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ARVO Annual Meeting Abstract June 2022

Advances in photoreceptor quantification moving from conventional to high-resolution SPECTRALIS optical coherence tomography

Sophie Frank; Hrvoje Bogunovic; Oliver Leingang; Philipp Fuchs; Leonard Coulibaly; Gregor Sebastian Reiter; Ursula Schmidt-Erfurth

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):1023 – F0270. doi:

ARVO Annual Meeting Abstract June 2022

Deep Survival Prediction of Progression from Intermediate to Atrophic AMD from Longitudinal Retinal OCT

Antoine Rivail; Wolf-Dieter Vogl; Sophie Riedl; Christoph Grechenig; Gregor Sebastian Reiter; Robyn H Guymer; Zhichao Wu; Ursula Schmidt-Erfurth; Hrvoje Bogunovic

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):3858. doi:

ARVO Annual Meeting Abstract June 2022

Self-supervised pretraining enables deep learning-based classification of AMD with fewer annotations

Robbie Holland; Martin Joseph Menten; Oliver Leingang; Hrvoje Bogunovic; Ahmed M Hagag; Rebecca Kaye; Sophie Riedl; Ghislaine Traber; Lars Fritsche; Toby Prevost; Hendrik P Scholl; Ursula Schmidt-Erfurth; Sobha Sivaprasad; Daniel Rueckert; Andrew J Lotery

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):3004 – F0274. doi:

ARVO Annual Meeting Abstract June 2022

Ratio of photoreceptor to retinal pigment epithelium loss as an important predictor of GA growth and therapeutic efficacy

Ursula Schmidt-Erfurth; Sophie Riedl; Wolf-Dieter Vogl; Julia Mai; Gregor Sebastian Reiter; Dmitrii Lachinov; Christoph Grechenig; Alex McKeown; Lukas Scheibler; Hrvoje Bogunovic

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):2324. doi:

ARVO Annual Meeting Abstract June 2022

Automated deep learning-based AMD stage detection in real-world OCT datasets

Oliver Leingang; Hrvoje Bogunovic; Sophie Riedl; Arunava Chakravarty; Martin Joseph Menten; Robbie Holland; Ghislaine L Traber; Lars Fritsche; Toby Prevost; Hendrik P Scholl; Daniel Rueckert; Sobha Sivaprasad; Andrew J Lotery; Ursula Schmidt-Erfurth

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):2106 – F0095. doi:

ARVO Annual Meeting Abstract June 2022

Analysis of AI-quantified fluid dynamics over time and their impact on visual acuity in real-world patients with neovascular age-related macular degeneration

Philipp Fuchs; Leonard Coulibaly; Hrvoje Bogunovic; Oliver Leingang; Anastasiia Gruber; Daniel Barthelmes; Gregor Sebastian Reiter; Ursula Schmidt-Erfurth

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):1333 – F0167. doi:

ARVO Annual Meeting Abstract June 2022

Artificial intelligence to identify conventional treatment patterns in neovascular age-related macular degeneration in a real-world population

VIRGINIA DE SOUZA LEOLINO MARES; Hrvoje Bogunovic; Oliver Leingang; Daniel Barthelmes; Gregor Sebastian Reiter; Ursula Schmidt-Erfurth

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):3014 – F0284. doi:

ARVO Annual Meeting Abstract June 2022

Automated detection of morphologic changes on SD-OCT leading toward outer retinal atrophy in intermediate age-related macular degeneration in the prospective PINNACLE trial

Sophie Riedl; Wolf-Dieter Vogl; Oliver Leingang; Hrvoje Bogunovic; Lars Fritsche; Toby Prevost; Daniel Rueckert; Hendrik P Scholl; Sobha Sivaprasad; Andrew J Lotery; Ursula Schmidt-Erfurth

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):2319. doi:

ARVO Annual Meeting Abstract June 2022

Discovery of imaging biomarkers for healthy aging and age-related macular degeneration using counterfactual generative adversarial networks

Martin Joseph Menten; Oliver Leingang; Hrvoje Bogunovic; Robbie Holland; Ahmed M Hagag; Rebecca Kaye; Sophie Riedl; Ghislaine Traber; Lars Fritsche; Toby Prevost; Hendrik P Scholl; Ursula Schmidt-Erfurth; Sobha Sivaprasad; Daniel Rueckert; Andrew J Lotery

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):3855. doi:

ARVO Annual Meeting Abstract June 2022

Early changes of photoreceptor layer thickness following surgery in eyes with epiretinal membranes analyzed by deep-learning

Marlene Hollaus; Michael Georgopoulos; Johannes Iby; Jonas Brugger; Oliver Leingang; Hrvoje Bogunovic; Ursula Schmidt-Erfurth; Stefan Sacu

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):2994 – F0264. doi:

ARVO Annual Meeting Abstract June 2022

Comparison of a human versus deep learning-based evaluation of fluid change in real-world OCT images of neovascular AMD

Martin Michl; Bianca S Gerendas; Philipp Seeböck; Elisa de Llano; Anastasiia Gruber; Felix Goldbach; Georgios Mylonas; Oliver Leingang; Wolf Bühl; Stefan Sacu; Hrvoje Bogunovic; Ursula Schmidt-Erfurth

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):3354 – F0163. doi:

ARVO Annual Meeting Abstract June 2022

The RAP study, Report 6: Quantification of exudative biomarkers in neovascular age-related macular degeneration (nAMD) using deep learning, a type-based comparative analysis.

Bilal Haj Najeeb; Bianca S Gerendas; Hrvoje Bogunovic; Ursula Schmidt-Erfurth

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):3861. doi:

ARVO Annual Meeting Abstract June 2022

Depolarization mapping in the retina using polarization-sensitive OCT in a large field of view

Alice Regina Motschi; Sylvia Desissaire; Markus Schranz; Stefan Steiner; Florian Schwarzhans; Hrvoje Bogunovic; Philipp Ken Roberts; Clemens Vass; Michael Pircher; Christoph K Hitzenberger

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):4071 – F0035. doi:

ARVO Annual Meeting Abstract June 2022

Artificial Intelligence to Identify Retinal OCT Parameters Predictive of Patient Visual Outcomes under a Treat-and-Extend Regimen

Hrvoje Bogunovic; Gregor Sebastian Reiter; Oliver Leingang; Ursula Schmidt-Erfurth

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):1346 – F0180. doi:

ARVO Annual Meeting Abstract June 2022

GA area measured by SD-OCT shows good correspondence with FAF-based measurements in patients enrolled in the FILLY trial

Julia Mai; Dmitrii Lachinov; Sophie Riedl; Gregor Sebastian Reiter; Wolf-Dieter Vogl; Hrvoje Bogunovic; Ursula Schmidt-Erfurth

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):1015 – F0262. doi:

ARVO Annual Meeting Abstract June 2022

Spatial and temporal distribution of recurrent fluid in neovascular AMD quantified with a deep learning algorithm

Veronika Röggla; Gregor Sebastian Reiter; Philipp Fuchs; Oliver Leingang; Leonard Mana Coulibaly; Hrvoje Bogunovic; Daniel Barthelmes; Ursula Schmidt-Erfurth

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):3019 – F0289. doi:

ARVO Annual Meeting Abstract June 2022

Comparison between a deep learning algorithm and human expertise predicting the progression of geographic atrophy (GA) secondary to age-related macular degeneration

Gregor Sebastian Reiter; Dmitrii Lachinov; Wolf Bühl; Günther Weigert; Christoph Grechenig; Hrvoje Bogunovic; Ursula Schmidt-Erfurth

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):3857. doi:

ARVO Annual Meeting Abstract June 2022

Progression dynamics of early versus advanced atrophic lesions in non-neovascular AMD using AI-based OCT analysis

Leonard Coulibaly; Philipp Fuchs; Dmitrii Lachinov; Oliver Leingang; Hrvoje Bogunovic; Gregor Sebastian Reiter; Ursula Schmidt-Erfurth

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):3011 – F0281. doi:

ARVO Annual Meeting Abstract June 2021

Quantifying the clinical evaluation of retinal fluid change in real-world OCT images using deep learning

Martin Michl; Felix Goldbach; Georgios Mylonas; Gabor Deak; Thomas Alten; Stefan Sacu; Hrvoje Bogunovic; Bianca S Gerendas; Ursula Schmidt-Erfurth

Invest. Ophthalmol. Vis. Sci.. 2021; 62(8):2455. doi:

ARVO Annual Meeting Abstract June 2021

Association between retinal thickness and retinal fluid volumes measured by deep learning in the HAWK & HARRIER trials

Maximilian Pawloff; Hrvoje Bogunovic; Zufar Mulyukov; Daniel Lorand; Ursula Schmidt-Erfurth

Invest. Ophthalmol. Vis. Sci.. 2021; 62(8):2105. doi:

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Advances in photoreceptor quantification moving from conventional to high-resolution SPECTRALIS optical coherence tomography

Deep Survival Prediction of Progression from Intermediate to Atrophic AMD from Longitudinal Retinal OCT

Self-supervised pretraining enables deep learning-based classification of AMD with fewer annotations

Ratio of photoreceptor to retinal pigment epithelium loss as an important predictor of GA growth and therapeutic efficacy

Automated deep learning-based AMD stage detection in real-world OCT datasets

Analysis of AI-quantified fluid dynamics over time and their impact on visual acuity in real-world patients with neovascular age-related macular degeneration

Artificial intelligence to identify conventional treatment patterns in neovascular age-related macular degeneration in a real-world population

Automated detection of morphologic changes on SD-OCT leading toward outer retinal atrophy in intermediate age-related macular degeneration in the prospective PINNACLE trial

Discovery of imaging biomarkers for healthy aging and age-related macular degeneration using counterfactual generative adversarial networks

Early changes of photoreceptor layer thickness following surgery in eyes with epiretinal membranes analyzed by deep-learning

Comparison of a human versus deep learning-based evaluation of fluid change in real-world OCT images of neovascular AMD

The RAP study, Report 6: Quantification of exudative biomarkers in neovascular age-related macular degeneration (nAMD) using deep learning, a type-based comparative analysis.

Depolarization mapping in the retina using polarization-sensitive OCT in a large field of view

Artificial Intelligence to Identify Retinal OCT Parameters Predictive of Patient Visual Outcomes under a Treat-and-Extend Regimen

GA area measured by SD-OCT shows good correspondence with FAF-based measurements in patients enrolled in the FILLY trial

Spatial and temporal distribution of recurrent fluid in neovascular AMD quantified with a deep learning algorithm

Comparison between a deep learning algorithm and human expertise predicting the progression of geographic atrophy (GA) secondary to age-related macular degeneration

Progression dynamics of early versus advanced atrophic lesions in non-neovascular AMD using AI-based OCT analysis

Quantifying the clinical evaluation of retinal fluid change in real-world OCT images using deep learning

Association between retinal thickness and retinal fluid volumes measured by deep learning in the HAWK & HARRIER trials

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