Hwang Sung Ha, Kim Young Jae, Cho Jae Bok, Kim Kwang Gi, Nam Dong Heun
Department of Ophthalmology, Gachon University Gil Medical Center, 21 Namdong-daero 774-beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea.
Department of Pre-Medicine, Gachon University, Incheon, Korea.
Sci Rep. 2025 May 26;15(1):18429. doi: 10.1038/s41598-025-98801-7.
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.
本研究旨在利用深度学习算法预测三维直视玻璃体视网膜手术中的最佳成像参数，并评估其在提高手术中玻璃体视网膜表面可见度方面的有效性。为开发深度学习算法，我们利用了从视网膜前膜（ERM）手术视频中提取的212张手动优化的静态图像。这些图像被应用于一个两阶段生成对抗网络（GAN）和卷积神经网络（CNN）架构。基于峰值信噪比（PSNR）和结构相似性指数图（SSIM）评估算法的性能，并基于清晰度、亮度和对比度值评估算法对手术图像的增强程度。进行了一项调查以评估优化图像在术中的适用性。对于体外实验，使用基于该算法的3D显示器对121张匿名的高分辨率ERM眼底图像进行了优化。PSNR和SSIM值分别为34.59±5.34和0.88±0.08。该算法提高了手术图像的清晰度、亮度和对比度值。在体外实验中，优化后的眼底图像中ERM的大小和颜色对比度均显著增加。手术图像和眼底图像均使用深度学习算法进行了数字增强。使用该算法进行数字图像增强可能适用于三维直视玻璃体视网膜手术。
