Suitability of machine learning for atrophy and fibrosis development in neovascular age-related macular degeneration
© 2023 The Authors. Acta Ophthalmologica published by John Wiley & Sons Ltd on behalf of Acta Ophthalmologica Scandinavica Foundation..
PURPOSE: To assess the suitability of machine learning (ML) techniques in predicting the development of fibrosis and atrophy in patients with neovascular age-related macular degeneration (nAMD), receiving anti-VEGF treatment over a 36-month period.
METHODS: An extensive analysis was conducted on the use of ML to predict fibrosis and atrophy development on nAMD patients at 36 months from start of anti-VEGF treatment, using only data from the first 12 months. We use data collected according to real-world practice, which includes clinical and genetic factors.
RESULTS: The ML analysis consistently identified ETDRS as a relevant factor for predicting the development of atrophy and fibrosis, confirming previous statistical analyses. Also, it was shown that genetic variables did not demonstrate statistical relevance in the prediction. Despite the complexity of predicting macular degeneration, our model was able to obtain a balance accuracy of 63% and an AUC of 0.72 when predicting the development of atrophy or fibrosis at 36 months.
CONCLUSION: This study demonstrates the potential of ML techniques in predicting the development of fibrosis and atrophy in nAMD patients receiving long-term anti-VEGF treatment. The findings highlight the importance of clinical factors, particularly ETDRS (early treatment diabetic retinopathy study) visual acuity test, in predicting these outcomes. The lessons learned from this research can guide future ML-based prediction tasks in the field of ophthalmology and contribute to the design of data collection processes.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
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| Enthalten in: |
Acta ophthalmologica - (2023) vom: 22. Dez. |
| Sprache: |
Englisch |
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| Links: |
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| Themen: |
Atrophy |
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| Anmerkungen: |
Date Revised 22.12.2023 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1111/aos.16616 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
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| 100 | 1 | |a de la Fuente, Jesus |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Suitability of machine learning for atrophy and fibrosis development in neovascular age-related macular degeneration |
| 264 | 1 | |c 2023 | |
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| 500 | |a Date Revised 22.12.2023 | ||
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| 500 | |a Citation Status Publisher | ||
| 520 | |a © 2023 The Authors. Acta Ophthalmologica published by John Wiley & Sons Ltd on behalf of Acta Ophthalmologica Scandinavica Foundation. | ||
| 520 | |a PURPOSE: To assess the suitability of machine learning (ML) techniques in predicting the development of fibrosis and atrophy in patients with neovascular age-related macular degeneration (nAMD), receiving anti-VEGF treatment over a 36-month period | ||
| 520 | |a METHODS: An extensive analysis was conducted on the use of ML to predict fibrosis and atrophy development on nAMD patients at 36 months from start of anti-VEGF treatment, using only data from the first 12 months. We use data collected according to real-world practice, which includes clinical and genetic factors | ||
| 520 | |a RESULTS: The ML analysis consistently identified ETDRS as a relevant factor for predicting the development of atrophy and fibrosis, confirming previous statistical analyses. Also, it was shown that genetic variables did not demonstrate statistical relevance in the prediction. Despite the complexity of predicting macular degeneration, our model was able to obtain a balance accuracy of 63% and an AUC of 0.72 when predicting the development of atrophy or fibrosis at 36 months | ||
| 520 | |a CONCLUSION: This study demonstrates the potential of ML techniques in predicting the development of fibrosis and atrophy in nAMD patients receiving long-term anti-VEGF treatment. The findings highlight the importance of clinical factors, particularly ETDRS (early treatment diabetic retinopathy study) visual acuity test, in predicting these outcomes. The lessons learned from this research can guide future ML-based prediction tasks in the field of ophthalmology and contribute to the design of data collection processes | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a atrophy | |
| 650 | 4 | |a extreme gradient boosting | |
| 650 | 4 | |a fibrosis | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a nAMD | |
| 650 | 4 | |a random Forest | |
| 650 | 4 | |a support vector machine | |
| 700 | 1 | |a Llorente-González, Sara |e verfasserin |4 aut | |
| 700 | 1 | |a Fernandez-Robredo, Patricia |e verfasserin |4 aut | |
| 700 | 1 | |a Hernandez, María |e verfasserin |4 aut | |
| 700 | 1 | |a García-Layana, Alfredo |e verfasserin |4 aut | |
| 700 | 1 | |a Ochoa, Idoia |e verfasserin |4 aut | |
| 700 | 1 | |a Recalde, Sergio |e verfasserin |4 aut | |
| 700 | 0 | |a Spanish AMD group |e verfasserin |4 aut | |
| 700 | 1 | |a Alonso-Iglesias, Elena |e investigator |4 oth | |
| 700 | 1 | |a Salinas-Alamán, Ángel |e investigator |4 oth | |
| 700 | 1 | |a Bezunartea-Bezunartea, Jaione |e investigator |4 oth | |
| 700 | 1 | |a Abraldes López-Veiga, Maximino J |e investigator |4 oth | |
| 700 | 1 | |a Díez-Sotelo, Marta |e investigator |4 oth | |
| 700 | 1 | |a Sarandeses-Díez, María Teresa |e investigator |4 oth | |
| 700 | 1 | |a Taboada-Gago, María Dolores |e investigator |4 oth | |
| 700 | 1 | |a Asorey, María Knight |e investigator |4 oth | |
| 700 | 1 | |a Valverde-Megías, Alicia |e investigator |4 oth | |
| 700 | 1 | |a Felipe-Márquez, Gema |e investigator |4 oth | |
| 700 | 1 | |a Honrubia-Grijalbo, Ana |e investigator |4 oth | |
| 700 | 1 | |a Núñez-Benito, María Esther |e investigator |4 oth | |
| 700 | 1 | |a Escobar-Barranco, José J |e investigator |4 oth | |
| 700 | 1 | |a Pina-Marín, Begoña |e investigator |4 oth | |
| 700 | 1 | |a Fernández-Bonet, Manel |e investigator |4 oth | |
| 700 | 1 | |a Jadraque-Ruíz, Yolanda |e investigator |4 oth | |
| 700 | 1 | |a Crespi-Vilimelis, Jaume |e investigator |4 oth | |
| 700 | 1 | |a García-Parès, Eva |e investigator |4 oth | |
| 700 | 1 | |a Mingorance-Moya, Esther |e investigator |4 oth | |
| 700 | 1 | |a Bassaganyas-Vilarrasa, María Francisca |e investigator |4 oth | |
| 700 | 1 | |a Sambricio, Javier |e investigator |4 oth | |
| 700 | 1 | |a de Lucas-Viejo, Beatriz |e investigator |4 oth | |
| 700 | 1 | |a Arias-Barquet, Luis |e investigator |4 oth | |
| 700 | 1 | |a Badia-Vera, Mercè |e investigator |4 oth | |
| 700 | 1 | |a Gili-Manzanaro, Pablo |e investigator |4 oth | |
| 700 | 1 | |a Lloreda-Martín, Leyre |e investigator |4 oth | |
| 700 | 1 | |a González-Oliva, Martim Azevedo |e investigator |4 oth | |
| 700 | 1 | |a Cobo-Soriano, Rosario |e investigator |4 oth | |
| 700 | 1 | |a Zarallo-Gallardo, Jesús |e investigator |4 oth | |
| 700 | 1 | |a Escobar, Inmaculada Lozano |e investigator |4 oth | |
| 700 | 1 | |a Del Pino Cidad-Betegón, María |e investigator |4 oth | |
| 700 | 1 | |a Coca-Robinot, Javier F |e investigator |4 oth | |
| 700 | 1 | |a D'Anna-Mardero, Oriana |e investigator |4 oth | |
| 700 | 1 | |a Méndez-Martínez, Silvia |e investigator |4 oth | |
| 700 | 1 | |a Pardiñas-Barón, Nieves |e investigator |4 oth | |
| 700 | 1 | |a Gil-Ruiz, María Rocío |e investigator |4 oth | |
| 700 | 1 | |a Udaondo, Patricia |e investigator |4 oth | |
| 700 | 1 | |a Catalá-Gregori, Ana I |e investigator |4 oth | |
| 700 | 1 | |a Ramos-González, Lucía |e investigator |4 oth | |
| 700 | 1 | |a Vila-Arteaga, Jorge |e investigator |4 oth | |
| 700 | 1 | |a Gómez-Ledesma, Isabel |e investigator |4 oth | |
| 700 | 1 | |a Zarranz-Ventura, Javier |e investigator |4 oth | |
| 700 | 1 | |a Sala-Puigdollers, Anna |e investigator |4 oth | |
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| 700 | 1 | |a Figueras-Roca, Marc |e investigator |4 oth | |
| 700 | 1 | |a Marín-Méndez, Juan J |e investigator |4 oth | |
| 700 | 1 | |a Sayar-Beristain, Onintza |e investigator |4 oth | |
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