Category: 2019, No. 08

Guidelines for artificial intelligent diabetic retinopathy screening system based on fundus photography

Authors:Intelligent Medicine Special Committee of China Medicine Education Association,  National Key Research and Development Program of China “Development and Application of Ophthalmic Multimodal Imaging and Artificial Intelligence Diagnosis and Treatment System” Project Team DOI: 10.3760/cma.j.issn.2095-0160.2019.08.001 Published 2019-08-10 Cite as Chin J Exp Ophthalmol, 2019,37(8): 593-598. Abstract Artificial intelligence (AI) aided diagnosis technology based onmedical big data […]

Attach importance to the opportunities and challenges facing the development of ophthalmic artificial intelligence in China

Authors:Yuan Jin,  Li Meng DOI: 10.3760/cma.j.issn.2095-0160.2019.08.002 Published 2019-08-10 Cite as Chin J Exp Ophthalmol, 2019,37(8): 599-602. Abstract The combination of “artificial intelligence (AI)+ medical” provides new ideas for improving medical quality and innovating clinical diagnosis and treatment modes.The research of AI in ophthalmology is in the ascendant, however, the current challenges of ophthalmic AI research remains: […]

Diabetic retinopathy detection algorithm based on transfer learning

Authors:Huang Yijin,  Lyu Junyan,  Li Meng,  Xia Honghui,  Yuan Jin,  Tang Xiaoying DOI: 10.3760/cma.j.issn.2095-0160.2019.08.003 Published 2019-08-10 Cite as Chin J Exp Ophthalmol, 2019,37(8): 603-607. Abstract Objective To investigate a diabetic retinopathy (DR) detection algorithm based on transfer learning in small sample dataset. Methods Total of 4 465 fundus color photographs taken by Gaoyao People’s Hospital was […]

Retinal image quality assessment based on FA-Net

Authors:Wan Cheng,  You Qijing,  Sun Jing,  Shen Jianxin,  Yu Qiuli DOI: 10.3760/cma.j.issn.2095-0160.2019.08.004 Published 2019-08-10 Cite as Chin J Exp Ophthalmol, 2019,37(8): 608-612. Abstract Objective To propose a deep learning-based retinal image quality classification network, FA-Net, to make convolutional neural network (CNN) more suitable for image quality assessment in eye disease screening system. Methods The main network […]

Diabetic retinopathy fundus image generation based on generative adversarial networks

Authors:Wan Cheng,  Zhou Peng,  Wu Luhui,  Wu Yiquan,  Shen Jianxin,  Ye Hui DOI: 10.3760/cma.j.issn.2095-0160.2019.08.005 Published 2019-08-10 Cite as Chin J Exp Ophthalmol, 2019,37(8): 613-618. DOI: 10.3760/cma.j.issn.2095-0160.2019.08.005 Abstract Objective To generate various types of diabetic retinopathy (DR) fundus images automatically by computer vision algorithm. Methods A method based on deep learning to generate fundus images was proposed, which […]

Multi-channel conditional generative adversarial networks retinal vessel segmentation algorithm

Authors:Wan Cheng,  Wang Yikuang,  Xu Peiyuan,  Shen Jianxin,  Chen Zhiqiang DOI: 10.3760/cma.j.issn.2095-0160.2019.08.006 Published 2019-08-10 Cite as Chin J Exp Ophthalmol, 2019,37(8): 619-623. Abstract Objective To propose a model for accurately segmenting blood vessels in medical fundus images. Methods The algorithm of deep learning was used for the task of automatic segmentation of blood vessels in retinal […]

A novel lesion detection algorithm based on multi-scale input convolutional neural network model for diabetic retinopathy

Authors:Yang Yehui,  Liu Jia,  Xu Yanwu,  Huang Yan,  Wang Lei DOI: 10.3760/cma.j.issn.2095-0160.2019.08.007 Published 2019-08-10 Cite as Chin J Exp Ophthalmol, 2019,37(8): 624-629. Abstract Objective To propose a multi-scale convolutional neural network (CNN) based lesions detection method of fundus image, and evaluate its application in diabetic retinopathy (DR) assisted diagnosis. Methods A multi-scale CNN based on lesions […]

Screening and grading of fundus images of diabetic retinopathy based on visual attention

Authors:Wan Jialong,  Hu Jianbin,  Jin Weidong,  Tang Peng DOI: 10.3760/cma.j.issn.2095-0160.2019.08.008 Published 2019-08-10 Cite as Chin J Exp Ophthalmol, 2019,37(8): 630-637. Abstract Objective To construct an intelligent analysis system based on visual attention for diabetic retinopathy (DR) assistant diagnosis and to realize the automatic screening and grading of fundus images of DR. Methods Total of 35 126 […]

Objective analysis of corneal subbasal nerve tortuosity and its changes in patients with dry eye and diabetes

Authors:Ma Baikai,  Zhao Kun,  Ma Siyi,  Liu Rongjun,  Gao Yufei,  Hu Chenxi,  Xie Jianyang,  Liu Yiyun,  Zhao Yitian,  Qi Hong DOI: 10.3760/cma.j.issn.2095-0160.2019.08.009 Published 2019-08-10 Cite as Chin J Exp Ophthalmol, 2019,37(8): 638-644. Abstract Objective To construct an objective analysis system of corneal nerve tortuosity and detect the changes of corneal subbasal nerve tortuosity in patients with […]

Automated assisted clinical diagnosis of retinopathy of prematurity based on deep learning

Authors:Tong Yan,  Lu Wei,  Xu Yangtao,  Li Ying,  Wang Xiaoling,  Chen Changzheng,  Shen Yin DOI: 10.3760/cma.j.issn.2095-0160.2019.08.011 Published 2019-08-10 Cite as Chin J Exp Ophthalmol, 2019,37(8): 647-651. Abstract Objective To evaluate the application value of an intelligent fundus assisted diagnosis system for detecting retinopathy of prematurity (ROP) based on deep learning. Methods A total of 38 895 […]