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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

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: https://doi.org/

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: https://doi.org/

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: https://doi.org/

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: https://doi.org/

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: https://doi.org/

ARVO Annual Meeting Abstract July 2019

A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss from Optic Disc Photographs

Atalie C. Thompson; Alessandro A Jammal; Felipe A Medeiros

Invest. Ophthalmol. Vis. Sci.. 2019; 60(9):5577. doi: https://doi.org/

ARVO Annual Meeting Abstract July 2019

Simulating glaucomatous visual fields using a variational autoencoder

Samuel I Berchuck; Eduardo Bicalho Mariottoni; Felipe A Medeiros

Invest. Ophthalmol. Vis. Sci.. 2019; 60(9):1993. doi: https://doi.org/

ARVO Annual Meeting Abstract July 2019

Detecting Retinal Nerve Fiber Layer Segmentation Errors on Spectral Domain-Optical Coherence Tomography with a Deep Learning Algorithm

Alessandro A Jammal; Atalie C. Thompson; Nara Ogata; Eduardo Bicalho Mariottoni; Carla Urata; Vital P Costa; Felipe A Medeiros

Invest. Ophthalmol. Vis. Sci.. 2019; 60(9):2209. doi: https://doi.org/

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: https://doi.org/

Glaucoma February 2019

Visual Crowding in Glaucoma

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Nara G. Ogata; Erwin R. Boer; Fábio B. Daga; Alessandro A. Jammal; James M. Stringham; Felipe A. Medeiros
TAGS: crowding, glaucoma, visual crowding
Invest. Ophthalmol. Vis. Sci.. 2019; 60(2):538-543. doi: https://doi.org/10.1167/iovs.18-25150

Glaucoma September 2018

Macular Pigment and Visual Function in Patients With Glaucoma: The San Diego Macular Pigment Study

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Fábio B. Daga; Nara G. Ogata; Felipe A. Medeiros; Rachel Moran; Jeffrey Morris; Linda M. Zangwill; Robert N. Weinreb; John M. Nolan
TAGS: glaucoma, macular pigment, vision, diet, carotenoids
Invest. Ophthalmol. Vis. Sci.. 2018; 59(11):4471-4476. doi: https://doi.org/10.1167/iovs.18-24170

Articles August 2018

Comparison of Visual Field Point-Wise Event-Based and Global Trend-Based Analysis for Detecting Glaucomatous Progression

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Zhichao Wu; Felipe A. Medeiros
TAGS: glaucoma, trend, visual fields, eye, specificity of diagnostic test
Trans. Vis. Sci. Tech.. 2018; 7(4):20. doi: https://doi.org/10.1167/tvst.7.4.20

ARVO Annual Meeting Abstract July 2018

A Structural and Functional Machine Learning Classifier Improves Prediction of Patient-Reported Disability in Glaucoma

Susan Wakil; Alessandro Adad Jammal; Nara Ogata; Felipe Medeiros

Invest. Ophthalmol. Vis. Sci.. 2018; 59(9):4985. doi: https://doi.org/

ARVO Annual Meeting Abstract July 2018

What amount of visual field loss is associated with visual impairment in glaucoma?

Alessandro Jammal; Nara Gravina Ogata; Fabio Bernardi Daga; Ricardo Y Abe; Vital P Costa; Felipe Medeiros

Invest. Ophthalmol. Vis. Sci.. 2018; 59(9):5101. doi: https://doi.org/

ARVO Annual Meeting Abstract July 2018

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

Nara Gravina Ogata; Alessandro Adad Jammal; Felipe Medeiros

Invest. Ophthalmol. Vis. Sci.. 2018; 59(9):5113. doi: https://doi.org/

ARVO Annual Meeting Abstract July 2018

Using a Novel Anterior Segment Optical Coherence Tomography (AS-OCT) Method to Assess Iridocorneal Angle

Mariana Nunez; Michelle Y. Chen; Susan S Lee; Chyong Nien; Felipe Medeiros; Jeffrey L Goldberg

Invest. Ophthalmol. Vis. Sci.. 2018; 59(9):5905. doi: https://doi.org/

ARVO Annual Meeting Abstract July 2018

Visual Field Progression in Eyes with Seemingly Well-Controlled Intraocular Pressure

Bianca Nicolela Nicolela Susanna; Nara Ogata; Alessandro Adad Jammal; Felipe Medeiros

Invest. Ophthalmol. Vis. Sci.. 2018; 59(9):1996. doi: https://doi.org/

ARVO Annual Meeting Abstract July 2018

Why do Some Glaucomatous Eyes Lose a Lot More Nerve Tissue than Others by the Time of First Appearance of a Visual Field Defect?

Courtney Lynne Ondeck; Alessandro Adad Jammal; Nara Ogata; Felipe Medeiros

Invest. Ophthalmol. Vis. Sci.. 2018; 59(9):4062. doi: https://doi.org/

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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)

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

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

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

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

A Deep Learning Algorithm to Quantify Neuroretinal Rim Loss from Optic Disc Photographs

Simulating glaucomatous visual fields using a variational autoencoder

Detecting Retinal Nerve Fiber Layer Segmentation Errors on Spectral Domain-Optical Coherence Tomography with a Deep Learning Algorithm

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

Visual Crowding in Glaucoma

Macular Pigment and Visual Function in Patients With Glaucoma: The San Diego Macular Pigment Study

Comparison of Visual Field Point-Wise Event-Based and Global Trend-Based Analysis for Detecting Glaucomatous Progression

A Structural and Functional Machine Learning Classifier Improves Prediction of Patient-Reported Disability in Glaucoma

What amount of visual field loss is associated with visual impairment in glaucoma?

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

Using a Novel Anterior Segment Optical Coherence Tomography (AS-OCT) Method to Assess Iridocorneal Angle

Visual Field Progression in Eyes with Seemingly Well-Controlled Intraocular Pressure

Why do Some Glaucomatous Eyes Lose a Lot More Nerve Tissue than Others by the Time of First Appearance of a Visual Field Defect?

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