Approved AI-based fluid monitoring to identify morphological and functional treatment outcomes in neovascular age-related macular degeneration in real-world routine.

Autor:	Mares V; Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria.; Department of Ophthalmology, Federal University of Minas Gerais, Belo Horizonte, Brazil., Schmidt-Erfurth UM; Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria ursula.schmidt-erfurth@meduniwien.ac.at., Leingang O; Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Fuchs P; Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Nehemy MB; Department of Ophthalmology, Federal University of Minas Gerais, Belo Horizonte, Brazil., Bogunovic H; Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria., Barthelmes D; Department of Ophthalmology, University of Zurich Faculty of Medicine, Zurich, Switzerland.; Department of Ophthalmology, The University of Sydney, Sydney, New South Wales, Australia., Reiter GS; Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria.
Jazyk:	angličtina
Zdroj:	The British journal of ophthalmology [Br J Ophthalmol] 2024 Jun 20; Vol. 108 (7), pp. 971-977. Date of Electronic Publication: 2024 Jun 20.
DOI:	10.1136/bjo-2022-323014
Abstrakt:	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. Competing Interests: 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. (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)
Databáze:	MEDLINE
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