AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally ...AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.展开更多
目的:采用color-pilot颜色校正软件校正比色片数码照片并分析其在色度空间中的位置变化。方法:采用Vita 3D Master比色板作为试件,在诊室内拍摄比色片数码照片,并运用color-pilot颜色校正软件进行校正,最后与暗箱内拍摄的相应比色片数...目的:采用color-pilot颜色校正软件校正比色片数码照片并分析其在色度空间中的位置变化。方法:采用Vita 3D Master比色板作为试件,在诊室内拍摄比色片数码照片,并运用color-pilot颜色校正软件进行校正,最后与暗箱内拍摄的相应比色片数码照片进行对比。结果:比较诊室内与暗箱内拍摄的比色片数码照片,发现两组照片L*值之间、a*值之间差异明显(P<0.05),b*值之间则无明显差异(P>0.05);运用color-pilot颜色校正软件校正诊室内拍摄的数码照片后,发现两组照片L*值之间的差异仍然存在(P<0.05),但差异缩小(较诊室内拍摄的数码照片,校正后的数码照片L*值更接近于暗箱内拍摄的数码照片L*值),而a*值之间无明显差异(P>0.05),b*值之间依然无明显差异(P>0.05)。二者在色度空间中的位置也较校正前更为靠近。计算暗箱内、诊室内、校正后3组照片的ΔE值发现,校正后与暗箱内数码照片此值最小。结论:color-pilot颜色校正软件校正比色片数码照片后,可以在一定程度上减小数码照片的色彩偏差。展开更多
基金Supported by Shenzhen Fund for Guangdong Provincial High-level Clinical Key Specialties(No.SZGSP014)Sanming Project of Medicine in Shenzhen(No.SZSM202011015)Shenzhen Science and Technology Planning Project(No.KCXFZ20211020163813019).
文摘AIM:To develop an artificial intelligence(AI)diagnosis model based on deep learning(DL)algorithm to diagnose different types of retinal vein occlusion(RVO)by recognizing color fundus photographs(CFPs).METHODS:Totally 914 CFPs of healthy people and patients with RVO were collected as experimental data sets,and used to train,verify and test the diagnostic model of RVO.All the images were divided into four categories[normal,central retinal vein occlusion(CRVO),branch retinal vein occlusion(BRVO),and macular retinal vein occlusion(MRVO)]by three fundus disease experts.Swin Transformer was used to build the RVO diagnosis model,and different types of RVO diagnosis experiments were conducted.The model’s performance was compared to that of the experts.RESULTS:The accuracy of the model in the diagnosis of normal,CRVO,BRVO,and MRVO reached 1.000,0.978,0.957,and 0.978;the specificity reached 1.000,0.986,0.982,and 0.976;the sensitivity reached 1.000,0.955,0.917,and 1.000;the F1-Sore reached 1.000,0.9550.943,and 0.887 respectively.In addition,the area under curve of normal,CRVO,BRVO,and MRVO diagnosed by the diagnostic model were 1.000,0.900,0.959 and 0.970,respectively.The diagnostic results were highly consistent with those of fundus disease experts,and the diagnostic performance was superior.CONCLUSION:The diagnostic model developed in this study can well diagnose different types of RVO,effectively relieve the work pressure of clinicians,and provide help for the follow-up clinical diagnosis and treatment of RVO patients.
文摘目的:采用color-pilot颜色校正软件校正比色片数码照片并分析其在色度空间中的位置变化。方法:采用Vita 3D Master比色板作为试件,在诊室内拍摄比色片数码照片,并运用color-pilot颜色校正软件进行校正,最后与暗箱内拍摄的相应比色片数码照片进行对比。结果:比较诊室内与暗箱内拍摄的比色片数码照片,发现两组照片L*值之间、a*值之间差异明显(P<0.05),b*值之间则无明显差异(P>0.05);运用color-pilot颜色校正软件校正诊室内拍摄的数码照片后,发现两组照片L*值之间的差异仍然存在(P<0.05),但差异缩小(较诊室内拍摄的数码照片,校正后的数码照片L*值更接近于暗箱内拍摄的数码照片L*值),而a*值之间无明显差异(P>0.05),b*值之间依然无明显差异(P>0.05)。二者在色度空间中的位置也较校正前更为靠近。计算暗箱内、诊室内、校正后3组照片的ΔE值发现,校正后与暗箱内数码照片此值最小。结论:color-pilot颜色校正软件校正比色片数码照片后,可以在一定程度上减小数码照片的色彩偏差。
基金国家自然科学基金(the National Natural Science Foundation of China under Grant No.60672072)浙江省自然科学基金( the NaturalScience Foundation of Zhejiang Province of China under Grant No.Y106505)宁波市科技攻关项目( the Special Science and Technolo-gy Foundation of Ningbo of China under Grant No.2005B100016) 。
