Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Retina (Philadelphia, Pa.)

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.

Kawai Kentaro, Uji Akihito, Murakami Tomoaki, Kadomoto Shin, Oritani Yasuyuki, Dodo Yoko, Muraoka Yuki, Akagi Tadamichi, Miyata Manabu, Tsujikawa Akitaka

2020-Dec-31