ORIGINAL RESEARCH article
Front. Cell Dev. Biol.
Sec. Molecular and Cellular Pathology
Volume 13 - 2025 | doi: 10.3389/fcell.2025.1598475
This article is part of the Research TopicArtificial Intelligence Applications in Chronic Ocular Diseases, Volume IIView all 31 articles
Based on TransRes-Pix2Pix Network to Generate the OBL Image During SMILE Surgery
Provisionally accepted
Zeyu Zhu1,2
Peifen Lin3Lingling Zhong4Qing Wang2Jingjing Xu2
Kang Yu2Zheliang Guo5Yicheng Xu5Taorong Qiu3*
Yifeng Yu2*- 1Heyou Hospital, Foshan, China
- 2Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi Province, China
- 3School of Mathematics and Computer Sciences, Nanchang University, Nanchang, Jiangxi Province, China
- 4Fuzhou Experimental School, Fuzhou, China
- 5Nanchang University, Nanchang, Jiangxi Province, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
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.
Keywords: artificial intelligence, Generative Adversarial Networks, Opaque bubble layer, small-incision lenticule extraction, complication
Received: 23 Mar 2025; Accepted: 05 May 2025.
Copyright: © 2025 Zhu, Lin, Zhong, Wang, Xu, Yu, Guo, Xu, Qiu and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Taorong Qiu, School of Mathematics and Computer Sciences, Nanchang University, Nanchang, Jiangxi Province, China
Yifeng Yu, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, Jiangxi Province, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.