Clinical Validation of an Artificial Intelligence-Based Screening and Diagnostic Tool 3Nethra Ultima for chronic eye diseases compared to standard diagnostic methods

This study aims to prospectively evaluate the diagnostic accuracy and clinical utility of the AI-based ophthalmic device 3Nethra Ultima for detecting Diabetic Retinopathy (DR), Age-Related Macular Degeneration (AMD), and Dry Eye Disease in a clinical setting. Diagnostic accuracy for DR and AMD will be assessed using fundus images from 3Nethra Ultima, 3Nethra Pico, and EIDON cameras, compared to a reference standard of independent dual expert grading and senior specialist adjudication. For Dry Eye Disease, the AI tool’s performance in measuring TBUT, TMH, LLI, and MGD will be evaluated against standard clinical diagnostic methods. The goal is to validate the AI system’s ability to support accurate, efficient, and scalable eye disease screening.To prospectively determine the diagnostic accuracy and clinical utility of the AI-driven 3nethra Ultima device for the automated detection of Diabetic Retinopathy (DR), Age-Related Macular Degeneration (AMD), and Dry Eye Disease, compared to established clinical diagnostic standards and masked expert evaluation.