EyeWorld Asia-Pacific December 2020 Issue

DEVICES 54 EWAP DECEMBER 2020 ophthalmologists’ offices for screening exams. Specifically relating to diabetic retinopathy, Dr. Ho said it’s important to improve communication among all members of the care team. In large-scale disease screening, the interaction would hopefully become more refined, providing risk assessments to the general care team from specialists. Cost effectiveness Dr. Ho said AI could create value by identifying patients who are more responsive to medication, those who may not respond to certain treatments, those at risk for vision loss, and patients more likely to be lost to follow- up. Currently AI algorithms are employed in home monitoring solutions for the early detection of neovascular AMD and in the near future with home diagnostic imaging that may refine treatment of macular diseases with increased value to the patient and the healthcare system in general. The cost- efficient interpretation of daily home OCT images for the identification and quantification of intra- and subretinal fluid will require AI assistance. Dr. Stoller added that AI may be a cost-effective way to improve patients’ access to care because remote visits/ teleophthalmology help reach patients who may not be located in areas with easy access to care. “I think screening initiatives combined with AI technologies may help to diagnose eye disease that might otherwise get missed,” he said. It could also require fewer resources to operate, as opposed to some of the more time- and labor- intensive tests, Dr. Stoller said. Dr. Joseph said that if AI reaches the point where it’s able to predict disease progression and more tailored therapy, physicians could hopefully use resources more efficiently, instead of relying on a cookie-cutter approach. “If we’re looking at earlier disease detection and more accurate detection, hopefully we’re treating patients before they develop a serious disease burden,” he said. Risks and challenges Dr. Ho said that one possible risk is that you can get misled by large datasets. Just because there’s an association doesn’t mean there’s causation, he said. AI may give you hypotheses but not definite causation or optimal treatment. Expert, experienced human intelligence will still be an essential component of the interpretation and implementation of AI analyses. Another risk is trying to obtain more information than what the dataset can yield, he added. “I think this is something that has been on the radar for awhile, but it takes time,” Dr. Joseph said, adding that he expects a number of regulatory and ethical concerns for patient privacy and data protection. AI requires large amounts of patient data, he added, so there is the question of who owns the data. There are also technical limitations in terms of computational power that’s required. “We’re producing or obtaining a large amount of clinical imaging, and that’s what we want to use, but I think processing it takes manpower now and computational power later,” Dr. Joseph said. “I think if you’re looking further down the road, once we develop algorithms or tools, what kind of human oversights are we going to have?” He thinks some degree of human oversight will be needed. Adding this new tool won’t happen overnight, he said. “I think people think that this is not something that will be a replacement for clinical judgment, but it will certainly be an important tool in how we manage disease,” Dr. Joseph said. Dr. Stoller said that one major challenge for AI will be acceptance from patients and doctors. “Many patients are willing to embrace high- tech devices, but I think they still might not trust computer diagnosis and would rather have their diagnosis made by an in-person visit,” he said. With regard to physician acceptance, it’s not always obvious how the computer algorithm reaches its conclusion, so the physicians are forced to trust the AI system without being able to evaluate the value of the metrics and data used by the computer program. Another challenge is managing the technology itself. It’s important to assess the quality and validation of the datasets to ensure the results are applicable across diverse populations. “The dynamic AI-based identification and quantification of intra- and subretinal fluid on home OCT images of a patient with neovascular AMD. Source: Notal Vision continued on page 56