Research
It takes rigorous quality-by-design R&D process, state-of-the-art cross functional expertise, capabilities and partnerships to develop the AI-solutions that RetInSight envisions bringing to eye care professionals, patients and payors in the coming years. Please find our scientific publications here.
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Fundus autofluorescence and optical coherence tomographyThe Royal Collage of Ophthalmologists, 2021
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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
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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
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Baseline predictors for subretinal fibrosis in neovascular age-related macular degeneration.Sci Rep. 2022 Jan 7;12(1):88
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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
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Validation of an automated fluid algorithm on real-world data of neovascular age-related macular degeneration over five yearsRetina: September 2022 – Volume 42 – Issue 9 – p 1673-1682
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Leveraging artificial intelligence in clinical management of nAMDConference | EURETINA September 12, 2022
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AI-based monitoring of retinal fluid in disease activity and under therapy.Prog Retin Eye Res. 2021; Article in press
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Artificial intelligence in retina.[Prog Retin Eye Res. 2018; 67:1-29]
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A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration.[Prog Retin Eye Res. 2016; 50: 1-24]
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Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning[Ophthalmology. 2018;125(4):549-558]
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Application of Automated Quantification of Fluid Volumes to Anti-VEGF Therapy of Neovascular Age-Related Macular Degeneration.[Ophthalmology. 2020; 127(9):1211-1219]
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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]
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RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge.[IEEE Trans Med Imaging. 2019;38(8):1858-1874]
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Machine Learning to Analyze the Prognostic Value of Current Imaging Biomarkers in Neovascular Age-Related Macular Degeneration.[Ophthalmol Retina. 2018, 2(1):24-30]
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Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence.[Invest Ophthalmol Vis Sci. 2018;59(8):3199-3208]
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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]
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Role of Deep Learning-Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy Progression.[Am J Ophthalmol. 2020;216: 257-270]
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Subretinal Drusenoid Deposits and Photoreceptor Loss Detecting Global and Local Progression of Geographic Atrophy by SD-OCT Imaging.[Invest Ophthalmol Vis Sci. 2020;61(6):11]
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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]
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Computational image analysis for prognosis determination in DME.[Vision Res. 2017; 139: 204-210]
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Topographic Analysis of Photoreceptor Loss Correlated with Disease Morphology in Neovascular Age-Related Macular Degeneration.[Retina. 2020;40(11):2148-2157]
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Automated Quantification of Photoreceptor alteration in macular disease using Optical Coherence Tomography and Deep Learning.[Sci Rep. 2020; 10(1): 5619]
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f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.[Med Image Anal. 2019;54:30-44]
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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]
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Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learning.[Sci Rep. 2020; 10(1):12954]
Guidelines
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Guidelines for the Management of Retinal Vein Occlusion by the European Society of Retina Specialists (EURETINA).[Ophthalmologica. 2019;242(3):123-162]
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Guidelines for the Management of Diabetic Macular Edema by the European Society of Retina Specialists (EURETINA).[Ophthalmologica. 2017;237(4):185-2229]
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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]
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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]
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Deliberations of an International Panel of Experts on OCTA Nomenclature of nAMD.[Ophthalmology. 2020: S0161-6420(20)31198-2]