36 EyeWorld Asia-Pacific | March 2025 While Dr. Ambrósio noted the utility of AI in refractive surgery screening overall, he also provided some more specific updates. “The TBIv2 (or BrAIN-TBI) was optimized based on a larger dataset for training and including novel parameters.7 Further optimization is expected from integrating segmental tomography data with Fourierdomain OCT, with epithelial and Bowman thickness profiles,” he said. “Genetic testing and other molecular biology tests may also play a relevant role. “Considering the impact of surgery, we developed the relational thickness altered (RTA) with an AI algorithm that includes the patient’s age, thinnest point data, ablation depth (PRK or LASIK), flap (LASIK), or cap thickness and lenticule extraction (LALEX/SMILE). The RTA considers each case individually and weighs the flap or cap and the ablation in LASIK differently. It provides a superior approach to characterizing the lamellar dissection and ablation, significantly outperforming traditional methods of calculating residual stromal bed and percentage of thickness altered for a more comprehensive and accurate risk assessment.” The updates and developments in AI for refractive surgery are driven by a desire for increased patient safety, Dr. Ambrósio said. “By prioritizing safety and leveraging advanced AI algorithms like the RTA and the TBI, we can generate the enhanced susceptibility score available in the BrAIN Enhanced Corneal Ectasia Software,” he said. “This individual approach combines a nuanced understanding of the surgical impact and the intrinsic susceptibility. “Nevertheless, preventing ectasia after refractive surgery (and in non-refractive patients) should include educating patients on not rubbing their eyes. When common and artificial intelligence are consciously applied, we can uphold the highest standards of patient care and significantly enhance refractive surgery,” Dr. Ambrósio said. Nambi Nallasamy, MD, who is more involved in research for AI applications in cornea (see the box on page 37), said we can always improve upon screening for refractive surgery, and AI is a promising avenue. “I’m sure that AI will be able to identify patients better than we can alone. It may require other types of imaging,” he said. “I think the combination of more data, more types of imaging plus AI is really going to help us here.” Travis Redd, MD, also said that the primary application for AI in refractive surgery at the moment is in screening for ectasia risk. “Deep learning models are very good at detecting subtle patterns in images such as corneal tomography,” he said. “This capability could be used to preoperatively identify patients with a high likelihood of developing post-refractive surgery ectasia.” AI In Treatment Planning The nomograms used for refractive surgery planning are heavily dependent on machine learning, which takes outcomes from cases and updates the formulas, Dr. Faktorovich said. “To fully harness the power of treating both lower and higher order aberrations, nomogram use is essential. AI is, therefore, a critical component of achieving the best vision postoperatively,” she said. “We use both machine learning and rule-based AI to plan topography-guided surgical treatments. With machine learning AI, linear regression analysis formulas allow us to accurately plan treatment REFRACTIVE SURGERY
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