Eyetelligence Assure & Assure+
AI-enabled software for eye disease screening.
Eyetelligence is a software as a medical device (SaMD) that uses clinically validated AI algorithms to analyse digital retinal images. So, you’re uniquely placed to detect the early features of glaucoma, diabetic retinopathy (DR), neovascular age-related macular degeneration (nAMD), as well as screening for cardiovascular disease (CVD) risks. Its AI sets new benchmarks in affordable disease screening tools, while its evidence-based reporting empowers clinicians to make informed decisions with speed, accuracy, and confidence.
Trained on over 1,000,000 retinal images for an accuracy of 95%.1,2,3
Eyetelligence software has been trained to analyse images on over 1,000,000 retinal scans for the early detection of major eye diseases with proven accuracy.
TGA and CE mark Class 1 Medical Device.
Eyetelligence Assure software is classified as a Class 1 medical device, with Australian TGA listing, and European CE Mark, conforming to the performance and safety standards in over 30 countries across the EU and Australasia.4
Backed by extensive peer-reviewed research.
Eyetelligence was developed by an international team of world-renowned experts in ophthalmology AI. Its underlying research has undergone thorough peer review and been published in top academic journals.
Manage serious diseases with peace of mind.
Empower your practice with Instant reports to facilitate diagnosis, treatment, and referrals. Share clinical evidence with patients for their peace of mind. Make fast and affordable evidence-based decisions for quality coordinated care.
Non-invasive early detection to minimise risk.
Eyetelligence makes it easy to understand the patient’s level of risk so that you can begin to manage their condition. All it requires is taking a retinal photo of the patient’s eyes.
Plug and play with most fundus cameras.
Eyetelligence software works seamlessly with most fundus cameras and connected PCs. Pair it with your existing camera or purchase it as a package with Optain’s Resolve Portable Automatic Fundus Camera.
Eyetelligence Assure
AI Software for Eye Disease Screening
Make decisions based on accurate analysis.
Detect the features of glaucoma, neovascular age-related macular degeneration and diabetic retinopathy with an accuracy of 95%.5,6,7
Boost the power of your fundus camera.
Seamless integrations empower clinicians with expert analysis based on digital fundus images to help support and make more informed choices.
Expert knowledge at your fingertips.
The AI was trained by a team of sub-specialist ophthalmologists, drawing on more than 1,000,000 retinal images to help you identify signs of serious eye diseases.
Greater patient understanding.
Help your patient’s comprehension of their clinical assessments with easy-to-understand, take-home reports and supporting information.
Eyetelligence Assure+
AI Software for Eye Diseases + Cardiovascular Assessment
32% of all global deaths are from CVD.8
We see a better future. Eyetelligence Assure+ adds another level to our revolutionary eye disease assessment capability. Clinically validated AI algorithms analyse the microvasculature within retinal images to generate heatmaps revealing patterns indicative of cardiovascular risk, resulting in non-invasive, rapid, and accurate detection for timely intervention.
Identifying indicators of CVD risk.
Research has enabled us to detect the presence of cardiovascular diseases by analysing the state of the microvasculature captured in the retinal fundus image. Our AI algorithm is trained on large population datasets to see the difference between the retinal photos with different CVD risk levels. Our software’s visualisation technology utilises vessel patterns in patient fundus images to develop heat maps indicating low, moderate, and high-risk potential for cardiovascular disease. Clinicians have the results in seconds.
Eyetelligence Assure suite of products is not available for sale in the US
References.
1 Zhixi Li, Yifan He, Stuart Keel, Wei Meng, Robert T. Chang, Mingguang He. (AUG 01, 2018). Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.
2 Zhixi Li, Stuart Keel,Chi Liu,Yifan He, Wei Meng, Jane Scheetz, Pei Ying Lee, Jonathan Shaw, Daniel Ting, Tien Wong, Hugh Taylor, Robert Chang, and Mingguang He. (OCT 01 2018). An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs. (https://doi.org/10.2337/dc18-0147)
3 Stuart Keel, Zhixi Li, Jane Scheetz, Liubov Robman, James Phung, Galina Makeyeva, KhinZaw Aung, Chi Liu, Xixi Yan, Wei Meng, Robyn Guymer, Robert Chang, Mingguang He. Clinical & Experimental Ophthalmology (19 June 2019). Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs. (https://doi.org/10.1111/ceo.13575)
4 For country-specific regulatory information, please contact your local Optain representative.
5 Zhixi Li, Yifan He, Stuart Keel, Wei Meng, Robert T. Chang, Mingguang He. (AUG 01, 2018). Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.
6 Zhixi Li, Stuart Keel,Chi Liu,Yifan He, Wei Meng, Jane Scheetz, Pei Ying Lee, Jonathan Shaw, Daniel Ting, Tien Wong, Hugh Taylor, Robert Chang, and Mingguang He. (OCT 01 2018). An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs. (https://doi.org/10.2337/dc18-0147)
7 Stuart Keel, Zhixi Li, Jane Scheetz, Liubov Robman, James Phung, Galina Makeyeva, KhinZaw Aung, Chi Liu, Xixi Yan, Wei Meng, Robyn Guymer, Robert Chang, Mingguang He. Clinical & Experimental Ophthalmology (19 June 2019). Development and validation of a deep-learning algorithm for the detection of neovascular age-related macular degeneration from colour fundus photographs. (https://doi.org/10.1111/ceo.13575)
8 Source: https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds)
