In ophthalmology, the quality of fundus images is critical for accurate diagnosis, both in clinical practice and in artificial intelligence (AI)-assisted diagnostics. Despite the broad view provided by ultrawide-field (UWF) imaging, pseudocolor images may conceal critical lesions necessary for precise diagnosis. To address this, we introduce UWF-Net, a sophisticated image enhancement algorithm that takes disease characteristics into consideration. Using the Fudan University ultra-wide-field image (FDUWI) dataset, which includes 11 294 Optos pseudocolor and 2415 Zeiss true-color UWF images, each of which is rigorously annotated, UWF-Net combines global style modeling with feature-level lesion enhancement. Pathological consistency loss is also applied to maintain fundus feature integrity, significantly improving image quality. Quantitative and qualitative evaluations demonstrated that UWF-Net outperforms existing methods such as contrast limited adaptive histogram equalization (CLAHE) and structure and illumination constrained generative adversarial network (StillGAN), delivering superior retinal image quality, higher quality scores, and preserved feature details after enhancement. In disease classification tasks, images enhanced by UWF-Net showed notable improvements when processed with existing classification systems over those enhanced by StillGAN, demonstrating a 4.62% increase in sensitivity (SEN) and a 3.97% increase in accuracy (ACC). In a multicenter clinical setting, UWF-Net-enhanced images were preferred by ophthalmologic technicians and doctors, and yielded a significant reduction in diagnostic time ((13.17 ± 8.40) s for UWF-Net enhanced images vs (19.54 ± 12.40) s for original images) and an increase in diagnostic accuracy (87.71% for UWF-Net enhanced images vs 80.40% for original images). Our research verifies that UWF-Net markedly improves the quality of UWF imaging, facilitating better clinical outcomes and more reliable AI-assisted disease classification. The clinical integration of UWF-Net holds great promise for enhancing diagnostic processes and patient care in ophthalmology.

中文翻译：

---

促进临床诊断的自动超宽视野眼底成像增强系统的开发与验证：一项横断面多中心研究


在眼科中，眼底图像的质量对于准确诊断至关重要，无论是在临床实践中还是在人工智能 （AI） 辅助诊断中。尽管超宽视野 （UWF） 成像提供了广阔的视野，但伪彩色图像可能隐藏了精确诊断所需的关键病变。为了解决这个问题，我们引入了 UWF-Net，这是一种复杂的图像增强算法，它考虑了疾病特征。UWF-Net 使用复旦大学超宽场图像 （FDUWI） 数据集，其中包括 11 294 张 Optos 伪彩图像和 2415 张 Zeiss 真彩色 UWF 图像，每张图像都经过严格注释，将全局样式建模与特征级病变增强相结合。病理一致性损失也用于保持眼底特征的完整性，显著提高图像质量。定量和定性评估表明，UWF-Net 优于现有方法，例如对比度受限自适应直方图均衡 （CLAHE） 和结构和照明约束生成对抗网络 （StillGAN），提供卓越的视网膜图像质量、更高的质量分数和增强后保留的特征细节。在疾病分类任务中，使用现有分类系统处理时，UWF-Net 增强的图像比 StillGAN 增强的图像具有显着改进，灵敏度 （SEN） 提高了 4.62%，准确性 （ACC） 提高了 3.97%。在多中心临床环境中，眼科技术人员和医生更喜欢 UWF-Net 增强图像，并显着缩短了诊断时间 （UWF-Net 增强图像为 （13.17 ± 8.40） 秒，原始图像为 （19.54 ± 12.40） 秒）和诊断准确性提高 （87.UWF-Net 增强图像为 71%，原始图像为 80.40%）。我们的研究验证 UWF-Net 显着提高了 UWF 成像的质量，促进了更好的临床结果和更可靠的 AI 辅助疾病分类。UWF-Net 的临床集成在增强眼科诊断过程和患者护理方面具有很大的前景。