Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos
Abstract Background To study the association between dynamic iris change and primary angle-closure disease (PACD) with anterior segment optical coherence tomography (AS-OCT) videos and develop an automated deep learning system for angle-closure screening as well as validate its performance. Methods...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-11-01
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| Series: | Eye and Vision |
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| Online Access: | https://doi.org/10.1186/s40662-022-00314-1 |
| _version_ | 1828095000773132288 |
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| author | Luoying Hao Yan Hu Yanwu Xu Huazhu Fu Hanpei Miao Ce Zheng Jiang Liu |
| author_facet | Luoying Hao Yan Hu Yanwu Xu Huazhu Fu Hanpei Miao Ce Zheng Jiang Liu |
| author_sort | Luoying Hao |
| collection | DOAJ |
| description | Abstract Background To study the association between dynamic iris change and primary angle-closure disease (PACD) with anterior segment optical coherence tomography (AS-OCT) videos and develop an automated deep learning system for angle-closure screening as well as validate its performance. Methods A total of 369 AS-OCT videos (19,940 frames)—159 angle-closure subjects and 210 normal controls (two datasets using different AS-OCT capturing devices)—were included. The correlation between iris changes (pupil constriction) and PACD was analyzed based on dynamic clinical parameters (pupil diameter) under the guidance of a senior ophthalmologist. A temporal network was then developed to learn discriminative temporal features from the videos. The datasets were randomly split into training, and test sets and fivefold stratified cross-validation were used to evaluate the performance. Results For dynamic clinical parameter evaluation, the mean velocity of pupil constriction (VPC) was significantly lower in angle-closure eyes (0.470 mm/s) than in normal eyes (0.571 mm/s) (P < 0.001), as was the acceleration of pupil constriction (APC, 3.512 mm/s2 vs. 5.256 mm/s2; P < 0.001). For our temporal network, the areas under the curve of the system using AS-OCT images, original AS-OCT videos, and aligned AS-OCT videos were 0.766 (95% CI: 0.610–0.923) vs. 0.820 (95% CI: 0.680–0.961) vs. 0.905 (95% CI: 0.802–1.000) (for Casia dataset) and 0.767 (95% CI: 0.620–0.914) vs. 0.837 (95% CI: 0.713–0.961) vs. 0.919 (95% CI: 0.831–1.000) (for Zeiss dataset). Conclusions The results showed, comparatively, that the iris of angle-closure eyes stretches less in response to illumination than in normal eyes. Furthermore, the dynamic feature of iris motion could assist in angle-closure classification. |
| first_indexed | 2024-04-11T07:07:02Z |
| format | Article |
| id | doaj.art-9b50799d331749a88eec7f1132c83800 |
| institution | Directory Open Access Journal |
| issn | 2326-0254 |
| language | English |
| last_indexed | 2024-04-11T07:07:02Z |
| publishDate | 2022-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Eye and Vision |
| spelling | doaj.art-9b50799d331749a88eec7f1132c838002022-12-22T04:38:22ZengBMCEye and Vision2326-02542022-11-019111010.1186/s40662-022-00314-1Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videosLuoying Hao0Yan Hu1Yanwu Xu2Huazhu Fu3Hanpei Miao4Ce Zheng5Jiang Liu6Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and TechnologyResearch Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and TechnologyIntelligent Healthcare UnitInstitute of High Performance Computing, Agency for Science, Technology and ResearchResearch Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and TechnologyDepartment of Ophthalmology, Xinhua Hospital, Shanghai Jiaotong University School of MedicineResearch Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and TechnologyAbstract Background To study the association between dynamic iris change and primary angle-closure disease (PACD) with anterior segment optical coherence tomography (AS-OCT) videos and develop an automated deep learning system for angle-closure screening as well as validate its performance. Methods A total of 369 AS-OCT videos (19,940 frames)—159 angle-closure subjects and 210 normal controls (two datasets using different AS-OCT capturing devices)—were included. The correlation between iris changes (pupil constriction) and PACD was analyzed based on dynamic clinical parameters (pupil diameter) under the guidance of a senior ophthalmologist. A temporal network was then developed to learn discriminative temporal features from the videos. The datasets were randomly split into training, and test sets and fivefold stratified cross-validation were used to evaluate the performance. Results For dynamic clinical parameter evaluation, the mean velocity of pupil constriction (VPC) was significantly lower in angle-closure eyes (0.470 mm/s) than in normal eyes (0.571 mm/s) (P < 0.001), as was the acceleration of pupil constriction (APC, 3.512 mm/s2 vs. 5.256 mm/s2; P < 0.001). For our temporal network, the areas under the curve of the system using AS-OCT images, original AS-OCT videos, and aligned AS-OCT videos were 0.766 (95% CI: 0.610–0.923) vs. 0.820 (95% CI: 0.680–0.961) vs. 0.905 (95% CI: 0.802–1.000) (for Casia dataset) and 0.767 (95% CI: 0.620–0.914) vs. 0.837 (95% CI: 0.713–0.961) vs. 0.919 (95% CI: 0.831–1.000) (for Zeiss dataset). Conclusions The results showed, comparatively, that the iris of angle-closure eyes stretches less in response to illumination than in normal eyes. Furthermore, the dynamic feature of iris motion could assist in angle-closure classification.https://doi.org/10.1186/s40662-022-00314-1AS-OCT videosAngle-closureIris changeGlaucomaDeep learning |
| spellingShingle | Luoying Hao Yan Hu Yanwu Xu Huazhu Fu Hanpei Miao Ce Zheng Jiang Liu Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos Eye and Vision AS-OCT videos Angle-closure Iris change Glaucoma Deep learning |
| title | Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos |
| title_full | Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos |
| title_fullStr | Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos |
| title_full_unstemmed | Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos |
| title_short | Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos |
| title_sort | dynamic analysis of iris changes and a deep learning system for automated angle closure classification based on as oct videos |
| topic | AS-OCT videos Angle-closure Iris change Glaucoma Deep learning |
| url | https://doi.org/10.1186/s40662-022-00314-1 |
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