Twenty-four cases (25 eyes) of fundus hemorrhage due to obstruction of the retinal vein were treated with traditional Chinese herbal drugs and satisfactory therapeutic results were obtained. A brief report is as f... Twenty-four cases (25 eyes) of fundus hemorrhage due to obstruction of the retinal vein were treated with traditional Chinese herbal drugs and satisfactory therapeutic results were obtained. A brief report is as follows.……展开更多
Pathological myopia(PM)is a severe ocular disease leading to blindness.As a traditional noninvasive diagnostic method,fundus color photography(FCP)is widely used in detecting PM due to its highfidelity and precision.H...Pathological myopia(PM)is a severe ocular disease leading to blindness.As a traditional noninvasive diagnostic method,fundus color photography(FCP)is widely used in detecting PM due to its highfidelity and precision.However,manual examination of fundus photographs for PM is time-consuming and prone to high error rates.Existing automated detection technologies have yet to study the detailed classification in diagnosing different stages of PM lesions.In this paper,we proposed an intelligent system which utilized Resnet101 technology to multi-categorically diagnose PM by classifying FCPs with different stages of lesions.The system subdivided different stages of PM into eight subcategories,aiming to enhance the precision and efficiency of the diagnostic process.It achieved an average accuracy rate of 98.86%in detection of PM,with an area under the curve(AUC)of 98.96%.For the eight subcategories of PM,the detection accuracy reached 99.63%,with an AUC of 99.98%.Compared with other widely used multi-class models such as VGG16,Vision Transformer(VIT),EfficientNet,this system demonstrates higher accuracy and AUC.This artificial intelligence system is designed to be easily integrated into existing clinical diagnostic tools,providing an efficient solution for large-scale PM screening.展开更多
Background:Myopic maculopathy(MM)has become a major cause of visual impairment and blindness worldwide,especially in East Asian countries.Deep learning approaches such as deep convolutional neural networks(DCNN)have b...Background:Myopic maculopathy(MM)has become a major cause of visual impairment and blindness worldwide,especially in East Asian countries.Deep learning approaches such as deep convolutional neural networks(DCNN)have been successfully applied to identify some common retinal diseases and show great potential for the intelligent analysis of MM.This study aimed to build a reliable approach for automated detection of MM from retinal fundus images using DCNN models.Methods:A dual-stream DCNN(DCNN-DS)model that perceives features from both original images and corresponding processed images by color histogram distribution optimization method was designed for classification of no MM,tessellated fundus(TF),and pathologic myopia(PM).A total of 36,515 gradable images from four hospitals were used for DCNN model development,and 14,986 gradable images from the other two hospitals for external testing.We also compared the performance of the DCNN-DS model and four ophthalmologists on 3000 randomly sampledfundus images.Results:The DCNN-DS model achieved sensitivities of 93.3%and 91.0%,specificities of 99.6%and 98.7%,areas under the receiver operating characteristic curves(AUCs)of 0.998 and 0.994 for detecting PM,whereas sensitivities of 98.8%and 92.8%,specificities of 95.6%and 94.1%,AUCs of 0.986 and 0.970 for detecting TF in two external testing datasets.In the sampled testing dataset,the sensitivities of four ophthalmologists ranged from 88.3%to 95.8%and 81.1%to 89.1%,and the specificities ranged from 95.9%to 99.2%and 77.8%to 97.3%for detecting PM and TF,respectively.Meanwhile,the DCNN-DS model achieved sensitivities of 90.8%and 97.9%and specificities of 99.1%and 94.0%for detecting PMand T,respectively.Conclusions:The proposed DCNN-DS approach demonstrated reliable performance with high sensitivity,specificity,and AUC to classify different MM levels on fundus photographs sourced from clinics.It can help identify MM automatically among the large myopic groups and show great potential for real-life applications.展开更多
文摘 Twenty-four cases (25 eyes) of fundus hemorrhage due to obstruction of the retinal vein were treated with traditional Chinese herbal drugs and satisfactory therapeutic results were obtained. A brief report is as follows.……
基金supported by the Natural National Science Foundation of China(62175156)the Science and technology innovation project of Shanghai Science and Technology Commission(22S31903000)Collaborative Innovation Project of Shanghai Institute of Technology(XTCX2022-27)。
文摘Pathological myopia(PM)is a severe ocular disease leading to blindness.As a traditional noninvasive diagnostic method,fundus color photography(FCP)is widely used in detecting PM due to its highfidelity and precision.However,manual examination of fundus photographs for PM is time-consuming and prone to high error rates.Existing automated detection technologies have yet to study the detailed classification in diagnosing different stages of PM lesions.In this paper,we proposed an intelligent system which utilized Resnet101 technology to multi-categorically diagnose PM by classifying FCPs with different stages of lesions.The system subdivided different stages of PM into eight subcategories,aiming to enhance the precision and efficiency of the diagnostic process.It achieved an average accuracy rate of 98.86%in detection of PM,with an area under the curve(AUC)of 98.96%.For the eight subcategories of PM,the detection accuracy reached 99.63%,with an AUC of 99.98%.Compared with other widely used multi-class models such as VGG16,Vision Transformer(VIT),EfficientNet,this system demonstrates higher accuracy and AUC.This artificial intelligence system is designed to be easily integrated into existing clinical diagnostic tools,providing an efficient solution for large-scale PM screening.
基金The research has been supported by the Qingdao Science and Technology Demonstration and Guidance Project(Grant No.20-3-4-45-nsh)Academic Promotion Plan of Shandong First Medical University&Shandong Academy of Medical Sciences(GrantNo.2019ZL001)National Science and Technology Major Project of China(Grant No.2017ZX09304010).
文摘Background:Myopic maculopathy(MM)has become a major cause of visual impairment and blindness worldwide,especially in East Asian countries.Deep learning approaches such as deep convolutional neural networks(DCNN)have been successfully applied to identify some common retinal diseases and show great potential for the intelligent analysis of MM.This study aimed to build a reliable approach for automated detection of MM from retinal fundus images using DCNN models.Methods:A dual-stream DCNN(DCNN-DS)model that perceives features from both original images and corresponding processed images by color histogram distribution optimization method was designed for classification of no MM,tessellated fundus(TF),and pathologic myopia(PM).A total of 36,515 gradable images from four hospitals were used for DCNN model development,and 14,986 gradable images from the other two hospitals for external testing.We also compared the performance of the DCNN-DS model and four ophthalmologists on 3000 randomly sampledfundus images.Results:The DCNN-DS model achieved sensitivities of 93.3%and 91.0%,specificities of 99.6%and 98.7%,areas under the receiver operating characteristic curves(AUCs)of 0.998 and 0.994 for detecting PM,whereas sensitivities of 98.8%and 92.8%,specificities of 95.6%and 94.1%,AUCs of 0.986 and 0.970 for detecting TF in two external testing datasets.In the sampled testing dataset,the sensitivities of four ophthalmologists ranged from 88.3%to 95.8%and 81.1%to 89.1%,and the specificities ranged from 95.9%to 99.2%and 77.8%to 97.3%for detecting PM and TF,respectively.Meanwhile,the DCNN-DS model achieved sensitivities of 90.8%and 97.9%and specificities of 99.1%and 94.0%for detecting PMand T,respectively.Conclusions:The proposed DCNN-DS approach demonstrated reliable performance with high sensitivity,specificity,and AUC to classify different MM levels on fundus photographs sourced from clinics.It can help identify MM automatically among the large myopic groups and show great potential for real-life applications.
