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

doi:10.1136/bjophthalmol-2021-319755

	Fully automated grading system for the evaluation of punctate epithelial erosions using deep neural networks
	Qu, Jing-Hao 1,2; Qin, Xiao-Ran 3; Li, Chen-Di 1,2; Peng, Rong-Mei 1,2; Xiao, Ge-Ge 1,2; Cheng, Jian3 ; Gu, Shao-Feng 1,2; Wang, Hai-Kun 1,2; Hong, Jing 1,2
发表期刊	BRITISH JOURNAL OF OPHTHALMOLOGY
ISSN	0007-1161
	2021-10-20
页码	8
通讯作者	Hong, Jing(hongjing196401@163.com)
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.
关键词	cornea imaging
DOI	10.1136/bjophthalmol-2021-319755
关键词[WOS]	CORNEAL ; QUANTIFICATION
收录类别	SCI
语种	英语
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]
项目资助者	National Natural Science Foundation of China ; China National Key Research and Development Program
WOS研究方向	Ophthalmology
WOS类目	Ophthalmology
WOS记录号	WOS:000721845700001
出版者	BMJ PUBLISHING GROUP
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46492
专题	复杂系统认知与决策实验室_高效智能计算与学习
通讯作者	Hong, Jing
作者单位	1.Peking Univ Third Hosp, Dept Ophthalmol, Beijing, Peoples R China 2.Peking Univ Third Hosp, Beijing Key Lab Restorat Damaged Ocular Nerve, Beijing, Peoples R China 3.Chinese Acad Sci, Res Ctr Brain Inspired Intelligence, Inst Automat, 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.