Aim To predict antivascular endothelial growth factor (VEGF) treatment requirements, visual acuity and morphological outcomes in neovascular age-related macular degeneration (nAMD) using fluid quantification by artificial intelligence (AI) in a real-world cohort.
Methods Spectral-domain optical coherence tomography data of 158 treatment-naïve patients with nAMD from the Fight Retinal Blindness! registry in Zurich were processed at baseline, and after initial treatment using intravitreal anti-VEGF to predict subsequent 1-year and 4-year outcomes. Intraretinal and subretinal fluid and pigment epithelial detachment volumes were segmented using a deep learning algorithm (Vienna Fluid Monitor, RetInSight, Vienna, Austria). A predictive machine learning model for future treatment requirements and morphological outcomes was built using the computed set of quantitative features.
Results Two hundred and two eyes from 158 patients were evaluated. 107 eyes had a lower median (≤7) and 95 eyes had an upper median (≥8) number of injections in the first year, with a mean accuracy of prediction of 0.77 (95% CI 0.71 to 0.83) area under the curve (AUC). Best-corrected visual acuity at baseline was the most relevant predictive factor determining final visual outcomes after 1 year. Over 4 years, half of the eyes had progressed to macular atrophy (MA) with the model being able to distinguish MA from non-MA eyes with a mean AUC of 0.70 (95% CI 0.61 to 0.79). Prediction for subretinal fibrosis reached an AUC of 0.74 (95% CI 0.63 to 0.81).
Conclusions The regulatory approved AI-based fluid monitoring allows clinicians to use automated algorithms in prospectively guided patient treatment in AMD. Furthermore, retinal fluid localisation and quantification can predict long-term morphological outcomes.
Data availability statement
Data are available on reasonable request.
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DB and GSR are joint senior authors.
DB and GSR contributed equally.
Contributors VM: performed literature review, wrote manuscript. UMS-E: provided supervision of research and reviewed/edited manuscript. Guarantor. OL: analysed the data, reviewed/edited manuscript. PF: reviewed/edited manuscript. MBN: reviewed/edited manuscript. HB: reviewed/edited manuscript. DB: provided supervision of research and reviewed/edited manuscript. GSR: provided supervision of research and reviewed/edited manuscript.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests UMS-E: Scientific consultancy for Genentech, Novartis, Roche, Heidelberg Engineering, Kodiak, RetInSight, Topcon. HB: Grants from Heidelberg Engineering and Apellis. Speaker fees from Bayer, Roche and Apellis. DB: Scientific consultancy, grants and speaker fees for Bayer and Novartis. GSR: Grant from RetInSight.VM, OL, PF, MBN: No financial support or conflicts of interest.
Provenance and peer review Not commissioned; externally peer reviewed.