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.
Our latest publication
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Deep Learning-Based Prediction of Individual Geographic Atrophy Progression from a Single Baseline OCTOphthalmol Sci. 2024 Jul-Aug; 4(4): 100466. Published online 2024 Jan 17. doi: 10.1016/j.xops.2024.100466
GA
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Deep Learning-Based Prediction of Individual Geographic Atrophy Progression from a Single Baseline OCTOphthalmol Sci. 2024 Jul-Aug; 4(4): 100466. Published online 2024 Jan 17. doi: 10.1016/j.xops.2024.100466
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Imaging geographic atrophy: integrating structure and function to better understand the effects of new treatmentsBr J Ophthalmol . 2024 Jan 30:bjo-2023-324246. doi: 10.1136/bjo-2023-324246.
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Monitoring der Progression von geografischer Atrophie in der optischen KohärenztomographieOphthalmologie. 2023 Sep;120(9):965-969. doi: 10.1007/s00347-023-01891-9. Epub 2023 Jul 7.
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Progression Dynamics of Early versus Later Stage Atrophic Lesions in Nonneovascular Age-Related Macular Degeneration Using Quantitative OCT Biomarker SegmentationOphthalmol Retina. 2023 Sep;7(9):762-770. doi: 10.1016/j.oret.2023.05.004. Epub 2023 May 9.PMID: 37169078
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Clinical validation for automated geographic atrophy monitoring on OCT under complement inhibitory treatmentSci Rep. 2023 Apr 29;13(1):7028.
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OCT-based applications of artificial intelligence in the management of neovascular and atrophic age-related macular degenerationWhite paper
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Developing and validating a multivariable prediction model which predicts progression of intermediate to late age-related macular degeneration—the PINNACLE trial protocolEye (Lond). 2023 Apr; 37(6):1275-1283
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Predicting Topographic Disease Progression and Treatment Response of Pegcetacoplan in Geographic Atrophy Quantified by Deep LearningOphthalmology Retina 2023; 7:4-13
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The Effect of Pegcetacoplan Treatment on Photoreceptor Maintenance in Geographic Atrophy Monitored by Artificial Intelligence – Based OCT AnalysisOphthalmology Retina 2022;6:1009-1018
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Fundus autofluorescence and optical coherence tomography biomarkers associated with the progression of geographic atrophy secondary to age-related macular degenerationEye (2022) 36: 2013–2019
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Role of Deep Learning–Quantified Hyperreflective Foci for the Prediction of Geographic Atrophy ProgressionAm J Ophthalmol. 2020 Aug; 216:257-270
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Subretinal Drusenoid Deposits and Photoreceptor LossDetecting Global and Local Progression of GeographicAtrophy by SD-OCT ImagingInvest Ophthalmol Vis Sci. 2020;61(6):11
nAMD
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A Systematic Prospective Comparison of Fluid Volume Evaluation across OCT Devices Used in Clinical PracticeOphthalmology. 2023 Dec DOI:https://doi.org/10.1016/j.xops.2023.100456
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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.
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Therapeutic response in the HAWK and HARRIER trials using deep learning in retinal fluid volume and compartment analysisEye (Lond). 2023 Apr;37(6):1160-1169. doi: 10.1038/s41433-022-02077-4. Epub 2022 May 6.PMID: 35523860
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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 methodsEye (Lond). 2023 May;37(7):1464-1469. doi: 10.1038/s41433-022-02154-8. Epub 2022 Jul 5.PMID: 35790835
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Correlation of vascular and fluid-related parameters in neovascular age-related macular degeneration using deep learningActa Ophthalmol. 2023 Feb;101(1):e95-e105. doi: 10.1111/aos.15219. Epub 2022 Aug 1.PMID: 35912717
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Predicting treat-and-extend outcomes and treatment intervals in neovascular age-related macular degeneration from retinal optical coherence tomography using artificial intelligenceFront Med (Lausanne). 2022 Aug 9;9:958469. doi: 10.3389/fmed.2022.958469. eCollection 2022.PMID: 36017006
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Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMDEye (Lond). 2023 Jun 13. doi: 10.1038/s41433-023-02615-8. PMID: 37311835
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Deep survival modeling of longitudinal retinal OCT volumes for predicting the onset of atrophy in patients with intermediate AMDBiomed Opt Express. 2023 May 2;14(6):2449-2464. doi: 10.1364/BOE.487206. eCollection 2023 Jun 1.PMID: 37342683
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Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMDEye (2023). https://doi.org/10.1038/s41433-023-02615-8
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OCT-based applications of artificial intelligence in the management of neovascular and atrophic age-related macular degenerationWhite paper
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Developing and validating a multivariable prediction model which predicts progression of intermediate to late age-related macular degeneration—the PINNACLE trial protocolEye (Lond). 2023 Apr; 37(6):1275-1283
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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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Baseline predictors for subretinal fibrosis in neovascular age-related macular degeneration.Sci Rep. 2022 Jan 7;12(1):88
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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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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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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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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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A paradigm shift in imaging biomarkers in neovascular age-related macular degeneration.[Prog Retin Eye Res. 2016; 50: 1-24]
DR and DME
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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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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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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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Computational image analysis for prognosis determination in DME.[Vision Res. 2017; 139: 204-210]
Exudative macular disease
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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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AI-based monitoring of retinal fluid in disease activity and under therapy.Prog Retin Eye Res. 2021; Article in press
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Fully Automated Detection and Quantification of Macular Fluid in OCT Using Deep Learning[Ophthalmology. 2018;125(4):549-558]
AMD progression
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Deep survival modeling of longitudinal retinal OCT volumes for predicting the onset of atrophy in patients with intermediate AMDBiomed Opt Express. 2023 May 2;14(6):2449-2464. doi: 10.1364/BOE.487206. eCollection 2023 Jun 1.PMID: 37342683
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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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Prediction of Individual Disease Conversion in Early AMD Using Artificial Intelligence.[Invest Ophthalmol Vis Sci. 2018;59(8):3199-3208]
AI development
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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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Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learning.[Sci Rep. 2020; 10(1):12954]
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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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RETOUCH: The Retinal OCT Fluid Detection and Segmentation Benchmark and Challenge.[IEEE Trans Med Imaging. 2019;38(8):1858-1874]
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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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Artificial intelligence in retina.[Prog Retin Eye Res. 2018; 67:1-29]
Guidelines
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Deliberations of an International Panel of Experts on OCTA Nomenclature of nAMD.[Ophthalmology. 2020: S0161-6420(20)31198-2]
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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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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]