP) analysis was employed to quantify the contribution of each feature to model predictions. Results: Results demonstrated that models incorporating both structural and functional features achieved high classification accuracy in predicting injection necessity (AUC = 0.747 ± 0.046, MCC = 0.541 ± 0.073). Moreover, the explainability analysis identified as key predictors both subretinal and intraretinal fluid, alongside central retinal thickness. Conclusions: These findings suggest that session-by-session prediction of injection needs in nAMD patients is feasible, even without processing the entire OCT image. The proposed ML framework has the potential to be integrated into routine clinical workflows, thereby optimizing nAMD therapeutic management. |
| format | Article |
| id | doaj-art-f3ee2f0a607848e69dcbca5fd214a2e1 |
| institution | Kabale University |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-f3ee2f0a607848e69dcbca5fd214a2e12024-12-13T16:24:23ZengMDPI AGDiagnostics2075-44182024-11-011423260910.3390/diagnostics14232609Session-by-Session Prediction of Anti-Endothelial Growth Factor Injection Needs in Neovascular Age-Related Macular Degeneration Using Optical-Coherence-Tomography-Derived Features and Machine LearningFlavio Ragni0Stefano Bovo1Andrea Zen2Diego Sona3Katia De Nadai4Ginevra Giovanna Adamo5Marco Pellegrini6Francesco Nasini7Chiara Vivarelli8Marco Tavolato9Marco Mura10Francesco Parmeggiani11Giuseppe Jurman12Data Science for Health Unit, Fondazione Bruno Kessler, 38123 Trento, ItalyData Science for Health Unit, Fondazione Bruno Kessler, 38123 Trento, ItalyData Science for Health Unit, Fondazione Bruno Kessler, 38123 Trento, ItalyData Science for Health Unit, Fondazione Bruno Kessler, 38123 Trento, ItalyDepartment of Translational Medicine and for Romagna, University of Ferrara, 44121 Ferrara, ItalyDepartment of Translational Medicine and for Romagna, University of Ferrara, 44121 Ferrara, ItalyDepartment of Translational Medicine and for Romagna, University of Ferrara, 44121 Ferrara, ItalyUnit of Ophthalmology, Azienda Ospedaliero Universitaria di Ferrara, 44100 Ferrara, ItalyDepartment of Translational Medicine and for Romagna, University of Ferrara, 44121 Ferrara, ItalyERN-EYE Network—Center Retinitis Pigmentosa of Veneto Region, Camposampiero Hospital, 35012 Padua, ItalyDepartment of Translational Medicine and for Romagna, University of Ferrara, 44121 Ferrara, ItalyDepartment of Translational Medicine and for Romagna, University of Ferrara, 44121 Ferrara, ItalyData Science for Health Unit, Fondazione Bruno Kessler, 38123 Trento, ItalyBackground/Objectives: Neovascular age-related macular degeneration (nAMD) is a retinal disorder leading to irreversible central vision loss. The pro-re-nata (PRN) treatment for nAMD involves frequent intravitreal injections of anti-VEGF medications, placing a burden on patients and healthcare systems. Predicting injections needs at each monitoring session could optimize treatment outcomes and reduce unnecessary interventions. Methods: To achieve these aims, machine learning (ML) models were evaluated using different combinations of clinical variables, including retinal thickness and volume, best-corrected visual acuity, and features derived from macular optical coherence tomography (OCT). A “Leave Some Subjects Out” (LSSO) nested cross-validation approach ensured robust evaluation. Moreover, the SHapley Additive exPlanations (SHAP) analysis was employed to quantify the contribution of each feature to model predictions. Results: Results demonstrated that models incorporating both structural and functional features achieved high classification accuracy in predicting injection necessity (AUC = 0.747 ± 0.046, MCC = 0.541 ± 0.073). Moreover, the explainability analysis identified as key predictors both subretinal and intraretinal fluid, alongside central retinal thickness. Conclusions: These findings suggest that session-by-session prediction of injection needs in nAMD patients is feasible, even without processing the entire OCT image. The proposed ML framework has the potential to be integrated into routine clinical workflows, thereby optimizing nAMD therapeutic management.https://www.mdpi.com/2075-4418/14/23/2609neovascular age-related macular degenerationoptical coherence tomographyanti-VEGF drugsartificial intelligencemachine learninginjection prediction |
| spellingShingle | Flavio Ragni Stefano Bovo Andrea Zen Diego Sona Katia De Nadai Ginevra Giovanna Adamo Marco Pellegrini Francesco Nasini Chiara Vivarelli Marco Tavolato Marco Mura Francesco Parmeggiani Giuseppe Jurman Session-by-Session Prediction of Anti-Endothelial Growth Factor Injection Needs in Neovascular Age-Related Macular Degeneration Using Optical-Coherence-Tomography-Derived Features and Machine Learning Diagnostics neovascular age-related macular degeneration optical coherence tomography anti-VEGF drugs artificial intelligence machine learning injection prediction |
| title | Session-by-Session Prediction of Anti-Endothelial Growth Factor Injection Needs in Neovascular Age-Related Macular Degeneration Using Optical-Coherence-Tomography-Derived Features and Machine Learning |
| title_full | Session-by-Session Prediction of Anti-Endothelial Growth Factor Injection Needs in Neovascular Age-Related Macular Degeneration Using Optical-Coherence-Tomography-Derived Features and Machine Learning |
| title_fullStr | Session-by-Session Prediction of Anti-Endothelial Growth Factor Injection Needs in Neovascular Age-Related Macular Degeneration Using Optical-Coherence-Tomography-Derived Features and Machine Learning |
| title_full_unstemmed | Session-by-Session Prediction of Anti-Endothelial Growth Factor Injection Needs in Neovascular Age-Related Macular Degeneration Using Optical-Coherence-Tomography-Derived Features and Machine Learning |
| title_short | Session-by-Session Prediction of Anti-Endothelial Growth Factor Injection Needs in Neovascular Age-Related Macular Degeneration Using Optical-Coherence-Tomography-Derived Features and Machine Learning |
| title_sort | session by session prediction of anti endothelial growth factor injection needs in neovascular age related macular degeneration using optical coherence tomography derived features and machine learning |
| topic | neovascular age-related macular degeneration optical coherence tomography anti-VEGF drugs artificial intelligence machine learning injection prediction |
| url | https://www.mdpi.com/2075-4418/14/23/2609 |
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