Zhu Zeyu, Lin Peifen, Zhong Lingling, Wang Qing, Xu Jingjing, Yu Kang, Guo Zheliang, Xu Yicheng, Qiu Taorong, Yu Yifeng
Ophthalmic Center, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.
Center of Ophthalmic, Heyou Hospital, Foshan, China.
Front Cell Dev Biol. 2025 May 21;13:1598475. doi: 10.3389/fcell.2025.1598475. eCollection 2025.
Generative adversarial networks (GANs) were employed to predict the morphology of OBL before femtosecond laser scanning during SMILE.
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.
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.
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.
采用生成对抗网络（GAN）在微笑手术（SMILE）飞秒激光扫描前预测眼前节透镜（OBL）的形态。
对2021年6月至2022年8月在南昌大学第二附属医院眼科中心接受SMILE手术的2265例患者的4442只眼进行回顾性横断面分析。收集手术视频、术前全角膜图像和术中OBL图像。数据集被随机分为3998幅图像的训练集和444幅图像的测试集，分别用于模型开发和评估。使用结构相似性指数（SSIM）和峰值信噪比（PSNR）定量评估OBL图像质量。还比较了不同模型对术中OBL图像预测的准确性。
开发了7个GAN模型。其中，在Pix2pix框架内结合了残差结构和Transformer模块的模型表现出最佳的预测性能。该模型对术中OBL形态的预测与实际图像具有高度一致性（SSIM = 0.67，PSNR = 26.02）。Trans - Pix2Pix（SSIM = 0.66，PSNR = 25.76）、Res - Pix2Pix（SSIM = 0.65，PSNR = 23.08）、Pix2Pix（SSIM = 0.64，PSNR = 22.97）、Pix2PixHD（SSIM = 0.63，PSNR = 23.46）、DCGAN（SSIM = 0.58，PSNR = 20.46）的预测准确性略低，而CycleGAN模型（SSIM = 0.51，PSNR = 18.30）的结果最不理想。
基于术前全角膜图像开发的用于预测术中OBL形态的GAN模型具有有效的预测能力，为眼科医生进行手术规划提供了有价值的见解。
