Details

Autor(en) / Beteiligte

• Fuente, Jesus
• Llorente‐González, Sara
• Fernandez‐Robredo, Patricia
• Hernandez, María
• García‐Layana, Alfredo
• Ochoa, Idoia
• Recalde, Sergio
• Alonso‐Iglesias, Elena
• Salinas‐Alamán, Ángel
• Bezunartea‐Bezunartea, Jaione
• Abraldes López‐Veiga, Maximino J.
• Díez‐Sotelo, Marta
• Sarandeses‐Díez, María Teresa
• Taboada‐Gago, María Dolores
• Asorey, María Knight
• Valverde‐Megías, Alicia
• Felipe‐Márquez, Gema
• Honrubia‐Grijalbo, Ana
• Núñez‐Benito, María Esther
• Escobar‐Barranco, José J.
• Pina‐Marín, Begoña
• Fernández‐Bonet, Manel
• Jadraque‐Ruíz, Yolanda
• Crespi‐Vilimelis, Jaume
• García‐Parès, Eva
• Mingorance‐Moya, Esther
• Bassaganyas‐Vilarrasa, María Francisca
• Sambricio, Javier
• Lucas‐Viejo, Beatriz
• Arias‐Barquet, Luis
• Badia‐Vera, Mercè
• Gili‐Manzanaro, Pablo
• Lloreda‐Martín, Leyre
• González‐Oliva, Martim Azevedo
• Cobo‐Soriano, Rosario
• Zarallo‐Gallardo, Jesús
• Escobar, Inmaculada Lozano
• Pino Cidad‐Betegón, María
• Coca‐Robinot, Javier F.
• D’Anna‐Mardero, Oriana
• Méndez‐Martínez, Silvia
• Pardiñas‐Barón, Nieves
• Gil‐Ruiz, María Rocío
• Udaondo, Patricia
• Catalá‐Gregori, Ana I.
• Ramos‐González, Lucía
• Vila‐Arteaga, Jorge
• Gómez‐Ledesma, Isabel
• Zarranz‐Ventura, Javier
• Sala‐Puigdollers, Anna
• Sabater‐Cruz, Noelia
• Bernal‐Morales, Carolina
• Figueras‐Roca, Marc
• Marín‐Méndez, Juan J.
• Sayar‐Beristain, Onintza

Titel

Suitability of machine learning for atrophy and fibrosis development in neovascular age‐related macular degeneration

Ist Teil von

• Acta ophthalmologica (Oxford, England), 2024-08, Vol.102 (5), p.e831-e841

Ort / Verlag

England: Wiley Subscription Services, Inc

Erscheinungsjahr

2024

Quelle

Wiley Online Library All Journals

Beschreibungen/Notizen

• 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.