In a presentation at the EURETINA 2021 Virtual Congress, Anat Loewenstein, MD, MHA, discussed optimizing optical coherence tomography (OCT) and how physicians can determine with even more accuracy precisely what is happening in patients' eyes with neovascular age-related macular degeneration (nAMD) because of the potential afforded by application of artificial intelligence (AI). Loewenstein is professor and director of the Department of Ophthalmology at Tel Aviv Medical Center, the Sidney A. Fox Chair in Ophthalmology, and vice dean, Sackler Faculty of Medicine, Tel Aviv University, Israel. OCT is a major step forward in patient diagnosis, treatment, and monitoring but shortcomings remain. For example, physicians routinely make qualitative assessments of the presence and degrees of intraretinal/subretinal fluid and pigment epithelial detachments, but these are not precise assessment that are likely to result in poor inter-grader agreement and intra-grader consistency; OCT also provides the central subfield thickness, but the retinal fluid and neural tissue are not considered separately. As Loewenstein pointed out, In neovascular AMD, it is important to distinguish between retinal fluid localization in the intraretinal and subretinal compartments and their volumetric information for informing retreatment decisions and predicting visual outcomes.

An automated method based on deep learning to identify and quantify intraretinal cystoid fluid and subretinal fluid is an accurate digital analysis tool. Researchers developed a validated artificial intelligence method using deep learning to fully detect and quantify macular fluid in clinical OCT imaging. The digital analysis tool can identify macular fluid in patients with neovascular age-related macular degeneration, diabetic macular edema and retinal vein occlusion. Using the newly developed method, researchers reported a mean accuracy of 0.94, a mean precision of 0.91 and a mean recall of 0.84 for the detection and quantification of intraretinal cystoid fluid. The subretinal fluid measurements were accurate as well, with a mean accuracy of 0.92, a mean precision of 0.61 and a mean recall of 0.81.