Development and Validation of an Automatic Ultrawide-Field Fundus Imaging Enhancement System for Facilitating Clinical Diagnosis: A Cross-Sectional Multicenter Study
Qiaoling Wei,
Zhuoyao Gu,
Weimin Tan,
Hongyu Kong,
Hao Fu,
Qin Jiang,
Wenjuan Zhuang,
Shaochi Zhang,
Lixia Feng,
Yong Liu,
Suyan Li,
Bing Qin,
Peirong Lu,
Jiangyue Zhao,
Zhigang Li,
Songtao Yuan,
Hong Yan,
Shujie Zhang,
Xiangjia Zhu,
Jiaxu Hong,
Chen Zhao,
Bo Yan
Affiliations
Qiaoling Wei
Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai 200231, China
Zhuoyao Gu
School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
Weimin Tan
School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
Hongyu Kong
Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai 200231, China
Hao Fu
School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
Qin Jiang
The Affiliated Eye Hospital of Nanjing Medical University, Nanjing 210029, China
Wenjuan Zhuang
Ningxia Eye Hospital, People’s Hospital of Ningxia Hui Autonomous Region, Third Clinical Medical College of Ningxia Medical University, Yinchuan 750002, China
Shaochi Zhang
Ningxia Eye Hospital, People’s Hospital of Ningxia Hui Autonomous Region, Third Clinical Medical College of Ningxia Medical University, Yinchuan 750002, China
Lixia Feng
Department of Ophthalmology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
Yong Liu
Southwest Hospital/Southwest Eye Hospital, Third Military Medical University (Army Medical University), Chongqing 400038, China; Key Lab of Visual Damage and Regeneration Restoration of Chongqing, Chongqing 400038, China
Suyan Li
Department of Ophthalmology, The Affiliated Xuzhou Municipal Hospital of Xuzhou Medical University, Xuzhou 221000, China
Bing Qin
Department of Ophthalmology, Suqian First Hospital of Jiangsu Province Hospital, Suqian 223800, China
Peirong Lu
Department of Ophthalmology, The First Affiliated Hospital of Soochow University, Suzhou 215006, China
Jiangyue Zhao
Department of Ophthalmology, The Fourth Affiliated Hospital of China Medical University, Eye Hospital of China Medical University, Shenyang 110000, China
Zhigang Li
Department of Ophthalmology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
Songtao Yuan
Department of Ophthalmology, Jiangsu Province Hospital, Nanjing 210029, China
Hong Yan
Shaanxi Eye Hospital, Xi’an People’s Hospital (Xi’an Fourth Hospital), The Affiliated People’s Hospital of Northwest University, Xi’an 710004, China
Shujie Zhang
Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai 200231, China
Xiangjia Zhu
Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai 200231, China
Jiaxu Hong
Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai 200231, China
Chen Zhao
Department of Ophthalmology, Eye & ENT Hospital of Fudan University, Shanghai 200231, China; Corresponding authors.
Bo Yan
School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China; Corresponding authors.
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.
