Personalized Keratoconus Treatment Through AI-Driven 3D Lenticule Optimization
Farideh Doroodgar1 , Mohammad-Reza Jafarinasab2 , Sana Niazi3 *, Mohammad Ali Javadi2
- Translational Ophthalmology Research Center, Tehran University of Medical Sciences, Tehran, Iran - Negah Aref Ophthalmic Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- Department of Ophthalmology, Labbafinezhad Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
- Translational Ophthalmology Research Center, Tehran University of Medical Sciences, Tehran, Iran
Abstract: Keratoconus, a progressive corneal ectasia, poses significant challenges in vision restoration due to its highly individualized presentation. Traditional treatment approaches, including fixed-shape stromal lenticules, often fail to adequately address the unique topography and biomechanical properties of the cornea. This project aims to overcome these limitations by developing a 3D application powered by AI and advanced Pentacam indices. The tool is designed to assist ophthalmologists in predicting and customizing stromal lenticules tailored to each patient’s specific corneal profile, optimizing visual and structural outcomes.
Methods: Keratoconus, a progressive corneal ectasia, poses significant challenges in vision restoration due to its highly individualized presentation. Traditional treatment approaches, including fixed-shape stromal lenticules, often fail to adequately address the unique topography and biomechanical properties of the cornea. This project aims to overcome these limitations by developing a 3D application powered by AI and advanced Pentacam indices. The tool is designed to assist ophthalmologists in predicting and customizing stromal lenticules tailored to each patient’s specific corneal profile, optimizing visual and structural outcomes.
Results: Preliminary evaluations demonstrate the application’s potential to improve preoperative planning by offering data-driven lenticule customization. The integration of predictive analytics with interactive 3D modeling provides a more individualized approach, surpassing traditional fixed-shape lenticule methods.
Conclusion: This innovative AI-driven approach presents a transformative tool for keratoconus management, enhancing both surgical precision and visual outcomes. Ongoing refinement of the model, incorporating additional imaging and genetic data, aims to further personalize and optimize treatment strategies.
