Eyeworld

R WINTER 2024 | EYEWORLD | 55 References 1. Maeda N, et al. Neural network classification of corneal topog- raphy. Preliminary demonstra- tion. Invest Ophthalmol Vis Sci. 1995;36:1327–1335. 2. Ambrosio Jr R, Randleman JB. Screening for ectasia risk: what are we screening for and how should we screen for it? J Refract Surg. 2013;29:230–232. 3. Ambrósio Jr R, Belin M. En- hanced screening for ectasia risk prior to laser vision correction. Int J Keratoconus Ectatic Corneal Dis. 2017;6:23–33. 4. Ambrósio Jr R. Post-LASIK ectasia: twenty years of a conundrum. Semin Ophthalmol. 2019;34:66–68. 5. Dupps WJ, Seven I. A large- scale computational analysis of corneal structural response and ectasia risk in myopic laser refractive surgery. Trans Am Ophthalmol Soc. 2016;114:T1. 6. McGhee CNJ, et al. Contem- porary treatment paradigms in keratoconus. Cornea. 2015;34 Suppl 10:S16–23. 7. Ambrósio Jr R, et al. Optimized artificial intelligence for en- hanced ectasia detection using Scheimpflug-based corneal tomography and biomechan- ical data. Am J Ophthalmol. 2023;251:126–142. promote the benefits of AI, leading to broader adoption and enhanced patient safety." Though rare, Dr. Ambrósio said he thinks AI for determining ectasia risk scores is an "abso- lute must-have in refractive surgery." Besides LVC, keratoconus detection is essential in refrac- tive cataract surgery as it impacts IOL selection, quality of vision, and the strategy for corneal enhancements. 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 in- cluding novel parameters. 7 Further optimization is expected from integrating segmental tomog- raphy data with Fourier-domain OCT, with epi- thelial 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 RTA (relational thickness altered) 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 character- izing the lamellar dissection and ablation, sig- nificantly 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 surgi- cal impact and the intrinsic susceptibility. "Nevertheless, preventing ectasia after refractive surgery (and in non-refractive pa- tients) should include educating patients on not rubbing the eye. When ancient and artifi- cial intelligence is 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 in- volved in research for AI applications in cornea (see the box on page 56), 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 pa- tients better than we can alone. It may require other types of imaging," he said. "I think the combination of more data, more types of imag- ing 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 de- tecting 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. continued on page 56 " While the journey toward widespread use is in its early stages, the path forward is clear and filled with promise, emphasizing the importance of ongoing research, education, and technological development in refractive surgery." —Renato Ambrósio Jr., MD, PhD

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