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ARVO Annual Meeting Abstract June 2025

Screening of Referable Macular Pathologies in Retirement Communities using a Nonmydriatic Robotic OCT Fundus Camera

Anjali Saini; Terry Lee; Mustafa Iftikhar; Majda Hadziahmetovic; Lejla Vajzovic

Invest. Ophthalmol. Vis. Sci.. 2025; 66(8):3485.

ARVO Annual Meeting Abstract June 2025

Generative-AI for dye-free fluorescein angiography

Colyn Munn; Lyna Kamintsky; Yonatan Serlin; Sageev Oore; Marko Popovic; Rajeev Hemant Muni; Majda Hadziahmetovic; Oren Tomkins-Netzer; Jaime Levy; Alan Cruess; Corey A Smith; Alon Friedman

Invest. Ophthalmol. Vis. Sci.. 2025; 66(8):865.

ARVO Annual Meeting Abstract June 2025

A Systematic Review and Meta-Analysis: Treatment Patterns and Clinical Outcomes for Diabetic Macular Edema

Christina Nadia Bobrek; Ishani Kapoor; Swara Sarvepalli; Elise Timtim; Majda Hadziahmetovic

Invest. Ophthalmol. Vis. Sci.. 2025; 66(8):3790.

ARVO Annual Meeting Abstract June 2025

Title: Preliminary Safety and Efficacy of OCU410 for Treatment of Geographic Atrophy: Phase 1/2 OCU410: The Age-related Macular Degeneration (ArMaDa) Study update.

Syed Mahmood Shah; Shailesh Gupta; Benjamin Bakall; Lejla Vajzovic; Majda Hadziahmetovic; Yoshihiro Yonekawa; Raj K Maturi; Kevin Binder; Michael J Borne; Venkata R M Chavali; Sanjay Mitter; Roshan George; Alex Berezow; Huma Qamar

Invest. Ophthalmol. Vis. Sci.. 2025; 66(8):3675.

ARVO Annual Meeting Abstract June 2024

RETICAD: A tool for generating dye-free fluorescein angiography and quantifying retinal disease activity

Colyn Munn; Lyna Kamintsky; Yonatan Serlin; Majda Hadziahmetovic; Jaime Levy; Oren Tomkins-Netzer; Alon Friedman; Alan Cruess

Invest. Ophthalmol. Vis. Sci.. 2024; 65(7):3758.

ARVO Annual Meeting Abstract June 2024

Binary Classification of OCT Images using the Retina Foundation Model on Limited Data

David Kuo; Qitong Gao; Miroslav Pajic; Majda Hadziahmetovic

Invest. Ophthalmol. Vis. Sci.. 2024; 65(7):1616.

ARVO Annual Meeting Abstract June 2024

Evaluating Performance of ResNet-50 and RETFound Image Classification Algorithms on Ultra-widefield Fundus Images

Nikhil Varma Gadiraju; David Kuo; Vahid Ownagh; Terry Lee; Lindsey A Chew; Grant A. Justin; Nita Valikodath; Stephanie Hsu; Miroslav Pajic; Majda Hadziahmetovic; Lejla Vajzovic

Invest. Ophthalmol. Vis. Sci.. 2024; 65(7):1605.

ARVO Annual Meeting Abstract June 2024

Biomarkers Associated with Survival in Ocular Melanoma

Majda Hadziahmetovic; Swara Sarvepalli; Beth Bailey; Ishani Kapoor

Invest. Ophthalmol. Vis. Sci.. 2024; 65(7):2434.

ARVO Annual Meeting Abstract June 2024

Improving laser targeting accuracy with Augmented Reality guidance in retinal laser therapy

Sangjun (Sarah) Eom; Miroslav Pajic; Maria Gorlatova; Majda Hadziahmetovic

Invest. Ophthalmol. Vis. Sci.. 2024; 65(7):793.

ARVO Annual Meeting Abstract June 2024

Clinical Results of Therapeutic Interventions for Autoimmune Retinopathies

Ishani Kapoor; Swara Sarvepalli; Majda Hadziahmetovic

Invest. Ophthalmol. Vis. Sci.. 2024; 65(7):4420.

ARVO Annual Meeting Abstract June 2023

Augmented reality for retinal laser therapy

Sangjun (Sarah) Eom; Miroslav Pajic; Maria Gorlatova; Majda Hadziahmetovic

Invest. Ophthalmol. Vis. Sci.. 2023; 64(8):216.

ARVO Annual Meeting Abstract June 2023

Deep learning for longitudinal OCT retinal layer thickness and AMD progression prediction

Qitong Gao; Joshua Amason; Jay Rathinavelu; Miroslav Pajic; Majda Hadziahmetovic

Invest. Ophthalmol. Vis. Sci.. 2023; 64(8):308.

ARVO Annual Meeting Abstract June 2022

Exploring Deep-Learning on SD-OCT images as an Effective Mitigation Technique for Hydroxychloroquine-Induced Retinal Toxicity

Harsh Bandhey; Amanda Del Risco; Qitong Gao; Jay Rathinavelu; Miroslav Pajic; Majda Hadziahmetovic

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):3330 – F0139.

ARVO Annual Meeting Abstract June 2022

An end-to-end deep learning method for predicting retinal layer thickness from Optical Coherence Tomography (OCT) images in patients with clinical progression of Age-related Macular Degeneration (AMD)

Qitong Gao; Joshua Amason; Amanda Del Risco; Praruj Pant; Terry Lee; Justin Ma; Jennifer Minjia Chang-Wolf; Jay Rathinavelu; Aditya Kotla; Miroslav Pajic; Majda Hadziahmetovic

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):3008 – F0278.

ARVO Annual Meeting Abstract June 2022

An EHR-Based Predictive Model to Understand Lost-to-Follow-Up Risk in Patients with Diabetic Retinopathy

Durga S Borkar; Chun Xu; Majda Hadziahmetovic; Benjamin Goldstein

Invest. Ophthalmol. Vis. Sci.. 2022; 63(7):2820 – A0150.

ARVO Annual Meeting Abstract June 2021

Deep learning approach for automated detection of retinal pathology from ultra-widefield retinal images

Mingzhe Hu; Joshua Amason; Terry Lee; Qitong Gao; Durga Borkar; Miroslav Pajic; Majda Hadziahmetovic

Invest. Ophthalmol. Vis. Sci.. 2021; 62(8):2129.

ARVO Annual Meeting Abstract June 2021

A deep learning-based automated tool for the identification of referable retinal pathology from multi-modal imaging sources

Qitong Gao; Joshua Amason; Scott Cousins; Miroslav Pajic; Majda Hadziahmetovic

Invest. Ophthalmol. Vis. Sci.. 2021; 62(8):2128.

ARVO Annual Meeting Abstract June 2021

Remote diagnosis of referable retinal pathology in diabetic patients visiting primary care clinic

Majda Hadziahmetovic; Joshua Amason; Terry Lee; Scott Cousins

Invest. Ophthalmol. Vis. Sci.. 2021; 62(8):1147.

Articles May 2021

Automated Identification of Referable Retinal Pathology in Teleophthalmology Setting

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Qitong Gao; Joshua Amason; Scott Cousins; Miroslav Pajic; Majda Hadziahmetovic
TAGS: datasets, pathology, training, diagnostic imaging
Trans. Vis. Sci. Tech.. 2021; 10(6):30. https://doi.org/10.1167/tvst.10.6.30

Includes: Supplemental Content

Special Issue June 2020

Automated Recognition of Retinal Pigment Epithelium Cells on Limited Training Samples Using Neural Networks

PDF

Qitong Gao; Ying Xu; Joshua Amason; Anna Loksztejn; Scott Cousins; Miroslav Pajic; Majda Hadziahmetovic
TAGS: retinal pigment cell, retinal pigment epithelium, training, cell culture techniques, datasets, mice, choroid
Trans. Vis. Sci. Tech.. 2020; 9(2):31. https://doi.org/10.1167/tvst.9.2.31

Includes: Supplemental Content

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Screening of Referable Macular Pathologies in Retirement Communities using a Nonmydriatic Robotic OCT Fundus Camera

Generative-AI for dye-free fluorescein angiography

A Systematic Review and Meta-Analysis: Treatment Patterns and Clinical Outcomes for Diabetic Macular Edema

Title: Preliminary Safety and Efficacy of OCU410 for Treatment of Geographic Atrophy: Phase 1/2 OCU410: The Age-related Macular Degeneration (ArMaDa) Study update.

RETICAD: A tool for generating dye-free fluorescein angiography and quantifying retinal disease activity

Binary Classification of OCT Images using the Retina Foundation Model on Limited Data

Evaluating Performance of ResNet-50 and RETFound Image Classification Algorithms on Ultra-widefield Fundus Images

Biomarkers Associated with Survival in Ocular Melanoma

Improving laser targeting accuracy with Augmented Reality guidance in retinal laser therapy

Clinical Results of Therapeutic Interventions for Autoimmune Retinopathies

Augmented reality for retinal laser therapy

Deep learning for longitudinal OCT retinal layer thickness and AMD progression prediction

Exploring Deep-Learning on SD-OCT images as an Effective Mitigation Technique for Hydroxychloroquine-Induced Retinal Toxicity

An end-to-end deep learning method for predicting retinal layer thickness from Optical Coherence Tomography (OCT) images in patients with clinical progression of Age-related Macular Degeneration (AMD)

An EHR-Based Predictive Model to Understand Lost-to-Follow-Up Risk in Patients with Diabetic Retinopathy

Deep learning approach for automated detection of retinal pathology from ultra-widefield retinal images

A deep learning-based automated tool for the identification of referable retinal pathology from multi-modal imaging sources

Remote diagnosis of referable retinal pathology in diabetic patients visiting primary care clinic

Automated Identification of Referable Retinal Pathology in Teleophthalmology Setting

Automated Recognition of Retinal Pigment Epithelium Cells on Limited Training Samples Using Neural Networks

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