IMAGE EVALUATION OF ARTIFICIAL INTELLIGENCE–SUPPORTED OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY IMAGING USING OCT-A1 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. Optical coherence tomography angiography imaging of a 3 × 3-mm area involving the lesions (neovascularization, intraretinal microvascular abnormality, and nonperfusion 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 through 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, vessel density, vessel length density, and fractal dimension. 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 vessel density, vessel length density, fractal dimension, and contrast-to-noise ratio ( 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 2 of the 35 cases of nonperfusion area. Conclusion: Denoise processing by AI improved the image quality of OCTA in a shorter time and allowed more accurate quantitative evaluation.