Eyepacs, Inc.

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EyePACS, Inc. is a clinical services company that facilitates blindness prevention in medical clinics using telemedicine, digital imaging, and a simple to use, cost-effective, web-based program that effectively detects diabetic retinopathy. Dr. Jorge Cuadros and Dr. Wyatt Tellis met during their Biological and Medical Information Science doctoral program at UC San Francisco in 2000. Dr. Cuadros had been working on telemedicine-based eye care programs since 1994. At the same time, Dr. Tellis was working in radiology informatics. Together, they began development of EyePACS in 2001. They launched their first diabetic retinopathy screening program in 2003 in a small community clinic run by UCSF Fresno. In 2005, they partnered with California Health Care Foundation to build sustainable diabetic retinopathy screening programs in community clinics all over California. They launched a pilot program comprised of 13 sites in the Central Valley. They used EyePACS to upload images and the UC Berkeley clinical faculty interpreted the images. Due to the success of the pilot program, they were able to create more programs across the state. Today, over 600 organizations nationwide use EyePACS.

Company Details

Employees
19
Founded
-
Address
211 Chase Street, Santa Cruz,california 95060,united States
Industry
Hospitals And Health Care
NAICS
Health Care and Social Assistance
Social Assistance
Website
http://www.eyepacs.com
Keywords
help you implement a successful blindness prevention program..
HQ
Santa Cruz, California
Competitors
Topcon Healthcare, EarGenie Pty Ltd, Apellis Pharmaceuticals, Procyon Insurance Services LLC, Photon Commerce, California Primary Care Association, Altima Creative, BIOMEDIX GROUP, IRIS AI LABS, Private Family.
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News

A deep learning-based ADRPPA algorithm for the prediction of diabetic retinopathy progression - Nature

A deep learning-based ADRPPA algorithm for the prediction of diabetic retinopathy progression Nature

Efficiency and safety of automated label cleaning on multimodal retinal images - Nature

Efficiency and safety of automated label cleaning on multimodal retinal images Nature

Autonomous Screening for Diabetic Macular Edema Using Deep Learning Processing of Retinal Images - medRxiv

Autonomous Screening for Diabetic Macular Edema Using Deep Learning Processing of Retinal Images medRxiv

EyePACS Achieves 1,000,000 Patient Encounters for Diabetic Retinopathy Assessment - PR Newswire

EyePACS Achieves 1,000,000 Patient Encounters for Diabetic Retinopathy Assessment PR Newswire

A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study - The Lancet

A deep learning model for novel systemic biomarkers in photographs of the external eye: a retrospective study The Lancet

Diabetic Retinopathy Grading by Deep Graph Correlation Network on Retinal Images Without Manual Annotations - Frontiers

Diabetic Retinopathy Grading by Deep Graph Correlation Network on Retinal Images Without Manual Annotations Frontiers

A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric - BMC Medical Informatics and Decision Making

A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric BMC Medical Informatics and Decision Making

(PDF) A deep learning-based smartphone app for real-time detection of five stages of diabetic retinopathy - researchgate.net

(PDF) A deep learning-based smartphone app for real-time detection of five stages of diabetic retinopathy researchgate.net

Development and validation of a deep-learning model to predict 10-year ASCVD risk from retinal images using the UK Biobank and EyePACS 10K datasets - medRxiv

Development and validation of a deep-learning model to predict 10-year ASCVD risk from retinal images using the UK Biobank and EyePACS 10K datasets medRxiv

A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability - The Lancet

A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability The Lancet

iGWAS: image-based genome-wide association of self-supervised deep phenotyping of human medical images - medRxiv

iGWAS: image-based genome-wide association of self-supervised deep phenotyping of human medical images medRxiv

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