Empower early diabetic retinopathy detection with the first FDA-cleared handheld AI fundus camera
Contact usDiabetic retinopathy is the leading cause of vision loss in working-age adults1. Approximately 38.4 million people
have diabetes (11.6% of the US population)2. Diabetic retinopathy is treatable, if detected early.
Join us today in closing the diabetic retinopathy eye screening gap!
The Optomed Aurora AEYE, AI-powered fundus camera revolutionizes diabetic eye screening by offering healthcare providers reliable, reimbursable, and rapid diabetic retinopathy screening results.
FDA-cleared and clinically proven, this portable solution integrates seamlessly into your workflow, delivering high-quality images and enabling eye screening anywhere. It bridges the gap in DR screening and drives significant improvements in patient outcomes and healthcare ratings6.
Read more
Requiring just one image per eye, without dilation, the Optomed Aurora AEYE is equipped with Smart Autofocus and Autoexposure functions facilitating easy image capture, ensuring efficiency in the screening process for any healthcare professional.
With immediate on-the-spot results, patients can receive timely feedback, facilitating prompt intervention if necessary.
Integration options with electronic medical records streamline data management, enhancing efficiency and accuracy in patient care.
Only one image per |
Results in less |
No startup costs and |
Reimbursable with |
The American Medical Association (AMA) supports diabetic eye screening in primary care with the help of artificial intelligence (AI)8.
Reimbursement (~$40 Medicare National Average) with 92229 CPT Code9.
Enhance HEDIS Quality Measure 117 utilizing a handheld fundus camera with AI technology for diabetic retinopathy detection10.
Join us today in closing the diabetic retinopathy eye screening gap!
Sources: 1. Kropp M, Golubnitschaja O, Mazurakova A, Koklesova L, Sargheini N, Vo TKS, de Clerck E, Polivka J Jr, Potuznik P, Polivka J, Stetkarova I, Kubatka P, Thumann G. Diabetic retinopathy as the leading cause of blindness and early predictor of cascading complications-risks and mitigation. EPMA J. 2023 Feb 13;14(1):21-42. doi: 10.1007/s13167-023-00314-8. PMID: 36866156; PMCID: PMC9971534. 2. https://www.cdc.gov/diabetes/data/statistics-report/index.html Accessed [18 April 2024] 3. Fathy C, Patel S, Sternberg P Jr, Kohanim S. Disparities in Adherence to Screening Guidelines for Diabetic Retinopathy in the United States: A Comprehensive Review and Guide for Future Directions. Semin Ophthalmol. 2016;31(4):364-77. doi: 10.3109/08820538.2016.1154170. Epub 2016 Apr 26. PMID: 27116205. 4. https://www.accessdata.fda.gov/cdrh_docs/pdf24/K240058.pdf 5. Schoenfeld ER, Greene JM, Wu SY, Leske MC. Patterns of adherence to diabetes vision care guidelines: Baseline findings from the Diabetic Retinopathy Awareness Program. Ophthalmology. 2001;108:563–571. 6. National Committee for Quality Assurance (NCQA) website. https://www.ncqa.org/hedis/measures/comprehensive-diabetes-care/ Accessed [18 April 2024] 7. Abràmoff MD, Roehrenbeck C, Trujillo S, et al. A reimbursement framework for artificial intelligence in healthcare. NPJ Digit Med. 2022;5(1):72. 8. Fairless E, Nwanyanwu K. Barriers to and Facilitators of Diabetic Retinopathy Screening Utilization in a High-Risk Population. J Racial Ethn Health Disparities. 2019;6(6):1244-1249 9. Abràmoff MD, Roehrenbeck C, Trujillo S, et al. A reimbursement framework for artificial intelligence in healthcare. NPJ Digit Med. 2022;5(1):72. 10. National Committee for Quality Assurance (NCQA) website. https://www.ncqa.org/blog/hedis-2022-see-whats-new-whats-changed-and-whats-retired/ Accessed [18 April 2024]
