Automated Imaging of Cataract Surgery Using Artificial Intelligence - Young Jae Kim; Sung Ha Hwang; Kwang Gi Kim; Dong Heun Nam (Diagnostics)
Objectives: This study proposes a state-of-the-art technology to estimate a set of parameters to automatically display an optimized image on a screen during cataract surgery. Methods: We constructed an architecture comprising two stages to estimate the parameters for realizing the optimized image. The Pix2Pix approach was first introduced to generate fake images that mimic the optimal image. This part can be considered a preliminary step; it uses training datasets comprising both an original microscopy image as the input data and an optimally tuned image by ophthalmologists as the label data. The second part of the architecture was inspired by ensemble learning, in which two ResNet-50 models were trained in parallel using fake images obtained in the previous step and unprocessed images. Each set of features extracted by the ensemble-like scheme was exploited for the regression of the optimal parameters. Results: The fidelity of our method was confirmed through relevant quantitative assessments (NMSE 121.052 ± 181.227, PSNR 29.887 ± 4.682, SSIM 0.965 ± 0.047). Conclusions: Subsequently, surgeons reassured that the objects to be highlighted on the screen for cataract surgery were faithfully visualized by the automatically estimated parameters.