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ARVO Annual Meeting Abstract June 2023

Deep learning segmentation of foveal avascular zone (FAZ) in optical coherence tomography angiography (OCTA) of nonproliferative diabetic retinopathy

Acner Camino Benech; Zilin Wang; Aditi Basu Bal; Fethallah Benmansour; Rick Carano; Daniela Ferrara

Invest. Ophthalmol. Vis. Sci.. 2023; 64(8):1125.

ARVO Annual Meeting Abstract June 2022

Deep learning (DL) to segment retinal layer disruption on optical coherence tomography (OCT) in neovascular age-related macular degeneration (nAMD)

Yun Li; Huanxiang Lu; Thomas Albrecht; Andreas Maunz; Ales Neubert; Fethallah Benmansour; Alvaro Gomariz; Jennifer Luu; Daniela Ferrara

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):2079 – F0068.

ARVO Annual Meeting Abstract June 2022

Prediction of proliferative diabetic retinopathy (PDR) from nonproliferative diabetic retinopathy (NPDR) using real-world (RW) electronic health record (EHR) data

Amy Shrader Babiuch; Qi Yang; Dimitrios Damopoulos; Shemra Rizzo; Carolina C S Valentim; Anna K Wu; Fethallah Benmansour; Jennifer Luu; Aneesha Kalur; Resya Sastry; Amogh Iyer; Justin Muste; Galin Spicer; Daniela Ferrara; Rishi P Singh

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):1154.

ARVO Annual Meeting Abstract June 2022

Deep learning (DL) model to identify moderately severe and severe nonproliferative diabetic retinopathy (NPDR) from 7-field color fundus photographs (7F-CFP)

Fethallah Benmansour; Dimitrios Damopoulos; Qi Yang; Ales Neubert; Luís Mendes; Torcato Santos; Nripun Sredar; Jose G Cunha-Vaz; Daniela Ferrara

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):2086 – F0075.

ARVO Annual Meeting Abstract June 2022

Detecting patients with ocular comorbidities when screening for diabetic retinopathy (DR): an out-of-distribution (OOD) perspective

Jelena Novosel; Jian Dai; Daniela Ferrara; Fethallah Benmansour

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):2987 – F0257.

ARVO Annual Meeting Abstract June 2022

Predicting 5-year progression to vision-threatening complications using machine learning

Dimitrios Damopoulos; Luís Mendes; Torcato Santos; Ales Neubert; Daniela Ferrara; Jose G Cunha-Vaz; Fethallah Benmansour

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):2085 – F0074.

ARVO Annual Meeting Abstract June 2022

A unified deep learning approach for OCT segmentation from different devices and retinal diseases

Alvaro Gomariz; Huanxiang Lu; Yun Li; Thomas Albrecht; Andreas Maunz; Fethallah Benmansour; Jennifer Luu; Orcun Goksel; Daniela Ferrara

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):2053 – F0042.

ARVO Annual Meeting Abstract June 2021

Prediction of ranibizumab injection frequency in patients with macular edema due to retinal vein occlusion during as-needed dosing period of BRAVO and CRUISE Phase 3 trials

Yun Li; Verena Steffen; Fethallah Benmansour; Michel Friesenhahn; Zdenka Haskova

Invest. Ophthalmol. Vis. Sci.. 2021; 62(8):127.

ARVO Annual Meeting Abstract June 2021

Automated screening of moderately severe and severe nonproliferative diabetic retinopathy (NPDR) from 7-field color fundus photographs (7F-CFP) using deep learning (DL)

Fethallah Benmansour; Qi Yang; Dimitrios Damopoulos; Neha Anegondi; Ales Neubert; Jelena Novosel; Beatriz Garcia Armendariz; Daniela Ferrara

Invest. Ophthalmol. Vis. Sci.. 2021; 62(8):115.

ARVO Annual Meeting Abstract June 2021

Predicting the development of clinically significant macular edema (CSME) in patients with mild nonproliferative diabetic retinopathy (NPDR) using machine learning (ML)

Dimitrios Damopoulos; Luís Mendes; Torcato Santos; Ales Neubert; Beatriz Garcia Armendariz; Robert Weikert; Daniela Ferrara; Jose G Cunha-Vaz; Fethallah Benmansour

Invest. Ophthalmol. Vis. Sci.. 2021; 62(8):2106.

ARVO Imaging in the Eye Conference Abstract July 2020

Development and External Validation of a Machine Learning Model for Predicting Response to anti-VEGF Treatment in Patients with neovascular AMD

Ian Lloyd Jones; Andreas Maunz; Thomas Albrecht; Huanxiang Lu; Yvonna Li; Fethallah Benmansour; Jayashree Nair Sahni; Martin Gliem

Invest. Ophthalmol. Vis. Sci.. 2020; 61(9):PP007.

ARVO Annual Meeting Abstract June 2020

Machine Learning to Predict Diabetic Retinopathy Improvement in Mild NPDR Patients From EUROCONDOR and C-Tracer Using Systemic and Retinal Imaging Features

Dimitrios Damopoulos; Luis Mendes; Beatriz Garcia Armendariz; Torcato Santos; Zdenka Haskova; Robert Weikert; Fethallah Benmansour; Jose G Cunha-Vaz

Invest. Ophthalmol. Vis. Sci.. 2020; 61(7):4036.

ARVO Annual Meeting Abstract June 2020

Diagnostic accuracy of a machine-learning algorithm to detect and classify choroidal neovascularization based on SD-OCT in neovascular age-related macular degeneration (nAMD)

Andreas Maunz; Fethallah Benmansour; Yun Li; Thomas Albrecht; Yan-Ping Zhang; Filippo Arcadu; Yalin Zheng; Savita Madhusudhan; Jayashree Sahni

Invest. Ophthalmol. Vis. Sci.. 2020; 61(7):2649.

ARVO Imaging in the Eye Conference Abstract August 2019

Deep learning algorithm to predict diabetic retinopathy (DR) progression on the individual patient level

Filippo Arcadu; Fethallah Benmansour; Andreas Maunz; Jeffrey Willis; Marco Prunotto; Zdenka Haskova

Invest. Ophthalmol. Vis. Sci.. 2019; 60(11):PB093.

ARVO Imaging in the Eye Conference Abstract August 2019

A machine learning approach to predict response to anti-VEGF treatment in patients with neovascular age-related macular degeneration using SD-OCT

Jayashree Nair Sahni; Andreas Maunz; Filippo Arcadu; Yan-Ping Zhang_Schaerer; Yvonna Li; Thomas Albrecht; Andreas Thalhammer; Fethallah Benmansour

Invest. Ophthalmol. Vis. Sci.. 2019; 60(11):PB094.

ARVO Annual Meeting Abstract July 2019

Deep learning predicts OCT measures of diabetic macular thickening from color fundus photographs

Jeffrey Ryuta Willis; Filippo Arcadu; Fethallah Benmansour; Andreas Maunz; John Michon; Zdenka Haskova; Dana McClintock; Anthony P Adamis; Marco Prunotto

Invest. Ophthalmol. Vis. Sci.. 2019; 60(9):1538.

ARVO Annual Meeting Abstract July 2019

Deep learning algorithm for patient-level prediction of diabetic retinopathy (DR) response to vascular endothelial growth factor (VEGF) inhibition

Filippo Arcadu; Fethallah Benmansour; Andreas Maunz; Jeffrey Ryuta Willis; Marco Prunotto; Zdenka Haskova

Invest. Ophthalmol. Vis. Sci.. 2019; 60(9):2806.

Retina March 2019

Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs

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Filippo Arcadu; Fethallah Benmansour; Andreas Maunz; John Michon; Zdenka Haskova; Dana McClintock; Anthony P. Adamis; Jeffrey R. Willis; Marco Prunotto
TAGS: color, deep learning, diabetes mellitus, diabetic macular edema, fundus photography, datasets
Invest. Ophthalmol. Vis. Sci.. 2019; 60(4):852-857. https://doi.org/10.1167/iovs.18-25634

Includes: Supplemental Content

ARVO Annual Meeting Abstract September 2016

Quantitative assessment of vessel regression and pericyte detachment in a mouse model of retinal angiogenesis

Sabine Uhles; Franco Revelant; Sabine Grüner; Guido Hartmann; Fethallah Benmansour

Invest. Ophthalmol. Vis. Sci.. 2016; 57(12):4534.

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Deep learning segmentation of foveal avascular zone (FAZ) in optical coherence tomography angiography (OCTA) of nonproliferative diabetic retinopathy

Deep learning (DL) to segment retinal layer disruption on optical coherence tomography (OCT) in neovascular age-related macular degeneration (nAMD)

Prediction of proliferative diabetic retinopathy (PDR) from nonproliferative diabetic retinopathy (NPDR) using real-world (RW) electronic health record (EHR) data

Deep learning (DL) model to identify moderately severe and severe nonproliferative diabetic retinopathy (NPDR) from 7-field color fundus photographs (7F-CFP)

Detecting patients with ocular comorbidities when screening for diabetic retinopathy (DR): an out-of-distribution (OOD) perspective

Predicting 5-year progression to vision-threatening complications using machine learning

A unified deep learning approach for OCT segmentation from different devices and retinal diseases

Prediction of ranibizumab injection frequency in patients with macular edema due to retinal vein occlusion during as-needed dosing period of BRAVO and CRUISE Phase 3 trials

Automated screening of moderately severe and severe nonproliferative diabetic retinopathy (NPDR) from 7-field color fundus photographs (7F-CFP) using deep learning (DL)

Predicting the development of clinically significant macular edema (CSME) in patients with mild nonproliferative diabetic retinopathy (NPDR) using machine learning (ML)

Development and External Validation of a Machine Learning Model for Predicting Response to anti-VEGF Treatment in Patients with neovascular AMD

Machine Learning to Predict Diabetic Retinopathy Improvement in Mild NPDR Patients From EUROCONDOR and C-Tracer Using Systemic and Retinal Imaging Features

Diagnostic accuracy of a machine-learning algorithm to detect and classify choroidal neovascularization based on SD-OCT in neovascular age-related macular degeneration (nAMD)

Deep learning algorithm to predict diabetic retinopathy (DR) progression on the individual patient level

A machine learning approach to predict response to anti-VEGF treatment in patients with neovascular age-related macular degeneration using SD-OCT

Deep learning predicts OCT measures of diabetic macular thickening from color fundus photographs

Deep learning algorithm for patient-level prediction of diabetic retinopathy (DR) response to vascular endothelial growth factor (VEGF) inhibition

Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs

Quantitative assessment of vessel regression and pericyte detachment in a mouse model of retinal angiogenesis

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