Development and Validation of an Automatic Ultrawide-Field Fundus Imaging Enhancement System for Facilitating Clinical Diagnosis: A Cross-Sectional Multicenter Study

IF 10.1 1区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
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Abstract

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.
开发和验证自动超宽视野眼底成像增强系统以促进临床诊断:一项横断面多中心研究
在眼科领域,无论是临床实践还是人工智能(AI)辅助诊断,眼底图像的质量对于准确诊断都至关重要。尽管超宽视场(UWF)成像提供了广阔的视野,但伪彩色图像可能会掩盖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,这是一种将疾病特征考虑在内的复杂图像增强算法。复旦大学超宽视场图像(FDUWI)数据集包括 11 294 张 Optos 伪彩色图像和 2415 张蔡司真彩超宽视场图像,每张图像都有严格的注释,UWF-Net 将全局风格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,从而显著提高图像质量。定量和定性评估结果表明,UWF-Net 优于对比度受限自适应直方图均衡化(CLAHE)和结构与光照约束生成式对抗网络(StillGAN)等现有方法,能提供更优越的视网膜图像质量、更高的质量分数以及增强后保留的特征细节。在疾病分类任务中,经 UWF-Net 增强的图像在使用现有分类系统处理时,比经 StillGAN 增强的图像有明显改善,灵敏度(SEN)提高了 4.62%,准确度(ACC)提高了 3.97%。在多中心临床环境中,UWF-Net 增强图像受到眼科技术人员和医生的青睐,显著缩短了诊断时间(UWF-Net 增强图像为(13.17 ± 8.40)秒,原始图像为(19.54 ± 12.40)秒),提高了诊断准确性(UWF-Net 增强图像为 87.71%,原始图像为 80.40%)。我们的研究证实,UWF-Net 能显著提高 UWF 成像的质量,促进更好的临床结果和更可靠的人工智能辅助疾病分类。UWF-Net的临床整合为提高眼科诊断流程和患者护理带来了巨大希望。
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来源期刊
Engineering
Engineering Environmental Science-Environmental Engineering
自引率
1.60%
发文量
335
审稿时长
35 days
期刊介绍: Engineering, an international open-access journal initiated by the Chinese Academy of Engineering (CAE) in 2015, serves as a distinguished platform for disseminating cutting-edge advancements in engineering R&D, sharing major research outputs, and highlighting key achievements worldwide. The journal's objectives encompass reporting progress in engineering science, fostering discussions on hot topics, addressing areas of interest, challenges, and prospects in engineering development, while considering human and environmental well-being and ethics in engineering. It aims to inspire breakthroughs and innovations with profound economic and social significance, propelling them to advanced international standards and transforming them into a new productive force. Ultimately, this endeavor seeks to bring about positive changes globally, benefit humanity, and shape a new future.
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