Eyeworld

R EFRACTIVE 54 | EYEWORLD | WINTER 2024 by Liz Hillman Editorial Co-Director About the physicians Renato Ambrósio Jr., MD, PhD Adjunct Professor of Ophthalmology Federal University of the State of Rio de Janeiro Rio de Janeiro, Brazil Ella Faktorovich, MD Pacific Vision Institute San Francisco, California Nambi Nallasamy, MD Assistant Professor of Ophthalmology and Visual Sciences Assistant Professor of Computational Medicine and Bioinformatics University of Michigan Kellogg Eye Center Ann Arbor, Michigan Travis Redd, MD Assistant Professor of Ophthalmology School of Medicine Oregon Health & Science University Portland, Oregon W hat's the latest tool in the refrac- tive surgeon's toolbox? As with many fields from marketing to medicine, it's artificial intelli- gence (AI). EyeWorld spoke with several surgeons to learn more about how AI is impacting refractive surgery, as well as some other areas of the anterior segment. "The field of refractive surgery has become incredibly complex," said Ella Faktorovich, MD. There are different types of refractive proce- dures and important diagnostic tools coinciding with preop screening. "How do we decide, for example, whether a patient with mild inferior corneal steepening is best treated with a corneal or lens procedure? Is the steepening consistent with keratoconus and significant enough to ben- efit from crosslinking prior to refractive surgery? When planning the patient's treatment, how can we use postoperative outcomes data from other patients who underwent similar treatments to plan this patient's treatment? AI has become essential to help us answer these questions and navigate the increasingly complex field of re- fractive surgery diagnostics and treatments." AI in diagnostics To determine a patient's suitability for refrac- tive surgery, various ocular parameters need to be carefully measured and analyzed, Dr. Faktorovich said. "We typically use five different methods to assess corneal health—topography, tomography, epithelial thickness mapping with widefield OCT, corneal biomechanics, and aber- rometry to map higher order aberrations. Diag- nostic software in topography and tomography uses [rule-based] AI to benchmark each individ- ual's corneal characteristics against the database in the software. Tomography performed with Pentacam [Oculus], for example, generates seven different corneal indices. The device's AI then compares these indices to the software's database and determines the likelihood of ker- atoconus. If keratoconus is likely, the software grades the severity. Currently, only topography and tomography devices have AI capabilities. "Devices such as the Ocular Response Analyzer [Reichert] to measure corneal biome- chanics and OCT to perform epithelial thickness mapping don't have AI," she continued. "A clinician relies on their expertise and published data to determine whether the results of these tests are normal or abnormal. This is an area where technology can be improved to include AI. Additionally, there could be improvement in creating a software that integrates information from multiple testing devices and benchmarks that against a data set of normal and abnormal corneal tests performed with these multiple devices. This multimodal benchmarking could be especially helpful when findings from one or several devices are mildly abnormal." AI for ectasia risk screening Renato Ambrósio Jr., MD, PhD, said Stephen Klyce, PhD, led groundbreaking work in the 1990s that used AI to help diagnose keratoco- nus. 1 Since then, AI in refractive surgery screen- ing has expanded. "The characterization of the inherent susceptibility of the cornea for biomechanical decompensation and ectasia progression 2-4 has to be considered along with the impact of the LVC procedure. 5 While this concept relates to the two-hit hypothesis, including intrinsic and extrinsic factors, 6 AI's ability to analyze complex datasets and identify subtle patterns by consid- ering multiple features instantaneously makes it a valuable tool for improving accuracy and inclusivity in refractive procedures. This individ- ualized approach improves sensitivity for safety and specificity for higher inclusivity of refractive procedures," Dr. Ambrósio said. Dr. Ambrosio said we don't have data about how widespread AI use is for ectasia risk assess- ments in refractive surgery screening, but he thinks it's limited. "Ectasia is a very severe and feared complication of LVC. While detailed data on its prevalence is not fully available, its inci- dence has reduced. The potential for AI to en- hance ectasia risk assessment is significant," he said, adding that adopting AI-based ectasia risk assessment tools is an opportunity to improve clinical practice. "Key factors include increasing awareness and education among clinicians, obtaining regulatory approval, ensuring data privacy and security, and developing cost-ef- fective solutions. Addressing these barriers can AI expanding in refractive surgery

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