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...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 11294 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.展开更多
目的:探讨准分子激光原位角膜磨镶术(laser in situ keratomileusis,LASIK)后影响OrbscanⅡ中角膜后表面前凸量(后表面Diff值)的主要因素。方法:选取120例240眼近视患者眼行LASIK术,应用Orbscan-Ⅱ角膜地形图于术前和术后1,3,6,12mo分...目的:探讨准分子激光原位角膜磨镶术(laser in situ keratomileusis,LASIK)后影响OrbscanⅡ中角膜后表面前凸量(后表面Diff值)的主要因素。方法:选取120例240眼近视患者眼行LASIK术,应用Orbscan-Ⅱ角膜地形图于术前和术后1,3,6,12mo分别检测术前和术后角膜后表面Diff值,统计分析影响术后Diff值的主要因素。结果:逐步回归方程分析,切削百分比和术前角膜后表面Diff值是术后各时期Diff值的影响因素,术前眼压也是影响术后1mo时Diff值的因素之一。结论:认为LASIK术前重视角膜后表面前凸量,并在保证手术效果的情况下控制切削百分比,同时在术后严密监测眼压,必要时降低患眼眼压,可以尽量避免术后角膜扩张、屈光回退等并发症的发生。展开更多
基金supported by the National Natural Science Foundation of China(82020108006 and 81730025 to Chen Zhao,U2001209 to Bo Yan)the Excellent Academic Leaders of Shanghai(18XD1401000 to Chen Zhao)the Natural Science Foundation of Shanghai,China(21ZR1406600 to Weimin Tan).
文摘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 11294 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.
文摘目的:探讨准分子激光原位角膜磨镶术(laser in situ keratomileusis,LASIK)后影响OrbscanⅡ中角膜后表面前凸量(后表面Diff值)的主要因素。方法:选取120例240眼近视患者眼行LASIK术,应用Orbscan-Ⅱ角膜地形图于术前和术后1,3,6,12mo分别检测术前和术后角膜后表面Diff值,统计分析影响术后Diff值的主要因素。结果:逐步回归方程分析,切削百分比和术前角膜后表面Diff值是术后各时期Diff值的影响因素,术前眼压也是影响术后1mo时Diff值的因素之一。结论:认为LASIK术前重视角膜后表面前凸量,并在保证手术效果的情况下控制切削百分比,同时在术后严密监测眼压,必要时降低患眼眼压,可以尽量避免术后角膜扩张、屈光回退等并发症的发生。
