Suitability of machine learning for atrophy and fibrosis development in neovascular age-related macular degeneration.
Jesús de la Fuente CedeñoSara Llorente-GonzálezPatricia Fernández-RobredoMaría HernandezAlfredo García-LayanaIdoia OchoaSergio Recalde-Maestrenull nullPublished in: Acta ophthalmologica (2023)
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.
Keyphrases
- age related macular degeneration
- diabetic retinopathy
- machine learning
- artificial intelligence
- big data
- type diabetes
- vascular endothelial growth factor
- optical coherence tomography
- endothelial cells
- metabolic syndrome
- skeletal muscle
- climate change
- electronic health record
- combination therapy
- replacement therapy
- weight loss
- human health