While the usage of digital ocular fundus image has been widespread in ophthalmology practice,the interpretation of the image has been still on the hands of the ophthalmologists which are quite costly.We explored a rob...While the usage of digital ocular fundus image has been widespread in ophthalmology practice,the interpretation of the image has been still on the hands of the ophthalmologists which are quite costly.We explored a robust deep learning system that detects three major ocular diseases:diabetic retinopathy(DR),glaucoma(GLC),and age-related macular degeneration(AMD).The proposed method is composed of two steps.First,an initial quality evaluation in the classification system is proposed to filter out poorquality images to enhance its performance,a technique that has not been explored previously.Second,the transfer learning technique is used with various convolutional neural networks(CNN)models that automatically learn a thousand features in the digital retinal image,and are based on those features for diagnosing eye diseases.Comparison performance of many models is conducted to find the optimal model which fits with fundus classification.Among the different CNN models,DenseNet-201 outperforms others with an area under the receiver operating characteristic curve of 0.99.Furthermore,the corresponding specificities for healthy,DR,GLC,andAMDpatients are found to be 89.52%,96.69%,89.58%,and 100%,respectively.These results demonstrate that the proposed method can reduce the time-consumption by automatically diagnosing multiple eye diseases using computer-aided assistance tools.展开更多
Purpose: To investigate the pathogenesis of a secondary iris cyst with an immunohistochemical method. Methods: Single observational case report. A pathologic specimen was obtained from a 5-year-old girl who was found ...Purpose: To investigate the pathogenesis of a secondary iris cyst with an immunohistochemical method. Methods: Single observational case report. A pathologic specimen was obtained from a 5-year-old girl who was found to have a secondary iris cyst. She had a history of previous penetrating ocular trauma and subsequent cataract surgery and pupilloplasty. Immunohistochemical staining with cytokeratin (CK) 19 and CK3 was used. Results: After immunohistochemical staining, the inner wall showed positive staining for CK19, which is specific for limbal, peripheral cornea and conjunctival epithelium, and negative staining for CK3, which is specific for corneal epithelium. Conclusions: With the aid of immunohistochemical analysis, a conjunctival epithelial origin was indicated, and pupilloplasty was identified as the causal event of the iris cyst while the possibility of primary iris cyst was ruled out.展开更多
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.NRF-2021R1A2C1010362)and the Soonchunhyang University Research Fund.
文摘While the usage of digital ocular fundus image has been widespread in ophthalmology practice,the interpretation of the image has been still on the hands of the ophthalmologists which are quite costly.We explored a robust deep learning system that detects three major ocular diseases:diabetic retinopathy(DR),glaucoma(GLC),and age-related macular degeneration(AMD).The proposed method is composed of two steps.First,an initial quality evaluation in the classification system is proposed to filter out poorquality images to enhance its performance,a technique that has not been explored previously.Second,the transfer learning technique is used with various convolutional neural networks(CNN)models that automatically learn a thousand features in the digital retinal image,and are based on those features for diagnosing eye diseases.Comparison performance of many models is conducted to find the optimal model which fits with fundus classification.Among the different CNN models,DenseNet-201 outperforms others with an area under the receiver operating characteristic curve of 0.99.Furthermore,the corresponding specificities for healthy,DR,GLC,andAMDpatients are found to be 89.52%,96.69%,89.58%,and 100%,respectively.These results demonstrate that the proposed method can reduce the time-consumption by automatically diagnosing multiple eye diseases using computer-aided assistance tools.
文摘Purpose: To investigate the pathogenesis of a secondary iris cyst with an immunohistochemical method. Methods: Single observational case report. A pathologic specimen was obtained from a 5-year-old girl who was found to have a secondary iris cyst. She had a history of previous penetrating ocular trauma and subsequent cataract surgery and pupilloplasty. Immunohistochemical staining with cytokeratin (CK) 19 and CK3 was used. Results: After immunohistochemical staining, the inner wall showed positive staining for CK19, which is specific for limbal, peripheral cornea and conjunctival epithelium, and negative staining for CK3, which is specific for corneal epithelium. Conclusions: With the aid of immunohistochemical analysis, a conjunctival epithelial origin was indicated, and pupilloplasty was identified as the causal event of the iris cyst while the possibility of primary iris cyst was ruled out.
