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Zhang J, Luo X, Li D, Peng Y, Gao G, Lei L, Gao M, Lu L, Xu Y, Yu T, Lin S, Ma Y, Yao C, Zou H. Evaluating imaging repeatability of fully self-service fundus photography within a community-based eye disease screening setting. Biomed Eng Online 2024; 23:32. [PMID: 38475784 DOI: 10.1186/s12938-024-01222-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
PURPOSE This study aimed to investigate the imaging repeatability of self-service fundus photography compared to traditional fundus photography performed by experienced operators. DESIGN Prospective cross-sectional study. METHODS In a community-based eye diseases screening site, we recruited 65 eyes (65 participants) from the resident population of Shanghai, China. All participants were devoid of cataract or any other conditions that could potentially compromise the quality of fundus imaging. Participants were categorized into fully self-service fundus photography or traditional fundus photography group. Image quantitative analysis software was used to extract clinically relevant indicators from the fundus images. Finally, a statistical analysis was performed to depict the imaging repeatability of fully self-service fundus photography. RESULTS There was no statistical difference in the absolute differences, or the extents of variation of the indicators between the two groups. The extents of variation of all the measurement indicators, with the exception of the optic cup area, were below 10% in both groups. The Bland-Altman plots and multivariate analysis results were consistent with results mentioned above. CONCLUSIONS The image repeatability of fully self-service fundus photography is comparable to that of traditional fundus photography performed by professionals, demonstrating promise in large-scale eye disease screening programs.
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Affiliation(s)
- Juzhao Zhang
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- National Clinical Research Center for Eye Diseases, Shanghai, China
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xuan Luo
- Songjiang Disease Control and Prevention Center, Shanghai, China
| | - Deshang Li
- Sijing Community Health Service Center, Shanghai, China
| | - Yajun Peng
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Guiling Gao
- Songjiang Disease Control and Prevention Center, Shanghai, China
| | - Liangwen Lei
- Sijing Community Health Service Center, Shanghai, China
| | - Meng Gao
- Sijing Community Health Service Center, Shanghai, China
| | - Lina Lu
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Yi Xu
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Tao Yu
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China
| | - Senlin Lin
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China.
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
| | - Yingyan Ma
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China.
- National Clinical Research Center for Eye Diseases, Shanghai, China.
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
| | - Chunxia Yao
- Songjiang Disease Control and Prevention Center, Shanghai, China.
| | - Haidong Zou
- Shanghai Eye Disease Prevention & Treatment Center/Shanghai Eye Hospital, School of Medicine, Tongji University, Shanghai, China.
- National Clinical Research Center for Eye Diseases, Shanghai, China.
- Shanghai Engineering Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China.
- Department of Ophthalmology, Shanghai General Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
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Yan J, Hao Z, Zhou R, Tang Y, Yang P, Liu K, Zhang W, Li X, Lu Y, Zeng X. A quantitative analysis method assisted by image features in laser-induced breakdown spectroscopy. Anal Chim Acta 2019; 1082:30-36. [PMID: 31472710 DOI: 10.1016/j.aca.2019.07.058] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Revised: 06/03/2019] [Accepted: 07/27/2019] [Indexed: 01/19/2023]
Abstract
The determination accuracy of alloying elements in high alloy steel is generally poor in laser-induced breakdown spectroscopy (LIBS) due to their matrix effect. To solve this problem, an image quantitative analysis (IQA) method was proposed and verified by determining nickel (Ni) in 17 stainless steel samples in this work. The results showed that the coefficient of determination (R2) was increased from 0.9833 of a conventional spectrum quantitative analysis (SQA) method to 0.9996 of the IQA method, and the average relative error of cross-validation (ARECV) and root mean squared error of cross-validation (RMSECV) were decreased from 56.80% and 1.0818 wt% to 15.93% and 0.9866 wt%, respectively. Besides, the determinations of chromium (Cr) and silicon (Si) demonstrated the generalization ability of the IQA. This study provides an effective approach to improving the quantitative performance of LIBS through the combination of image processing and computer vision technology.
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Affiliation(s)
- Jiujiang Yan
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan, Hubei, 430074, PR China
| | - Zhongqi Hao
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan, Hubei, 430074, PR China
| | - Ran Zhou
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan, Hubei, 430074, PR China
| | - Yun Tang
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan, Hubei, 430074, PR China
| | - Ping Yang
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan, Hubei, 430074, PR China
| | - Kun Liu
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan, Hubei, 430074, PR China
| | - Wen Zhang
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan, Hubei, 430074, PR China
| | - Xiangyou Li
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan, Hubei, 430074, PR China.
| | - Yongfeng Lu
- Department of Electrical and Computer Engineering, University of Nebraska, Lincoln, NE, 68588-0511, USA
| | - Xiaoyan Zeng
- Wuhan National Laboratory for Optoelectronics (WNLO), Huazhong University of Science and Technology (HUST), Wuhan, Hubei, 430074, PR China
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