AI Solutions for Ophthalmology

Our aim is to provide AI decision support to prevent sight loss around the world.

Healthcare is changing, and AI, amongst other technological advancements, will soon revolutionise the way healthcare is delivered.

At Visulytix, we believe AI solutions must be delivered in partnership with those who know best about clinical care.

Patients' interests are at the heart of everything we do, and we have designed our solutions around four key principles:

Explainable & Granular Solution
Our solutions provide clear and detailed heat maps, visual aids and quality assurance with each output.
Improving Access for Patients
Our solutions are cloud based and can be accessed from any web browser, in a range of settings and environments.
Evidence-based
We have partnered with many institutions to provide a robust validation of our algorithms and software. We also make sure our work is constantly subject to audit and quality improvement.
The Gift of Time
Ease the burden of triage to ensure your specialist time is used where it is needed most.

State of Global Sight Loss

703 million people will suffer from sight loss by 2050 [1]
80% preventable with available knowledge and prompt interventions [2]
£6.3 trillion cost of avoidable blindness from 2011 to 2020 [3]

Our Mission

  • Detect sight-threatening conditions early
  • Provide quick & accurate decision support at low cost
  • Improve the patient journey

Pegasus

Our flagship solution, Pegasus, provides indicative information on the assessment of ophthalmic retinal imaging data, in seconds.

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Specialist-Level Accuracy

  • Reduce costly false-positives
  • Detects signs of pathologies

Expedite your screening process

  • Spend less time looking at images
  • Reach more patients

Augment your workflow

  • Patient-friendly visual display
  • Used in the clinic or in the field
  • Local
  • Cloud
  • Embedded
  • API
  • Web App

In the Media

2019

2018

2017

Publications

  1. Bhatia et al. Disease classification of macular Optical Coherence Tomography scans using deep learning software: validation on independent, multi-centre data Accepted for publication in Retina, July 2019.
  2. Rogers et al. Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study. Nature Eye, July 2019.
  3. Al-Aswad et al. Evaluation of a Deep Learning System for Identifying Glaucomatous Optic Neuropathy based on Colour Fundus Photographs. Journal of Glaucoma, June 2019.
  4. Goldberg et al. Artificial intelligence-based automated segmentation of subretinal fluid and subretinal pigment epithelial fluid in patients with chronic serous chorioretinopathy. ARVO, 2019.
  5. Chandrasekaran et al. Non-Enhanced vs Software Enhanced Images of the Optic Nerve Head and Nerve Fiber Layer in Tele glaucoma screening. ARVO 2019.
  6. Al-Aswad et al. Evaluation of the Pegasus Deep Learning System for identifying Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. ARVO, 2019.
  7. Ooms et al. Robotics and Artificial Intelligence in the Management of Vision Threatening Disease. ARVO 2019.
  8. Khouri et al. Artificial Intelligence Assisted Tele-Ocular Screening in Type I Diabetes Mellitus. ARVO 2019.
  9. Mendez et al. Screening for Diabetic Retinopathy and other Retinal Diseases: a Telemedicine Project in Mexico. ARVO 2019.
  10. Tranos et al. Evaluation of a deep learning system for diabetic retinopathy and age related macular degeneration screening. EURETINA, 2018.
  11. Chandrasekaran et al. Traditional vs. Cloud-Based Artificial Intelligence Grading for Glaucoma Images. American Academy of Ophthalmology, 2018.
  12. Seo et al. Automated Evaluation of Optic Disc Images for Manifest Glaucoma Detection Using a Deep-Learning, Neural Network-Based Algorithm. Invest. Ophthalmol. Vis. Sci. 2018;59(9):2080.
  13. Al-Aswad et al. Investigation of a Deep Learning System in Identifying Glaucomatous Optic Neuropathy Based on Color Disc Photos. American Academy of Ophthalmology, 2018.
  14. Caterfino et al. Integration of Artificial Intelligence and OpacitySuppressionTM Software in Tele-Retinal Screenings. 162 - A0571 2018.

Collaborators

Cardiff University
Columbia University
L-Università ta' Malta
Mailor
Massachusetts Eye and Ear
Rotterdam Ophthalmic Institute
Rutgers
Tele-Glaucoma

Contact

We are always looking for collaborators, so please don't hesitate to reach out to us.