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Fully automated grading system for the evaluation of punctate epithelial erosions using deep neural networks

文献类型：期刊论文

作者Qu, Jing-Hao2,3; Qin, Xiao-Ran1; Li, Chen-Di2,3; Peng, Rong-Mei2,3; Xiao, Ge-Ge2,3; Cheng, Jian1; Gu, Shao-Feng2,3; Wang, Hai-Kun2,3; Hong, Jing2,3
刊名BRITISH JOURNAL OF OPHTHALMOLOGY
出版日期2021-10-20
页码8
ISSN号0007-1161
关键词cornea imaging
DOI10.1136/bjophthalmol-2021-319755
通讯作者Hong, Jing(hongjing196401@163.com)
英文摘要Purpose The goal was to develop a fully automated grading system for the evaluation of punctate epithelial erosions (PEEs) using deep neural networks. Methods A fully automated system was developed to detect corneal position and grade staining severity given a corneal fluorescein staining image. The fully automated pipeline consists of the following three steps: a corneal segmentation model extracts corneal area; five image patches are cropped from the staining image based on the five subregions of extracted cornea; a staining grading model predicts a score for each image patch from 0 to 3, and automated grading score for the whole cornea is obtained from 0 to 15. Finally, the clinical grading scores annotated by three ophthalmologists were compared with automated grading scores. Results For corneal segmentation, the segmentation model achieved an intersection over union of 0.937. For punctate staining grading, the grading model achieved a classification accuracy of 76.5% and an area under the receiver operating characteristic curve of 0.940 (95% CI 0.932 to 0.949). For the fully automated pipeline, Pearson's correlation coefficient between the clinical and automated grading scores was 0.908 (p<0.01). Bland-Altman analysis revealed 95% limits of agreement between the clinical and automated grading scores of between -4.125 and 3.720 (concordance correlation coefficient=0.904). The average time required for processing a single stained image during pipeline was 0.58 s. Conclusion A fully automated grading system was developed to evaluate PEEs. The grading results may serve as a reference for ophthalmologists in clinical trials and residency training procedures.
WOS关键词CORNEAL ; QUANTIFICATION
资助项目National Natural Science Foundation of China[81970768] ; National Natural Science Foundation of China[81800801] ; China National Key Research and Development Program[2020AAA0105004]
WOS研究方向Ophthalmology
语种英语
出版者BMJ PUBLISHING GROUP
WOS记录号WOS:000721845700001
资助机构National Natural Science Foundation of China ; China National Key Research and Development Program
源URL[http://ir.ia.ac.cn/handle/173211/46491]  
专题类脑芯片与系统研究
通讯作者Hong, Jing
作者单位1.Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Inst Automat, Beijing, Peoples R China
2.Peking Univ Third Hosp, Dept Ophthalmol, Beijing, Peoples R China
3.Peking Univ Third Hosp, Beijing Key Lab Restorat Damaged Ocular Nerve, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Qu, Jing-Hao,Qin, Xiao-Ran,Li, Chen-Di,et al. Fully automated grading system for the evaluation of punctate epithelial erosions using deep neural networks[J]. BRITISH JOURNAL OF OPHTHALMOLOGY,2021:8.
APA Qu, Jing-Hao.,Qin, Xiao-Ran.,Li, Chen-Di.,Peng, Rong-Mei.,Xiao, Ge-Ge.,...&Hong, Jing.(2021).Fully automated grading system for the evaluation of punctate epithelial erosions using deep neural networks.BRITISH JOURNAL OF OPHTHALMOLOGY,8.
MLA Qu, Jing-Hao,et al."Fully automated grading system for the evaluation of punctate epithelial erosions using deep neural networks".BRITISH JOURNAL OF OPHTHALMOLOGY (2021):8.

入库方式： OAI收割

来源：自动化研究所

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