Reviewed by Anat Loewentstein, MD, MHA
Using optical coherence tomography (OCT), physicians can determine with even more accuracy what is happening in patients’ eyes with neovascular age-related macular degeneration (nAMD) because of the potential afforded by the application of artificial intelligence (AI), according to Anat Loewenstein, MD, MHA.
Loewenstein is a professor and the director of the Department of Ophthalmology at Tel Aviv Medical Center. She also is the Sidney Fox Chair in Ophthalmology, and vice dean of the Sackler Faculty of Medicine at Tel Aviv University in 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 assessments that are likely to result in poor intergrader agreement and intragrader consistency; OCT also provides the central subfield thickness, but the retinal fluid and neural tissue are not considered separately.
As Loewenstein pointed out, with 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.
One technology to quantify OCT data, according to Loewenstein, is the home-based Notal OCT Analyzer (NOA), a machine learning algorithm that distinguishes normal morphologic features from elevated or distorted contours to quantify macular fluid of OCT volumes. The NOA quantifies intraretinal/subretinal fluid on Spectralis and Cirrus spectral-domain OCT devices. Both algorithms display their respective fluid output in nanoliters, which allows repeatable measurements and the precise monitoring of disease activity.
“Recent advances in machine learning and deep learning have resulted in the development of software that can process routinely acquired spectral-domain OCT data and precisely determine the location and volumetric information of macular fluid within different tissue compartments,” Loewenstein said.
According to Loewenstein, the software then can automatically generate multiple quantitative metrics related to macular fluid variables that may provide substantial advantages to research and clinical practice.
Loewenstein suggested that using volume instead of thickness, separating fluid volume from neural tissue volume, distinguishing between intraretinal and subretinal fluid, and monitoring dynamics over time might improve OCT’s ability to predict visual acuity changes.
Moreover, she emphasized the following:
Anat Loewenstein, MD, MHA
This article was adapted from Loewenstein’s presentation at the European Society of Retina Specialists virtual annual congress. She is a consultant to Notal Vision.