Artificial Intelligence Can Both Measure and Distinguish Retinal Fluid

Accurate retinal fluid volume data can be efficiently extracted from optical coherence tomography (OCT) scans by artificial intelligence (AI) algorithms and may assist clinicians in managing neovascular age-related macular degeneration (NV-AMD), according to researchers. A study published in the American Journal of Ophthalmology shows that the precise quantitative measures of intraretinal fluid (IRF), subretinal fluid (SRF) and pigment epithelial detachment (PED) can potentially be used as biomarkers by the algorithm to predict changes in NV-AMD and guide treatment.

The research involved scans from 4 retinal image repository datasets — 24,362 scans of 1095 eyes from the HARBOR study (ClinicalTrials.gov Identifier: NCT00891735), 511 of 511 from the AREDS2-10 year follow on (ClinicalTrials.gov Identifier: NCT03367767), 4673 of 880 from a real-world study in Belfast, and 1470 of 132 from a real-world study in Tel-Aviv. AI-based automated detection and quantification of IRF, SRF, and PED volumes was used in populations with NV-AMD.

The investigation shows large ranges that differed by population at the treatment-naïve stage and rapid mean volume decreases at the anti-VEGF therapy maintenance stage. The researchers found that the most appropriate unit of measurement to encompass the entire range of fluid volumes was the nanoliter because it encompassed the full range of fluid volumes.

“In clinical practice, the ability to extract quantitative metrics of exudative activity from OCT scans represents an important advance over the qualitative descriptions that are normally used, where retinal fluid is typically graded as present versus absent, or severe versus mild,” the study says. The quantitative approach the AI offers avoids problems associated with low intra-grader consistency or inter-grader agreement. The technology provides the capability to evaluate exudative activity more meaningfully between sequential and even distant visits of the same patient as well as improve record-keeping and visualization of exudative activity, the researchers report.

Importantly, the AI software allows assessments and comparisons to be made separately for IRF, SRF, and PED. This is key as prior academic research not all exudative activity is equal. Visual prognoses and the retreatment decisions based on them can be influenced differently depending on the type of exudative activity.

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