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- AI development
- AMD progression
- DR and DME
- Exudative macular disease
- GA
- iAMD
- nAMD
Artificial intelligence in assessing progression of age-related macular degeneration
Eye (London). 2024 Nov 18. doi: 10.1038/s41433-024-03460-z. Online ahead of print.
Repeatability of Microperimetry in areas of RPE and Photoreceptor loss in Geographic Atrophy supported by AI-based OCT biomarker quantification
American Journal of Ophthalmology. 2024 Nov 13:S0002-9394(24)00520-8. doi: 10.1016/j.ajo.2024.11.005. Online ahead of print.
Topographic and quantitative correlation of structure and function using deep learning in subclinical biomarkers of intermediate age-related macular degeneration
nature - Scientific Reports - volume 14, Article number: 28165 (2024). (2024 Nov 15;14(1):28165. doi: 10.1038/s41598-024-72522-9.)
AI in the clinical management of GA: A novel therapeutic universe requires novel tools
Progress in Retinal and Eye Research Volume 103, November 2024, 101305 | 2024 Sep 27:103:101305. doi: 10.1016/j.preteyeres.2024.101305.
Rolle der künstlichen Intelligenz bei verschiedenen retinalen Erkrankungen
Klin Monbl Augenheilkd . 2024 Sep;241(9):1023-1031. doi: 10.1055/a-2378-6138. Epub 2024 Sep 16.
Artificial intelligence for geographic atrophy: pearls and pitfalls
Curr Opin Ophthalmol. 2024 Aug 27. doi: 10.1097/ICU.0000000000001085.
Quantitative comparison of automated OCT and conventional FAF-based geographic atrophy measurements in the phase 3 OAKS/DERBY trials
Sci Rep. 2024; 14: 20531. Published online 2024 Sep 4. doi: 10.1038/s41598-024-71496-y
Disease activity and therapeutic response to pegcetacoplan for geographic atrophy identified by deep learning-based analysis of OCT
American Academy of Ophthalmology. Published: August 14, 2024 / DOI:https://doi.org/10.1016/j.ophtha.2024.08.017 / PMID: 39151755
Deep Learning-Based Prediction of Individual Geographic Atrophy Progression from a Single Baseline OCT
Ophthalmol Sci. 2024 Jul-Aug; 4(4): 100466. Published online 2024 Jan 17. doi: 10.1016/j.xops.2024.100466
A Systematic Prospective Comparison of Fluid Volume Evaluation across OCT Devices Used in Clinical Practice
Ophthalmology. 2023 Dec DOI:https://doi.org/10.1016/j.xops.2023.100456
Imaging geographic atrophy: integrating structure and function to better understand the effects of new treatments
Br J Ophthalmol . 2024 Jan 30:bjo-2023-324246. doi: 10.1136/bjo-2023-324246.
Long-term effect of fluid volumes during the maintenance phase in neovascular age-related macular degeneration in the real world: results from Fight Retinal Blindness!
Can J Ophthalmol . 2023 Nov 18:S0008-4182(23)00335-6. doi: 10.1016/j.jcjo.2023.10.017.
Monitoring der Progression von geografischer Atrophie in der optischen Kohärenztomographie
Ophthalmologie. 2023 Sep;120(9):965-969. doi: 10.1007/s00347-023-01891-9. Epub 2023 Jul 7.
Progression Dynamics of Early versus Later Stage Atrophic Lesions in Nonneovascular Age-Related Macular Degeneration Using Quantitative OCT Biomarker Segmentation
Ophthalmol Retina. 2023 Sep;7(9):762-770. doi: 10.1016/j.oret.2023.05.004. Epub 2023 May 9.PMID: 37169078
Therapeutic response in the HAWK and HARRIER trials using deep learning in retinal fluid volume and compartment analysis
Eye (Lond). 2023 Apr;37(6):1160-1169. doi: 10.1038/s41433-022-02077-4. Epub 2022 May 6.PMID: 35523860
Personalized treatment supported by automated quantitative fluid analysis in active neovascular age-related macular degeneration (nAMD)-a phase III, prospective, multicentre, randomized study: design and methods
Eye (Lond). 2023 May;37(7):1464-1469. doi: 10.1038/s41433-022-02154-8. Epub 2022 Jul 5.PMID: 35790835
Correlation of vascular and fluid-related parameters in neovascular age-related macular degeneration using deep learning
Acta Ophthalmol. 2023 Feb;101(1):e95-e105. doi: 10.1111/aos.15219. Epub 2022 Aug 1.PMID: 35912717
Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligence
Front Med (Lausanne). 2022 Aug 9;9:958469. doi: 10.3389/fmed.2022.958469. eCollection 2022.PMID: 36017006
Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD
Eye (Lond). 2023 Jun 13. doi: 10.1038/s41433-023-02615-8. PMID: 37311835
Deep survival modeling of longitudinal retinal OCT volumes for predicting the onset of atrophy in patients with intermediate AMD
Biomed Opt Express. 2023 May 2;14(6):2449-2464. doi: 10.1364/BOE.487206. eCollection 2023 Jun 1.PMID: 37342683
Clinical validation for automated geographic atrophy monitoring on OCT under complement inhibitory treatment
Sci Rep. 2023 Apr 29;13(1):7028.
Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD
Eye (2023). https://doi.org/10.1038/s41433-023-02615-8
OCT-based applications of artificial intelligence in the management of neovascular and atrophic age-related macular degeneration
White paper
Developing and validating a multivariable prediction model which predicts progression of intermediate to late age-related macular degeneration—the PINNACLE trial protocol
Eye (Lond). 2023 Apr; 37(6):1275-1283
Predicting Topographic Disease Progression and Treatment Response of Pegcetacoplan in Geographic Atrophy Quantified by Deep Learning
Ophthalmology Retina 2023; 7:4-13
The Effect of Pegcetacoplan Treatment on Photoreceptor Maintenance in Geographic Atrophy Monitored by Artificial Intelligence – Based OCT Analysis
Ophthalmology Retina 2022;6:1009-1018
Validation of an automated fluid algorithm on real-world data of neovascular age-related macular degeneration over five years
Retina: September 2022 - Volume 42 - Issue 9 - p 1673-1682
Comparison of Fundus Autofluorescence Versus Optical Coherence Tomography–based Evaluation of the Therapeutic Response to Pegcetacoplan in Geographic Atrophy
Am J Ophthalmol. 2022 Dec: 244:175-182. doi: 10.1016/j.ajo.2022.06.023. Epub 2022 Jul 16.
Linking Function and Structure with ReSenseNet: Predicting Retinal Sensitivity from Optical Coherence Tomography using Deep Learning.
Ophthalmol Retina. 2022 Feb 5:S2468-6530(22)00043-4
A systematic correlation of central subfield thickness (CSFT) with retinal fluid volumes quantified by deep learning in the major exudative macular diseases.
Retina. 2021 Dec 17
Comparison of early diabetic retinopathy staging in asymptomatic patients between autonomous AI-based screening and human-graded ultra-widefield colour fundus images.
Eye (Lond). 2022 Feb 7
AI-based monitoring of retinal fluid in disease activity and under therapy.
Prog Retin Eye Res. 2021; Article in press
Baseline predictors for subretinal fibrosis in neovascular age-related macular degeneration.
Sci Rep. 2022 Jan 7;12(1):88
Fundus autofluorescence and optical coherence tomography biomarkers associated with the progression of geographic atrophy secondary to age-related macular degeneration
Eye (2022) 36: 2013–2019
Analysis of Fluid Volume and its Impact on Visual Acuity in the FLUID Study as Quantified with Deep Learning.
[Retina. 2020 Nov 18. Online ahead of print]
Deliberations of an International Panel of Experts on OCTA Nomenclature of nAMD.
[Ophthalmology. 2020: S0161-6420(20)31198-2]
Topographic Analysis of Photoreceptor Loss Correlated with Disease Morphology in Neovascular Age-Related Macular Degeneration.
[Retina. 2020;40(11):2148-2157]
Role of Deep Learning–Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy Progression
Am J Ophthalmol. 2020 Aug; 216:257-270
Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learning.
[Sci Rep. 2020; 10(1):12954]
Quantification of Fluid Resolution and Visual Acuity Gain in Patients With Diabetic Macular Edema Using Deep Learning: A Post Hoc Analysis of a Randomized Clinical Trial.
[JAMA Ophthalmol. 2020; 138(9):945-953]
Subretinal Drusenoid Deposits and Photoreceptor LossDetecting Global and Local Progression of GeographicAtrophy by SD-OCT Imaging
Invest Ophthalmol Vis Sci. 2020;61(6):11
Consensus Nomenclature for Reporting Neovascular Age-Related Macular Degeneration Data: Consensus on Neovascular Age-Related Macular Degeneration Nomenclature Study Group.
[Ophthalmology. 2020;127(5):616-636. Erratum in: Ophthalmology. 2020;127(10):1434-1435]
Characterization of Drusen and Hyperreflective Foci as Biomarkers for Disease Progression in Age-Related Macular Degeneration Using Artificial Intelligence in Optical Coherence Tomography
[JAMA Ophthalmol. 2020;138(7):740-747]
Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning.
[Sci Rep. 2020; 10(1): 5619]
Application of Automated Quantification of Fluid Volumes to Anti-VEGF Therapy of Neovascular Age-Related Macular Degeneration.
[Ophthalmology. 2020; 127(9):1211-1219]
Exploiting Epistemic Uncertainty of Anatomy Segmentation for Anomaly Detection in Retinal OCT.
[IEEE Trans Med Imaging. 2020;39(1):87-98. Erratum in: IEEE Trans Med Imaging. 2020;39(4):1291]
Guidelines for the Management of Retinal Vein Occlusion by the European Society of Retina Specialists (EURETINA).
[Ophthalmologica. 2019;242(3):123-162]
RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge.
[IEEE Trans Med Imaging. 2019;38(8):1858-1874]
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.
[Med Image Anal. 2019;54:30-44]
Artificial intelligence in retina.
[Prog Retin Eye Res. 2018; 67:1-29]
Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence.
[Invest Ophthalmol Vis Sci. 2018;59(8):3199-3208]
Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration.
[Ophthalmol Retina. 2018, 2(1):24-30]
Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning
[Ophthalmology. 2018;125(4):549-558]
Computational image analysis for prognosis determination in DME.
[Vision Res. 2017; 139: 204-210]
Guidelines for the Management of Diabetic Macular Edema by the European Society of Retina Specialists (EURETINA).
[Ophthalmologica. 2017;237(4):185-2229]
A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration.
[Prog Retin Eye Res. 2016; 50: 1-24]
Guidelines for the management of neovascular age-related macular degeneration by the European Society of Retina Specialists (EURETINA).
[Br J Ophthalmol. 2014;98(9):1144-67]