Let AI analyze your retina scans

Optical coherence tomography (OCT) has transformed eye care diagnostics, providing clinicians with unprecedented insights into the intricate structures of the retina. Its non-invasive nature and ability to detect microscopic changes at the earliest stages of disease have made it an indispensable tool in modern ophthalmology and optometry.

However, OCT’s full potential is often hindered by eye care specialists’ challenges in interpreting the vast amount of data it generates. One survey of over 1,000 eye care professionals revealed that many struggle with confidence in their interpretation skills, leading to over-referral of patients to specialists or even avoiding OCT altogether. This impacts practice efficiency and delays timely diagnosis and treatment for patients in the field, where waiting time may result in irreversible blindness.

Furthermore, the time-consuming nature of manual OCT analysis poses a significant challenge. While a machine can capture thousands of high-resolution scans in mere seconds, a clinician’s subsequent interpretation of OCT is far more time-consuming. They must meticulously analyze each scan, not only for any signs of pathology but also in the patient’s complete medical history. Clinicians may spend up to 40 minutes per scan meticulously examining each layer for subtle signs of pathology. This strains resources and increases the risk of human error, as even experienced practitioners can miss minor or rare pathologies.

The good news is that AI-powered solutions are revolutionizing OCT interpretation, offering a range of benefits that address these pain points. AI algorithms can rapidly analyze OCT scans, flag potential abnormalities, instantly segment scans based on severity, provide detailed reports for practitioners and patients and streamline workflows. This allows eye care specialists to focus on more complex cases, prioritize patient care, and see more patients daily.

Moreover, AI’s ability to detect subtle or early-stage pathologies that may be missed by the human eye is a game-changer. This significantly improves diagnostic accuracy, enabling early intervention and potentially saving patients from vision loss. 

Another significant level-up of diagnostics with AI is early glaucoma assessment. Current tests often rely on observing changes over time, delaying treatment assessment and hindering early identification of rapid disease progression.

OCT is possible to detect microscopic damage to ganglion cells and thinning across these layers before changes are noticeable through other tests. But in the standard built-in analysis systems of OCT devices, this feature is not working to its true potential, as it checks the scan they made against normative databases that are limited and represent an average of a select group of people, potentially missing early glaucoma development in those who deviate from the “norm.” Conversely, individuals may be unnecessarily referred for treatment due to not fitting the “normal” profile, even if their eyes are healthy.

In addition to improving diagnostic accuracy and efficiency, AI enhances patient education and engagement. OCT reports by AI-powered platforms can translate complex medical data into clear, visual formats that patients can understand and monitor their treatment progress. This empowers patients to actively participate in their eye care actively, leading to better long-term outcomes.

While AI is not a magic bullet, it is a powerful tool that can transform the future of optometry and ophthalmology. By embracing AI-assisted tools, eye care specialists can overcome the limitations of traditional methods, improve diagnostic accuracy, optimize workflows, and ultimately provide better care for their patients.