Artificial intelligence-based fluid quantification and associated visual outcomes in a real-world, multicentre neovascular age-related macular degeneration national database.
Ruben Martin-PinardelJordi Izquierdo-SerraSandro De ZanetAlba Parrado-CarrilloGonzaga Garay-AramburuMartin PuzoCarolina ArruabarrenaLaura SararolsMaximino AbraldesLaura Broc-IturraldeJose Juan Escobar-BarrancoMarta FigueroaMiguel Angel ZapataJose Maria Ruiz-MorenoAina Moll-UdinaCarolina Bernal-MoralesMª Socorro AlforjaMarc Figueras-RocaLaia Gómez-BaldóCarlos CillerStefanos ApostolopoulosAgata MosinskaRicardo-Pedro Casaroli-MaranoJavier Zarranz-Venturanull nullPublished in: The British journal of ophthalmology (2023)
This real-world study describes an AI-based analysis of fluid dynamics and defines baseline OCT-based patient profiles that associate 12-month visual outcomes in a large cohort of treated naïve nAMD eyes nationwide.
Keyphrases
- artificial intelligence
- age related macular degeneration
- machine learning
- big data
- deep learning
- optical coherence tomography
- clinical trial
- cross sectional
- emergency department
- case report
- study protocol
- quality improvement
- randomized controlled trial
- diabetic retinopathy
- type diabetes
- newly diagnosed
- weight loss
- electronic health record