Suitability of machine learning for atrophy and fibrosis development in neovascular age-related macular degeneration
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...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universidad de Navarra |
| Repositorio: | Dadun. Depósito Académico Digital de la Universidad de Navarra |
| Idioma: | inglés |
| OAI Identifier: | oai:dadun.unav.edu:10171/70013 |
| Acceso en línea: | https://hdl.handle.net/10171/70013 |
| Access Level: | acceso abierto |
| Palabra clave: | Atrophy Extreme gradient boosting Fibrosis Machine learning NAMD Random Forest Support vector machine |
| Sumario: | 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 36months from start of anti-VEGF treatment, using only data from the first 12months. 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 36months. Conclusion: This study demonstrates the potential of ML techniques in predicting the development of fibrosis and atrophy in nAMD patients receiving longterm 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. |
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