The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera im...The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera imaging,single-phase FFA from scanning laser ophthalmoscopy(SLO),and three-phase FFA also from SLO.Although many deep learning models are available,a single model can only perform one or two of these prediction tasks.To accomplish three prediction tasks using a unified method,we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network.The three prediction tasks are processed as follows:data preparation,network training under FFA supervision,and FFA image prediction from fundus structure images on a test set.By comparing the FFA images predicted by our model,pix2pix,and CycleGAN,we demonstrate the remarkable progress achieved by our proposal.The high performance of our model is validated in terms of the peak signal-to-noise ratio,structural similarity index,and mean squared error.展开更多
Background:A variety of experimental animal models are used in basic ophthalmological research to elucidate physiological mechanisms of vision and disease pathogenesis.The choice of animal model is based on the measur...Background:A variety of experimental animal models are used in basic ophthalmological research to elucidate physiological mechanisms of vision and disease pathogenesis.The choice of animal model is based on the measurability of specific parameters or structures,the applicability of clinical measurement technologies,and the similarity to human eye function.Studies of eye pathology usually compare optical parameters between a healthy and altered state,so accurate baseline assessments are critical,but few reports have comprehensively examined the normal anatomical structures and physiological functions in these models.Methods:Three cynomolgus monkeys,six New Zealand rabbits,ten Sprague Dawley(SD)rats,and BALB/c mice were examined by fundus photography(FP),fundus fluorescein angiography(FFA),and optical coherence tomography(OCT).Results:Most retinal structures of cynomolgus monkey were anatomically similar to the corresponding human structures as revealed by FP,FFA,and OCT.New Zealand rabbits have large eyeballs,but they have large optic disc and myelinated retinal nerve fibers in their retinas,and the growth pattern of retinal vessels were also different to the human retinas.Unlike monkeys and rabbits,the retinal vessels of SD rats and BALB/c mice were widely distributed and clear.The OCT performance of them were similar with human beings except the macular.Conclusions:Monkey is a good model to study changes in retinal structure associated with fundus disease,rabbits are not suitable for studies on retinal vessel diseases and optic nerve diseases,and rats and mice are good models for retinal vascular diseases.These measures will help guide the choice of model and measurement technology and reduce the number of experimental animals required.展开更多
The eye is an immune-privileged and sensory organ in humans and animals.Anatomical,physiological,and pathobiological features share significant similarities across divergent species(1).Each compartment of the eye has ...The eye is an immune-privileged and sensory organ in humans and animals.Anatomical,physiological,and pathobiological features share significant similarities across divergent species(1).Each compartment of the eye has a unique structure and function.The anterior and posterior compartments of the eye contain endothelium(cornea),epithelium(cornea,ciliary body,iris),muscle(ciliary body),vitreous and neuronal(retina)tissues,which make the eye suitable to evaluate efficacy and safety of tissue specific drugs(2).展开更多
BACKGROUND Early screening and accurate staging of diabetic retinopathy(DR)can reduce blindness risk in type 2 diabetes patients.DR’s complex pathogenesis involves many factors,making ophthalmologist screening alone ...BACKGROUND Early screening and accurate staging of diabetic retinopathy(DR)can reduce blindness risk in type 2 diabetes patients.DR’s complex pathogenesis involves many factors,making ophthalmologist screening alone insufficient for prevention and treatment.Often,endocrinologists are the first to see diabetic patients and thus should screen for retinopathy for early intervention.AIM To explore the efficacy of non-mydriatic fundus photography(NMFP)-enhanced telemedicine in assessing DR and its various stages.METHODS This retrospective study incorporated findings from an analysis of 93 diabetic patients,examining both NMFP-assisted telemedicine and fundus fluorescein angiography(FFA).It focused on assessing the concordance in DR detection between these two methodologies.Additionally,receiver operating characteristic(ROC)curves were generated to determine the optimal sensitivity and specificity of NMFP-assisted telemedicine,using FFA outcomes as the standard benchmark.RESULTS In the context of DR diagnosis and staging,the kappa coefficients for NMFPassisted telemedicine and FFA were recorded at 0.775 and 0.689 respectively,indicating substantial intermethod agreement.Moreover,the NMFP-assisted telemedicine’s predictive accuracy for positive FFA outcomes,as denoted by the area under the ROC curve,was remarkably high at 0.955,within a confidence interval of 0.914 to 0.995 and a statistically significant P-value of less than 0.001.This predictive model exhibited a specificity of 100%,a sensitivity of 90.9%,and a Youden index of 0.909.CONCLUSION NMFP-assisted telemedicine represents a pragmatic,objective,and precise modality for fundus examination,particularly applicable in the context of endocrinology inpatient care and primary healthcare settings for diabetic patients.Its implementation in these scenarios is of paramount significance,enhancing the clinical accuracy in the diagnosis and therapeutic management of DR.This methodology not only streamlines patient evaluation but also contributes substantially to the optimization of clinical outcomes in DR management.展开更多
基金supported in part by the Gusu Innovation and Entrepreneurship Leading Talents in Suzhou City,grant numbers ZXL2021425 and ZXL2022476Doctor of Innovation and Entrepreneurship Program in Jiangsu Province,grant number JSSCBS20211440+6 种基金Jiangsu Province Key R&D Program,grant number BE2019682Natural Science Foundation of Jiangsu Province,grant number BK20200214National Key R&D Program of China,grant number 2017YFB0403701National Natural Science Foundation of China,grant numbers 61605210,61675226,and 62075235Youth Innovation Promotion Association of Chinese Academy of Sciences,grant number 2019320Frontier Science Research Project of the Chinese Academy of Sciences,grant number QYZDB-SSW-JSC03Strategic Priority Research Program of the Chinese Academy of Sciences,grant number XDB02060000.
文摘The prediction of fundus fluorescein angiography(FFA)images from fundus structural images is a cutting-edge research topic in ophthalmological image processing.Prediction comprises estimating FFA from fundus camera imaging,single-phase FFA from scanning laser ophthalmoscopy(SLO),and three-phase FFA also from SLO.Although many deep learning models are available,a single model can only perform one or two of these prediction tasks.To accomplish three prediction tasks using a unified method,we propose a unified deep learning model for predicting FFA images from fundus structure images using a supervised generative adversarial network.The three prediction tasks are processed as follows:data preparation,network training under FFA supervision,and FFA image prediction from fundus structure images on a test set.By comparing the FFA images predicted by our model,pix2pix,and CycleGAN,we demonstrate the remarkable progress achieved by our proposal.The high performance of our model is validated in terms of the peak signal-to-noise ratio,structural similarity index,and mean squared error.
基金This study was funded by Science and Technology Projects of Guangdong Province(Nos.2019A030317002,2017A030303013,2013B060300003).
文摘Background:A variety of experimental animal models are used in basic ophthalmological research to elucidate physiological mechanisms of vision and disease pathogenesis.The choice of animal model is based on the measurability of specific parameters or structures,the applicability of clinical measurement technologies,and the similarity to human eye function.Studies of eye pathology usually compare optical parameters between a healthy and altered state,so accurate baseline assessments are critical,but few reports have comprehensively examined the normal anatomical structures and physiological functions in these models.Methods:Three cynomolgus monkeys,six New Zealand rabbits,ten Sprague Dawley(SD)rats,and BALB/c mice were examined by fundus photography(FP),fundus fluorescein angiography(FFA),and optical coherence tomography(OCT).Results:Most retinal structures of cynomolgus monkey were anatomically similar to the corresponding human structures as revealed by FP,FFA,and OCT.New Zealand rabbits have large eyeballs,but they have large optic disc and myelinated retinal nerve fibers in their retinas,and the growth pattern of retinal vessels were also different to the human retinas.Unlike monkeys and rabbits,the retinal vessels of SD rats and BALB/c mice were widely distributed and clear.The OCT performance of them were similar with human beings except the macular.Conclusions:Monkey is a good model to study changes in retinal structure associated with fundus disease,rabbits are not suitable for studies on retinal vessel diseases and optic nerve diseases,and rats and mice are good models for retinal vascular diseases.These measures will help guide the choice of model and measurement technology and reduce the number of experimental animals required.
文摘The eye is an immune-privileged and sensory organ in humans and animals.Anatomical,physiological,and pathobiological features share significant similarities across divergent species(1).Each compartment of the eye has a unique structure and function.The anterior and posterior compartments of the eye contain endothelium(cornea),epithelium(cornea,ciliary body,iris),muscle(ciliary body),vitreous and neuronal(retina)tissues,which make the eye suitable to evaluate efficacy and safety of tissue specific drugs(2).
基金Supported by the Project of National Natural Science Foundation of China,No.82270863Major Project of Anhui Provincial University Research Program,No.2023AH040400Joint Fund for Medical Artificial Intelligence,No.MAI2023Q026.
文摘BACKGROUND Early screening and accurate staging of diabetic retinopathy(DR)can reduce blindness risk in type 2 diabetes patients.DR’s complex pathogenesis involves many factors,making ophthalmologist screening alone insufficient for prevention and treatment.Often,endocrinologists are the first to see diabetic patients and thus should screen for retinopathy for early intervention.AIM To explore the efficacy of non-mydriatic fundus photography(NMFP)-enhanced telemedicine in assessing DR and its various stages.METHODS This retrospective study incorporated findings from an analysis of 93 diabetic patients,examining both NMFP-assisted telemedicine and fundus fluorescein angiography(FFA).It focused on assessing the concordance in DR detection between these two methodologies.Additionally,receiver operating characteristic(ROC)curves were generated to determine the optimal sensitivity and specificity of NMFP-assisted telemedicine,using FFA outcomes as the standard benchmark.RESULTS In the context of DR diagnosis and staging,the kappa coefficients for NMFPassisted telemedicine and FFA were recorded at 0.775 and 0.689 respectively,indicating substantial intermethod agreement.Moreover,the NMFP-assisted telemedicine’s predictive accuracy for positive FFA outcomes,as denoted by the area under the ROC curve,was remarkably high at 0.955,within a confidence interval of 0.914 to 0.995 and a statistically significant P-value of less than 0.001.This predictive model exhibited a specificity of 100%,a sensitivity of 90.9%,and a Youden index of 0.909.CONCLUSION NMFP-assisted telemedicine represents a pragmatic,objective,and precise modality for fundus examination,particularly applicable in the context of endocrinology inpatient care and primary healthcare settings for diabetic patients.Its implementation in these scenarios is of paramount significance,enhancing the clinical accuracy in the diagnosis and therapeutic management of DR.This methodology not only streamlines patient evaluation but also contributes substantially to the optimization of clinical outcomes in DR management.
