Rayner has launched the first episode of what it calls a groundbreaking podcast series to give clinicians a breakdown of the first peer-reviewed clinical paper on the RayOne Galaxy intraocular lens (IOL).
Peer2Peer Deep Dive is a clinical podcast created with AI technology, offering clinicians a rapid summary of the newly published findings in the Journal of Refractive Surgery.
Rayner said the podcast would help listeners understand the clinical implications of the paper in the time it takes for a short break.
It said Peer2Peer Deep Dive discussed both pre-clinical simulation and pooled clinical analysis from 10 international sites, offering a comprehensive view of the IOL’s performance.
The Galaxy lens’s unique spiral technology was engineered using a proprietary AI engine to create a continuous variance of power across the lens surface.
This approach aims to eliminate the light-splitting and abrupt power shifts seen with traditional diffractive optics.
Rayner said the data strongly supported the lens’s design goals.
“Pre-clinical simulations showed the Galaxy’s spiral design significantly reduced halo and glare size compared to a leading trifocal, a powerful finding that was later supported by patient reports in the clinical trial.
“The paper presents compelling data on the world’s first spiral IOL, positioning it as a potentially transformative solution to the long-standing compromise between achieving a full range of vision and minimising visual disturbances,” the company said.
The clinical analysis confirmed “excellent visual performance”, demonstrating an uninterrupted, smooth plateau of vision.
It said the lens appeared to deliver exceptional dysphotopsia profiles: 95.4% of patients reported no or only mild halos, and a remarkable 100% reported no or only mild glare.
The full Peer2Peer Deep Dive episode runs to just over 14 minutes.
Those keen to know more can tune in now to hear the full analysis. Listen to Peer2Peer Deep Dive: The First RayOne Galaxy Peer-Reviewed Data on YouTube: https://youtu.be/TU7xUXSlWDw
