Digital image enhancement using deep learning algorithm in 3D heads-up vitreoretinal surgery

This study aims to predict the optimal imaging parameters using a deep learning algorithm in 3D heads-up vitreoretinal surgery and assess its effectiveness on improving the vitreoretinal surface visibility during surgery. To develop the deep learning algorithm, we utilized 212 manually-optimized still images extracted from epiretinal membrane (ERM) surgical videos. These images were applied to a two-stage Generative Adversarial Network (GAN) and Convolutional Neural Network (CNN) architecture. The algorithm's performance was evaluated based on the peak signal-to-noise ratio (PSNR) and structural similarity index map (SSIM), and the degree of surgical image enhancement by the algorithm was evaluated based on sharpness, brightness, and contrast values. A survey was conducted to evaluate the intraoperative suitability of optimized images. For an in-vitro experiment, 121 anonymized high-resolution ERM fundus images were optimized using a 3D display based on the algorithm. The PSNR and SSIM values are 34.59 ± 5.34 and 0.88 ± 0.08, respectively. The algorithm enhances the sharpness, brightness and contrast values of the surgical images. In the in-vitro experiment, both the ERM size and color contrast ratio increased significantly in the optimized fundus images. Both surgical and fundus images are digitally enhanced using a deep learning algorithm. Digital image enhancement using this algorithm can be potentially applied to 3D heads-up vitreoretinal surgeries.