MDR class IIa approved


CE (0344) certified


ISO 13485 certified


ISO 13485 certified


ISO 27001 certified


MDR class IIa approved


TGA approved


MHRA approved


Business & integration partners

Research partners

Next level eye care diagnostics


With global costs of vision loss estimated to be nearly $3 trillion and up to 80% cases of retinal diseases causing visual impairment or blindness that can often be prevented if detected timely, artificial intelligence opens up a whole new possibilities in eye care. Imaging is non-invasive and reliable screening method but requires expertise to analyse/read the acquired images. Using artificial intelligence, this can be done quickly, yet accurately.

RetCAD™ uses state-of-the-art deep learning and computer vision technology utilizing a large amount of retinal images in order to provide precisely quantified clinical evidence. The software assists eye care specialists in early diagnosis and grading of vision threatening diseases, such as Age-related Macular Degeneration (AMD) and Diabetic Retinopathy (DR). Simplified visualization of the scan analysis with heat maps help improve patient engagement and prevent severe eyesight problems and blindness.

  • Consistent performance with better patient care

  • Minimized screening and diagnostic lead times

  • Output conform international standards such as ICDR and AREDS

  • Customized integration to user specific workflows

  • Standardized reporting with a simple graphic representation

Applications


Large national screening programs and research

Helping more patients faster

With use of artificial intelligence healthcare organisations can perform mass examinations on a large scale with very accurate results, on pair or even surpassing the performance of traditional vision screening methods.

Having every patient scan instantly analyzed with a clinically validated software system trained to detect typical disease patterns, RetCAD™ allows for screening big populations fast while providing highly reliable results.

Optical retail and digital healthcare services

Instant detection of early symptoms

Detection for Diabetic Retinopathy and Age-related Macular Degeneration can be now performed by opticians using artificial intelligence to help spot early signs of the diseases and save diagnostics costs.

RetCAD™ software integrated with a fundus camera ensures reliable and consistent performance, irrespective of the reader, workload or time of day. Disease symptoms detection takes only few minutes and provides validated results instantly.

Medical centers and eye care specialists

Fast and effective patient triaging

RetCAD™ software can be customized to allow optimal integration in the clinic-specific workflow, in order to reduce workload for certified eye care specialists and to enhance clinical care capacity.

The ability to prioritize faster and objectively which patients need immediate follow-up, opens truly whole new possibilities in eye care. Ophthalmologists can now help more patients quicker by identifying severe symptoms faster.

How it works

With today’s digital color fundus cameras, one or multiple images can be acquired from a patient within seconds and are presented on the operator’s computer screen. RetCAD™ will automatically start analysing any acquired color fundus image and provide the user with a score and heat map that indicate referable disease symptoms.

  • RetCAD™ is GDPR compliant and analyzes pseudo-anonymized data to make sure patient privacy is preserved.

  • Processing/analysis on the Thirona servers with RetCAD™ AI takes approximately 1 minute.

  • Results are returned and imported to client application to be accessed directly in the application.

  • The intuitive visual format of the report allows quick and easy interpretation of the results by the eye care specialist.

Download the white paper to learn more on the general principles of the software and the performance in comparison to human experts, on various datasets.

Watch the demo


Watch the demo video on how to use RetCAD to analyse retina images with a fundus camera.

Should you require more information or a more detailed demonstration of RetCAD capabilities, our experts will be happy to assist you.

Papers & Publications


Learn more about the performance of RetCAD™ through below validation studies.

  • Diabetic retinopathy screening with artificial intelligence. Preliminary experience in Italian Healthcare System | Oct 2023 | Euretina
  • Performance of an artificial intelligence automated system for diabetic eye screening in a large English population | Feb 2023 | Diabetic Medicine Journal
  • Simultaneous screening and classification of diabetic retinopathy and age-related macular degeneration based on fundus photos—a prospective analysis of the RetCAD system | Dec 2022 | International Journal of Ophthalmology
  • Performance of a deep learning system for detection of referable diabetic retinopathy in real clinical settings | May 2022 | Arxiv
  • Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration | Jun 2020 | Acta Ophthalmologica
  • Automated grading of fundus photographs to identify referable AMD for first-line eye care | Jul 2019 | Investigative Ophthalmology and Visual Science
  • Genetic risk score has added value over initial clinical grading stage in predicting disease progression in age-related macular degeneration | Apr 2019 | Nature Scientific reports
  • Diabetic retinopathy screening with artificial intelligence. Preliminary experience in Italian Healthcare System | Oct 2023 | Euretina

  • Performance of an artificial intelligence automated system for diabetic eye screening in a large English population | Feb 2023 | Diabetic Medicine Journal

  • Simultaneous screening and classification of diabetic retinopathy and age-related macular degeneration based on fundus photos—a prospective analysis of the RetCAD system | Dec 2022 | International Journal of Ophthalmology

  • Performance of a deep learning system for detection of referable diabetic retinopathy in real clinical settings | May 2022 | Arxiv

  • Evaluation of a deep learning system for the joint automated detection of diabetic retinopathy and age-related macular degeneration | Jun 2020 | Acta Ophthalmologica

  • Automated grading of fundus photographs to identify referable AMD for first-line eye care | Jul 2019 | Investigative Ophthalmology and Visual Science

  • Genetic risk score has added value over initial clinical grading stage in predicting disease progression in age-related macular degeneration | Apr 2019 | Nature Scientific reports

Interested in integrating RetCAD in your screening services?


Contact us to find out more about distribution partnerships!

Diederik Sakkers

Business development manager

Diederik Sakkers
Business development manager