EyeWorld India December 2020 Issue

EWAP DECEMBER 2020 47 DEVICES (Relational Tissue Altered) that represents the impact of LASIK when assessing ectasia risk, which was described in another paper from the ASCRS Virtual Annual Meeting. David Wallace, MD, thinks that AI could help identify the early risk factors for keratoconus. Dr. Wallace is helping develop a smartphone-based, Placido corneal topography system (Delphi, Intelligent Diagnostics) that is more affordable, portable, and accessible than existing topography systems. Delphi will aggregate data in cloud servers to enable assisted analytics, intending to incorporate AI shortly after product launch. “This may identify a subset of risk factors, including forceful eye rubbing, that lead to early topographic change, which can then, if the behavior is not altered, develop into more advanced ectasia,” he said. Put another way, Dr. Wallace said, “the hope is that a machine like Delphi could offer the best real-time clinical interpretation to guide diagnostics and possibly therapeutic decisions.” In general, Dr. Wallace said he thinks AI will exceed human ability looking at single studies one at a time. “In terms of understanding the contributing factors to ectasia, AI may help us point to or identify risk factors that now perhaps are underappreciated. We also intend our system to be capable of real-time difference mapping in the cloud, which should greatly augment early detection.” From an imaging standpoint, Dr. Wallace pointed out that Placido reflectance is more sensitive than Scheimpflug elevation mapping for the front surface of the eye and should be able to identify smaller, more subtle irregularities than possible with Scheimpflug-based systems. “Dr. Ambrosio makes some excellent and valid points, which I wholeheartedly agree with, on relevance of posterior surgical imaging,” Dr. Wallace said. “There has been spirited debate about the relative merits of Placido vs. Scheimpflug for anterior surface imaging and the verdict is already in.” Corneal topography is an area where neural networks have already been used for assisted diagnostics. Neural networks, Dr. Wallace explained, are fixed-size datasets that help derive comparatives for normal and abnormal diagnoses. The challenge with an open system, as with AI, is that you can’t expect that it’s going to work in the same way as a closed neural net, Dr. Wallace said. “Any dataset needs to be somewhat curated so that bad data cannot pollute a good pool. That means finding and eliminating testing and sampling artifacts along with other sources of bad data,” he said. “There are a lot of factors that go into doing this right that are an important part of the process. There is no guarantee that every system that uses big data is going to generate inspired and thoughtful clinical- assisted diagnostics or analytics. It’s the combination of the big data capability, the thoughtful approach, and the selective use of good data that allows advances in this space,” Dr. Wallace said. Considering crosslinking There are two main possibilities for AI in planning crosslinking, Dr. Ambrósio said. The finite element modeling from William Dupps, MD, PhD, has been used to create customized crosslinking algorithms, he said, and the use of AI for prognostic factors is also promising. “In my routine, I consider the current parameters as the stiffness parameter described by Cynthia Roberts, PhD. But longitudinal studies are underway to develop such AI prognostic factors,” Dr. Ambrósio said. As new parameters are added to datasets, such as biomechanics, Dr. Ambrósio said he thinks the ability to detect and characterize disease will be improved. Dr. Wallace weighed in on the utility of artificial intelligence in crosslinking planning, first noting its potential to identify topographic asymmetry and early keratoconus for referrals for crosslinking. Second, will topography be able to guide custom treatment including combined excimer and CXL? he asked. “In theory certainly, but the devil is in the details. The details require much more information than just corneal topography from anterior surface Placido reflectance as an input dataset. You would want to know thickness mapping, you might want to know something about corneal biomechanics; that is not assumed just from thickness mapping,” Dr. Wallace said. Predicting refractive surgery outcomes Dr. Ambrósio said beyond screening for ectasia risk, AI is promising for augmenting efficiency and predictability of refractive surgery. Dr. Wallace was guarded in his opinion for AI’s utility in refractive surgery. “First, laser treatment technology with either excimer (PRK, LASIK) or femtosecond laser (SMILE) has certain limitations. Second, identification of certain corneal anatomy by machine, such as the visual axis, angle kappa, and other features, is to some extent variable from machine to machine, so there may not be enough consistency to agree on the input data to guide output for treatment planning. Third, I honestly think that we are going to have several years of adaptation to AI in a strictly diagnostic mode, and that is going to need to precede any thinking about artificial intelligence in a treatment capacity,” he said. Ultimately, Dr. Wallace cautioned against machines being “as good or better than a skilled, experienced refractive surgeon.” “… most of us who are experienced laser refractive surgeons have spent years or decades getting here, and