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ARVO Imaging in the Eye Conference Abstract July 2020

Clustering Spectral-Domain Optical Coherence Tomography Images using a Deep Variational Auto-encoder

Eduardo Mariottoni; Samuel I Berchuck; Felipe Medeiros

Invest. Ophthalmol. Vis. Sci.. 2020; 61(9):PB00145. doi:

Special Issue July 2020

A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression

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Atalie C. Thompson; Alessandro A. Jammal; Felipe A. Medeiros
TAGS: deep learning, glaucoma, screening
Trans. Vis. Sci. Tech.. 2020; 9(2):42. doi: https://doi.org/10.1167/tvst.9.2.42

ARVO Annual Meeting Abstract June 2020

Visual Crowding in Glaucoma: Structural and Functional Relationships

James Stringham; Alessandro A Jammal; Eduardo Bicalho Mariottoni; Tais Estrela; Carla Urata; Leonardo Shigueoka; Nara Ogata; Henry Tseng; Felipe Medeiros

Invest. Ophthalmol. Vis. Sci.. 2020; 61(7):3214. doi:

ARVO Annual Meeting Abstract June 2020

Comparison of the performance of a Machine-to-Machine (M2M) deep learning algorithm versus human graders for glaucoma screening in teleretinal fundus images

Delaram Mirzania; Atalie C Thompson; Eduardo Mariottoni; Leonardo Shigueoka; Kelly Muir; Felipe Medeiros

Invest. Ophthalmol. Vis. Sci.. 2020; 61(7):4545. doi:

ARVO Annual Meeting Abstract June 2020

A Deep Learning-Based Mapping of Structure to Function in Glaucoma

Eduardo Bicalho Mariottoni; Shounak Datta; David Dov; Alessandro Adad Jammal; Samuel Berchuck; Ivan Tavares; Lawrence Carin; Felipe Medeiros

Invest. Ophthalmol. Vis. Sci.. 2020; 61(7):3213. doi:

ARVO Annual Meeting Abstract June 2020

The relationship between concomitant mental health disorders and disease severity among glaucoma patients

Samuel Berchuck; Alessandro A Jammal; Eduardo Bicalho Mariottoni; Sayan Mukherjee; Felipe A Medeiros

Invest. Ophthalmol. Vis. Sci.. 2020; 61(7):1994. doi:

ARVO Annual Meeting Abstract June 2020

Effect of diabetes control on rates of visual field loss in patients with glaucoma.

Nicholas Johnson; Alessandro Adad Jammal; Felipe A Medeiros; Leonardo Shigueoka; Eduardo Mariottoni

Invest. Ophthalmol. Vis. Sci.. 2020; 61(7):3891. doi:

ARVO Annual Meeting Abstract June 2020

Predicting Rates of RNFL Loss from Analysis of Deep Optic Nerve Head Images

Manav Mayank Vakil; Eduardo Bicalho Mariottoni; Felipe A Medeiros

Invest. Ophthalmol. Vis. Sci.. 2020; 61(7):4535. doi:

ARVO Annual Meeting Abstract June 2020

From Machine to the Real World: Assessing the Accuracy of a Machine-to-Machine (M2M) Deep Learning Model to Detect Glaucoma during a Population-Based Screening Effort in Brazil

Tais Estrela; Eduardo Bicalho Mariottoni; Tabatta Graciolli; Nubia Chouchounova; Nara Ogata; Mariana Siqueira; Carla Urata; Leonardo Shigueoka; Alessandro A Jammal; Atalie C. Thompson; Felipe A Medeiros

Invest. Ophthalmol. Vis. Sci.. 2020; 61(7):4539. doi:

ARVO Annual Meeting Abstract June 2020

The Effect of Intraocular Pressure Control on Rates of Glaucomatous Structural Loss in a Large Clinical Population

Alessandro A Jammal; Eduardo Bicalho Mariottoni; Atalie C. Thompson; Samuel Berchuck; Tais Estrela; Leonardo Shigueoka; Felipe A Medeiros

Invest. Ophthalmol. Vis. Sci.. 2020; 61(7):5196. doi:

ARVO Annual Meeting Abstract June 2020

The association between race and longitudinal visual field variability

Brian Stagg; Eduardo Mariottoni; Samuel Berchuck; Alessandro A Jammal; Rachel Hess; Kensaku Kawamoto; Ben Haaland; Felipe A Medeiros

Invest. Ophthalmol. Vis. Sci.. 2020; 61(7):4045. doi:

ARVO Annual Meeting Abstract June 2020

Predicting Future Rates of Retinal Nerve Fiber Layer Loss from Deep Learning Assessment of Baseline Optic Disc Photographs

Atalie C. Thompson; Alessandro A Jammal; Eduardo Bicalho Mariottoni; Leonardo Shigueoka; Tais Estrela; Felipe A Medeiros

Invest. Ophthalmol. Vis. Sci.. 2020; 61(7):4533. doi:

Special Issue March 2020

Artificial Intelligence Mapping of Structure to Function in Glaucoma

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Eduardo B. Mariottoni; Shounak Datta; David Dov; Alessandro A. Jammal; Samuel I. Berchuck; Ivan M. Tavares; Lawrence Carin; Felipe A. Medeiros
TAGS: artificial intelligence, glaucoma, retinal nerve fiber layer, visual field defects, spectral domain-optical coherence tomography
Trans. Vis. Sci. Tech.. 2020; 9(2):19. doi: https://doi.org/10.1167/tvst.9.2.19

Includes: Supplemental Content

Glaucoma October 2019

Racial Differences in the Association of Anterior Lamina Cribrosa Surface Depth and Glaucoma Severity in the African Descent and Glaucoma Evaluation Study (ADAGES)

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Christopher A. Girkin; Massimo A. Fazio; Christopher Bowd; Felipe A. Medeiros; Robert N. Weinreb; Jeffrey M. Liebmann; James Proudfoot; Linda M. Zangwill; Akram Belghith
TAGS: cribriform plate, glaucoma, racial differences, intraocular pressure
Invest. Ophthalmol. Vis. Sci.. 2019; 60(13):4496-4502. doi: https://doi.org/10.1167/iovs.19-26645

ARVO Annual Meeting Abstract July 2019

Deep Learning Segmentation-Free Assessment of Retinal Nerve Fiber Layer in OCT Scans

Eduardo Bicalho Mariottoni; Alessandro A Jammal; Carla Urata; Atalie C. Thompson; Felipe A Medeiros

Invest. Ophthalmol. Vis. Sci.. 2019; 60(9):1508. doi:

ARVO Annual Meeting Abstract July 2019

A Deep Learning Algorithm can Predict Retinal Nerve Fiber Layer Thickness with Lower Long-term Variability than Conventional Automated Segmentation

Carla Urata; Eduardo Bicalho Mariottoni; Alessandro A Jammal; Atalie C. Thompson; Felipe A Medeiros

Invest. Ophthalmol. Vis. Sci.. 2019; 60(9):1517. doi:

ARVO Annual Meeting Abstract July 2019

The Relationship Between Machine Learning-Derived Binocular Patterns of Visual Field Loss and Patient-Reported Disability in Glaucoma

Nara Ogata; Alessandro A Jammal; Eduardo Bicalho Mariottoni; Carla Urata; Susan Wakil; Felipe A Medeiros

Invest. Ophthalmol. Vis. Sci.. 2019; 60(9):2440. doi:

ARVO Annual Meeting Abstract July 2019

Novel Macular Bayesian Deviation Map for Differentiation Between Healthy and Glaucoma eyes.

Linda M Zangwill; Felipe A Medeiros; Robert N Weinreb; Akram Belghith

Invest. Ophthalmol. Vis. Sci.. 2019; 60(9):5548. doi:

ARVO Annual Meeting Abstract July 2019

The Relationship between Asymmetries of Corneal Properties and Rates of Visual Field Progression in Glaucoma Patients

Tais Estrela; Alessandro A Jammal; Eduardo Bicalho Mariottoni; Carla Urata; Nara Ogata; Felipe A Medeiros

Invest. Ophthalmol. Vis. Sci.. 2019; 60(9):6199. doi:

ARVO Annual Meeting Abstract July 2019

Prediction of Corneal Thickness and Corneal Hysteresis from Deep Learning Analysis of Optic Nerve Head OCT Scans

Swarup Sai Swaminathan; Felipe A Medeiros

Invest. Ophthalmol. Vis. Sci.. 2019; 60(9):5561. doi:

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Clustering Spectral-Domain Optical Coherence Tomography Images using a Deep Variational Auto-encoder

A Review of Deep Learning for Screening, Diagnosis, and Detection of Glaucoma Progression

Visual Crowding in Glaucoma: Structural and Functional Relationships

Comparison of the performance of a Machine-to-Machine (M2M) deep learning algorithm versus human graders for glaucoma screening in teleretinal fundus images

A Deep Learning-Based Mapping of Structure to Function in Glaucoma

The relationship between concomitant mental health disorders and disease severity among glaucoma patients

Effect of diabetes control on rates of visual field loss in patients with glaucoma.

Predicting Rates of RNFL Loss from Analysis of Deep Optic Nerve Head Images

From Machine to the Real World: Assessing the Accuracy of a Machine-to-Machine (M2M) Deep Learning Model to Detect Glaucoma during a Population-Based Screening Effort in Brazil

The Effect of Intraocular Pressure Control on Rates of Glaucomatous Structural Loss in a Large Clinical Population

The association between race and longitudinal visual field variability

Predicting Future Rates of Retinal Nerve Fiber Layer Loss from Deep Learning Assessment of Baseline Optic Disc Photographs

Artificial Intelligence Mapping of Structure to Function in Glaucoma

Racial Differences in the Association of Anterior Lamina Cribrosa Surface Depth and Glaucoma Severity in the African Descent and Glaucoma Evaluation Study (ADAGES)

Deep Learning Segmentation-Free Assessment of Retinal Nerve Fiber Layer in OCT Scans

A Deep Learning Algorithm can Predict Retinal Nerve Fiber Layer Thickness with Lower Long-term Variability than Conventional Automated Segmentation

The Relationship Between Machine Learning-Derived Binocular Patterns of Visual Field Loss and Patient-Reported Disability in Glaucoma

Novel Macular Bayesian Deviation Map for Differentiation Between Healthy and Glaucoma eyes.

The Relationship between Asymmetries of Corneal Properties and Rates of Visual Field Progression in Glaucoma Patients

Prediction of Corneal Thickness and Corneal Hysteresis from Deep Learning Analysis of Optic Nerve Head OCT Scans

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