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IMAGE EVALUATION OF ARTIFICIAL INTELLIGENCE SUPPORTED OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IMAGING USING OCT-HS100 DEVICE IN DIABETIC RETINOPATHY.

PURPOSE
To investigate the effect of denoise processing by artificial intelligence (AI) on the optical coherence tomography angiography (OCTA) images in eyes with retinal lesions.


METHODS
Prospective, observational, cross-sectional study. OCTA imaging of a 3 x 3 mm area involving the lesions (neovascularization, intraretinal microvascular abnormality, and non-perfusion area) was performed five times using OCT-HS100 (Canon, Tokyo, Japan). We acquired AI-denoised OCTA images and averaging OCTA images generated from five cube scan data via built-in software. Main outcomes were image acquisition time and the subjective assessment by graders and quantitative measurements of original OCTA images, averaging OCTA images, and AI-denoised OCTA images. The parameters of quantitative measurements were contrast-to-noise ratio (CNR), vessel density (VD), vessel length density (VLD), and fractal dimension (FD).


RESULTS
We studied 56 eyes from 43 patients. The image acquisition times for the original, averaging, and AI-denoised images were 31.87±12.02, 165.34±41.91, and 34.37±12.02 seconds, respectively. We found significant differences in VD, VLD, FD, and CNR (P < 0.001) between original, averaging, and AI-denoised images. Both subjective and quantitative evaluations showed that AI-denoised OCTA images had less background noise and depicted vessels clearly. In AI-denoised images, the presence of fictional vessels was suspected in two out of 35 cases of non-perfusion area.


CONCLUSIONS
Denoise processing by AI improved image quality of OCTA in a shorter time and allowed more accurate quantitative evaluation.