EyeWorld India December 2020 Issue

46 EWAP DECEMBER 2020 DEVICES How AI applies to cornea by Liz Hillman Editorial Co-Director Contact information Ambrósio: dr.renatoAmbr ósio@gmail.com Wallace: dw@la-sight.com This article originally appeared in the August 2020 issue of EyeWorld . It has been slightly modified and appears here with permission from the ASCRS Ophthalmic Services Corp. R efractive surgery screening, ectasia risk detection and diagnosis, recommendations for corneal crosslinking, improving refractive surgery outcomes—these are just a few of the areas where clinician- scientists are hoping to employ artificial intelligence to enhance doctors’ decision making. And, in some respects, artificial intelligence is ready to deploy in the realm of the cornea. As Lopes et al. put it in a 2019 paper in the open-access journal Current Ophthalmology Reports , the cornea subspecialty was a “pioneer in aggregating technology to clinical practice,” and the “tremendous amount of information from complementary multimodal imaging devices” is “perfectly suitable for AI.” 1 Recognizing ectasia Renato Ambrósio Jr., MD, PhD, and colleagues have been conducting research that focuses on developing AI indices for describing the susceptibility of the cornea to ectasia and determining the impact of laser vision correction. “Considering the vulnerability or susceptibility of the cornea for biomechanical decompensation and ectasia progression, multimodal imaging is a factual revolution in evolution,” Dr. Ambrósio said. Dr. Ambrósio started working with artificial intelligence in 2008, helping design the Belin/ Ambrósio Enhanced Ectasia Display for Pentacam (Oculus). Other indices Dr. Ambrósio mentioned on this front include the Pentacam Random Forest Index, and the tomography and biomechanical index (TBI) that combines the data from Pentacam and Corvis ST (Oculus). According to a review article that included a meta-analysis of the published studies involving the TBI, it “had the highest accuracy for the detection of subclinical keratoconus compared to all other parameters tested.” 2 However, there are published reports of a lower sensitivity of TBI for detecting abnormalities on very asymmetric ectasia- normal topography (VAE-NT) cases. These cases demonstrate the opportunity and need for optimizing artificial intelligence for the combination of corneal tomography and biomechanical data. At the 2020 ASCRS Virtual Annual Meeting, Dr. Ambrósio presented a paper that showed a significant improvement in accuracy with an optimized machine learning algorithm, including the sensitivity of 85.6% (compared to TBI at 75.7%) in a series with more than 500 VAE-NT cases. Furthermore, assessing the impact of surgery on the corneal structure has also been developed with AI, Dr. Ambrósio said, mentioning the Ectasia Susceptibility Score, 3 which is available on the website of the Brazilian Study Group of Artificial Intelligence and Corneal Analysis (BrAIN). The next step is the development of the Enhanced Ectasia Susceptibility Score, Dr. Ambrósio continued, as one of the main projects of the BrAIN. He also described a new machine learning tool AT A GLANCE • Artificial intelligence (AI) helps to predict ectasia risk factors prior to refractive laser vision correction procedures (PRK, LASIK, SMILE). • Datasets for AI need to be carefully curated in order to deliver an accurate algorithm. • AI is likely to be used, in addition to the diagnosis, for the prognosis and treatment, including crosslinking.