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
Examining the contribution of hemispherical photographs in the understanding of Natural forest regeneration is very important in estimating the future forest structure, composition and to enforce conservation regulati...Examining the contribution of hemispherical photographs in the understanding of Natural forest regeneration is very important in estimating the future forest structure, composition and to enforce conservation regulations. This study sets out to examine the interaction between stump sprouting, LAI, site and canopy openness for the entire AKAK forest area and for the logging compartments;2013, 2015 and 2017 respectively. 49 sprouted stump were identified randonly. 20 m × 20 m plots were demarcated along a canopy gaps for each sprouted stump, the plots were established in such a manner that the sprouted stumps will be in the middle. For each of the selected 49 sprouted stump, indirect measurements of canopy cover were performed in the 49 plots of 20 m × 20 m (0.04 ha), giving a total of 1.96 ha of land covered. Galaxy S3 smartphone with a built-in Infinix ZERO 4 fish-eye lens with 198˚ view angle equidistant projection was used to take photos. The fish-eye lens was mounted on the phone camera and photograph were taken at a fixed height of 1.3 m. Results revealed that, the combine Principal Component Factor Analysis (2013, 2015 and 2017) of the correlation matrix for Sprout, Years, LAI 4%, LAI 5%, Canopy and Site openness, shows that factor 1 explained 62.6% of total variance while factor 2 explained 17.9% together explain 80.05% Communalities. For the year 2013, 2015 and 2017 respectively shows that there is a very strong correlation (p p < 0.0005) between LAI4 and LAI5.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Examining the contribution of hemispherical photographs in the understanding of Natural forest regeneration is very important in estimating the future forest structure, composition and to enforce conservation regulations. This study sets out to examine the interaction between stump sprouting, LAI, site and canopy openness for the entire AKAK forest area and for the logging compartments;2013, 2015 and 2017 respectively. 49 sprouted stump were identified randonly. 20 m × 20 m plots were demarcated along a canopy gaps for each sprouted stump, the plots were established in such a manner that the sprouted stumps will be in the middle. For each of the selected 49 sprouted stump, indirect measurements of canopy cover were performed in the 49 plots of 20 m × 20 m (0.04 ha), giving a total of 1.96 ha of land covered. Galaxy S3 smartphone with a built-in Infinix ZERO 4 fish-eye lens with 198˚ view angle equidistant projection was used to take photos. The fish-eye lens was mounted on the phone camera and photograph were taken at a fixed height of 1.3 m. Results revealed that, the combine Principal Component Factor Analysis (2013, 2015 and 2017) of the correlation matrix for Sprout, Years, LAI 4%, LAI 5%, Canopy and Site openness, shows that factor 1 explained 62.6% of total variance while factor 2 explained 17.9% together explain 80.05% Communalities. For the year 2013, 2015 and 2017 respectively shows that there is a very strong correlation (p p < 0.0005) between LAI4 and LAI5.
