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Newly designed method based on deep learning can detect macular fluid
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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.
“Expert readers trained in standardized identification of fluid-related features perform fluid detection and delineation often superior to clinicians in clinical routine. Fully automated segmentation offered identical precision in detection and delineation compared with the manual ground truth reading by experts in all three disease entities,” the researchers wrote.
Because of the tool’s precision, reliability and objectivity, this measurement device may become important in “the individual and the large-scale management of patients with macular disease,” they wrote. – by Robert Linnehan
Disclosures: Schlegl reports a patent pending for WO 2016139183 A1. Please see the study for all other authors’ relevant financial disclosures.
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