AEYE-DS Data Show 99% Imageability in Eye Screening

Tel Aviv, Israel, September 9, 2026

Clinical Studies Evaluate Autonomous Retinopathy Screening

The clinical evidence was published in peer-reviewed ophthalmology literature and examined AEYE-DS across different retinal imaging configurations. The technology uses artificial intelligence to analyze retinal photographs and determine whether a patient has signs of more-than-mild diabetic retinopathy, allowing screening to be performed without requiring an ophthalmologist or trained retinal specialist to review every image. A key finding reported from the studies was 99% imageability, indicating that the system was able to obtain analyzable retinal images in nearly all evaluated eyes. The results also support a simplified one-image-per-eye workflow, potentially reducing the number of photographs required to complete an automated screening examination. This may be particularly relevant in primary-care environments, where screening systems need to operate efficiently without adding significant complexity to routine patient visits. The technology is designed to work with both portable and tabletop fundus cameras, potentially allowing healthcare providers to select imaging equipment according to the clinical environment. Portable systems may support screening in locations with limited access to specialized ophthalmic infrastructure, while tabletop systems can be integrated into established clinical workflows.

AI Technology Demonstrates Diagnostic Performance

Diabetic retinopathy is a major complication of diabetes and can cause irreversible vision loss if clinically significant disease is not detected and treated appropriately. Because early-stage disease may not produce noticeable symptoms, regular retinal screening is an important component of diabetes management. However, access to specialist eye-care services can be limited in many healthcare systems. AEYE-DS is designed to automate the screening and diagnostic decision-making process after retinal images are acquired. The system evaluates the images using artificial intelligence and provides an automated assessment intended to identify patients who may require further ophthalmic evaluation. The newly published clinical findings report strong diagnostic efficacy alongside the high imageability rate. AEYE described the results as demonstrating best-in-class diagnostic efficacy, although such comparative claims should be understood in the context of the specific study populations, cameras, endpoints and comparator systems used in the respective analyses. The reported one-image-per-eye approach is another potentially important operational advantage. Traditional retinal photography can require multiple images of each eye to obtain sufficient coverage and image quality. A workflow requiring fewer images could help reduce examination time and simplify screening for healthcare workers who are not specialists in ophthalmic imaging.

Technology Could Expand Access to Screening

The clinical findings could support broader use of autonomous diabetic retinopathy screening in primary-care practices, diabetes clinics and other healthcare settings where specialist ophthalmology resources may not be immediately available. Automated screening technologies may help identify patients requiring referral while reducing some of the workload associated with manual image interpretation. AEYE’s focus on compatibility with both portable and tabletop imaging devices also addresses different deployment environments. Portable retinal cameras can potentially support screening programs outside conventional eye clinics, while tabletop systems can be used in established medical practices. The published evidence represents a clinical validation milestone for AI-enabled ophthalmic screening, but the findings should not be interpreted as meaning that AI replaces ophthalmologists in all aspects of diabetic eye care. Patients identified as having potentially significant disease still require appropriate clinical evaluation and management by qualified healthcare professionals. As diabetic populations continue to grow globally, technologies capable of delivering accessible and efficient retinal screening could play an important role in earlier detection of vision-threatening disease. The reported 99% imageability and simplified one-image-per-eye workflow provide additional evidence supporting the potential utility of AEYE-DS across different retinal imaging platforms. The findings strengthen AEYE’s position in the emerging field of autonomous medical AI and demonstrate how clinically validated software can potentially expand access to screening while integrating into existing healthcare workflows.

Source: AEYE press release