Introducing our AI-Powered platform for ophthalmic image annotation

Pittsburgh, PA, USA - 2nd May, 2025

Hi from the NetraMind team,

We were thrilled by the positive response to last month's newsletter— thank you! This month, we're diving deeper into one of our tools that's been quietly generating unexpected buzz across the ophthalmic imaging space.

It may not be for everyone, but if you're working with image annotations or exploring Al in your workflow, this might be worth a closer look.

We'd love to hear your thoughts — and if there are other topics you'd like us to explore in the future, just reply and let us know.

Thanks for being such a valued supporter and part of the NetraMind journey.

-------- Our Al-Powered Image Annotation Tool --------

At NetraMind Innovations, we're bringing a new level of precision and efficiency to ophthalmic imaging with our Al-powered annotation tool.

Designed specifically for image reading centers, this platform improves labeling quality, enables consistent segmentation, and supports streamlined case management across a variety of imaging modalities.

------ About the Annotation Tool ------

Our annotation platform is purpose-built to support both clinical-research and real world workflows at image reading centers. The platform enables:

• Multi-layered annotation filters for precise labeling and segmentation

• Customizable tools to adapt to different imaging modalities and retinal conditions

• Al-assisted annotation features to reduce manual workload and improve accuracy

• As-needed human intervention to correct the annotations and validation.

• Seamless saving and retrieval for efficient and trackable case management

• Biomarker quantification through automated Al models enabling measurement of key parameters including fluid measurements, drusen area and volume, geographic atrophy (GA) lesion size, retinal thickness, and Ellipsoid Zone (EZ) integrity and thickness.

The platform also supports workflow standardization across teams, helping to improve consistency, reduce labeling variability, and enhance the quality of downstream analysis.