Oculotix offers an AI-powered platform that assists ophthalmologists in selecting intraocular lenses, tailoring choices to each patient’s specific anatomy and visual needs. By analyzing pre‑operative data, the system predicts optimal lens parameters, helping surgeons achieve more accurate refractive outcomes and greater confidence in surgical planning. The technology integrates directly into existing clinical workflows, streamlining decision‑making and improving post‑operative results.
Funding
Funding not disclosed
Founders
Product
Problem
Choosing the appropriate intraocular lens (IOL) for cataract surgery involves evaluating numerous patient-specific factors, and errors can lead to suboptimal visual outcomes and the need for postoperative adjustments.
Solution
Oculotix offers an AI-driven decision support platform that processes individual patient data—such as biometric measurements, ocular history, and lifestyle preferences—to recommend the most suitable IOL model for each surgery. The system integrates directly into the surgeon’s workflow, providing real-time suggestions that enhance confidence in lens selection. By leveraging machine‑learning models trained on large clinical datasets, the platform improves prediction accuracy for postoperative visual acuity. The recommendations are presented through an intuitive interface, allowing surgeons to review and adjust choices before finalizing the procedure. This approach aims to increase consistency of outcomes across patients and reduce the incidence of postoperative refractive surprises.
Target Audience
Primary customers are ophthalmic surgeons and cataract surgery centers seeking to improve IOL selection accuracy and patient satisfaction.
Features
- AI algorithm that analyzes biometric and clinical data to generate personalized IOL recommendations
- Seamless integration with existing surgical planning software and electronic health record systems
- Real‑time, on‑screen decision support during pre‑operative consultations
- Transparent rationale display showing key factors influencing each recommendation
- Continuous learning loop that updates models with post‑operative outcome data