Eyeworld

R EFRACTIVE 56 | EYEWORLD | WINTER 2024 Contact Ambrósio: dr.renatoambrosio@gmail.com Faktorovich: ella@pacificvision.org Nallasamy: nnallasa@med.umich.edu Redd: redd@ohsu.edu Relevant disclosures Ambrósio: Alcon, Ambrósio Vision Academy, Brazilian Artificial Intelligence Networking in Medicine (BrAIN), Carl Zeiss Meditec, Mediphacos, Oculus Faktorovich: None Nallasamy: Recordati Rare Diseases Redd: None "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 [ma- chine learning] AI and [rule-based] AI to plan topography-guided surgical treatments. With [machine learning] AI, linear regression analysis formulas allow us to accurately plan treatment of sphere, cylinder, and higher order aberrations such as spherical aberration, coma, trefoil and others. With [rule-based] AI, we can integrate data from the corneal aberrometer and topogra- pher to help not only decide if the patient would benefit from topography-guided treatment but also what is the correct treatment to program into the laser to achieve the most precise out- comes. Postoperative outcomes data on visual acuity, refractive error, and higher order aber- rations are then input into another AI-driven software to generate nomograms for subsequent patients undergoing treatments." In the future, Dr. Faktorovich said she en- visions AI being used for enhanced diagnostics. Dr. Redd said there are a few things that are needed to bring AI in refractive surgery (and the anterior segment, in general) to the next level. "Deep learning models have been developed to successfully perform image-based diagnosis of several anterior segment diseases, including keratoconus, infectious keratitis, pterygia, and trachoma among many others. AI models have also been trained to provide quantification of various biomarkers of disease severity, allow- ing more objective monitoring of progression and response to treatment over time," he said. "However, currently the only FDA-authorized AI-enabled SaMD (software as a medical device) in ophthalmology are for automated diagnosis of diabetic retinopathy. There are many reasons for the gap between the large number of AI models described in the ophthalmology litera- ture and the few that have been implemented clinically, but chief among them are the scar- city of representative datasets for training and evaluating AI models, limited interdisciplinary collaboration, and lack of well-defined reim- bursement models." continued from page 55 Other AI updates Dr. Nallasamy shared some insights on where AI is being applied to other areas of the anterior segment. He said there is currently a clinical trial being led by Maria Woodward, MD, looking at microbial kera- titis. The work is developing algorithms to identify key parameters involved in corneal ulcers. This research, he said, could help provide some objectivity in a world that's usually subjective. "Typically, we've done slit lamp exams on these patients. Often we didn't even take photos, relying on just seeing whether it got better or worse and changing treatment ac- cordingly, but with these tools, we'll be able to rigorously follow the size and response to treatment and titrate accordingly," he said. Dr. Nallasamy said his lab is doing work us- ing confocal microscopy and AI to develop a system that can automatically diagnose the type of corneal infection earlier on, rather than just tracking its response to treatment. Separately, Dr. Nallasamy said his lab is also creating an intraoperative decision-making tool for cataract surgery that, using AI, will help the surgeon better understand the likelihood of a patient potentially needing a pupil expansion device during their proce- dure, based on how the eye is responding in real time to the surgery. Also in the anterior segment, Dr. Nallasamy referenced work being done by Carol Karp, MD, using anterior segment OCT and AI to develop a system that can differentiate OSSN from benign tumors.

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