Based on TransRes-Pix2Pix network to generate the OBL image during SMILE surgery.

Abstract

Aim: Generative adversarial networks (GANs) were employed to predict the morphology of OBL before femtosecond laser scanning during SMILE.

Methods: A retrospective cross-sectional analysis was conducted on 4,442 eyes from 2,265 patients who underwent SMILE surgery at the Ophthalmic Center of the Second Affiliated Hospital of Nanchang University between June 2021 and August 2022. Surgical videos, preoperative panoramic corneal images, and intraoperative OBL images were collected. The dataset was randomly split into a training set of 3,998 images and a test set of 444 images for model development and evaluation, respectively. Structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) were used to quantitatively assess OBL image quality. The accuracy of intraoperative OBL image predictions was also compared across different models.

Results: Seven GAN models were developed. Among them, the model incorporating a residual structure and Transformer module within the Pix2pix framework exhibited the best predictive performance. This model's intraoperative OBL morphology prediction demonstrated high consistency with actual images (SSIM = 0.67, PSNR = 26.02). The prediction accuracy of Trans-Pix2Pix (SSIM = 0.66, PSNR = 25.76), Res-Pix2Pix (SSIM = 0.65, PSNR = 23.08), and Pix2Pix (SSIM = 0.64, PSNR = 22.97), Pix2PixHD (SSIM = 0.63, PSNR = 23.46), DCGAN (SSIM = 0.58, PSNR = 20.46) was slightly lower, while the CycleGAN model (SSIM = 0.51, PSNR = 18.30) showed the least favorable results.

Conclusion: The GAN model developed for predicting intraoperative OBL morphology based on preoperative panoramic corneal images demonstrates effective predictive capabilities and offers valuable insights for ophthalmologists in surgical planning.