Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection
Abstract By 2040, ~100 million people will have glaucoma. To date, there are a lack of high-efficiency glaucoma diagnostic tools based on visual fields (VFs). Herein, we develop and evaluate the performance of ‘iGlaucoma’, a smartphone application-based deep learning system (DLS) in detecting glauco...
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Nature Portfolio
2020
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oai:doaj.org-article:481e146d7ee14aa8bb8e0910e851c4af2021-12-02T15:15:22ZDevelopment and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection10.1038/s41746-020-00329-92398-6352https://doaj.org/article/481e146d7ee14aa8bb8e0910e851c4af2020-09-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-00329-9https://doaj.org/toc/2398-6352Abstract By 2040, ~100 million people will have glaucoma. To date, there are a lack of high-efficiency glaucoma diagnostic tools based on visual fields (VFs). Herein, we develop and evaluate the performance of ‘iGlaucoma’, a smartphone application-based deep learning system (DLS) in detecting glaucomatous VF changes. A total of 1,614,808 data points of 10,784 VFs (5542 patients) from seven centers in China were included in this study, divided over two phases. In Phase I, 1,581,060 data points from 10,135 VFs of 5105 patients were included to train (8424 VFs), validate (598 VFs) and test (3 independent test sets—200, 406, 507 samples) the diagnostic performance of the DLS. In Phase II, using the same DLS, iGlaucoma cloud-based application further tested on 33,748 data points from 649 VFs of 437 patients from three glaucoma clinics. With reference to three experienced expert glaucomatologists, the diagnostic performance (area under curve [AUC], sensitivity and specificity) of the DLS and six ophthalmologists were evaluated in detecting glaucoma. In Phase I, the DLS outperformed all six ophthalmologists in the three test sets (AUC of 0.834–0.877, with a sensitivity of 0.831–0.922 and a specificity of 0.676–0.709). In Phase II, iGlaucoma had 0.99 accuracy in recognizing different patterns in pattern deviation probability plots region, with corresponding AUC, sensitivity and specificity of 0.966 (0.953–0.979), 0.954 (0.930–0.977), and 0.873 (0.838–0.908), respectively. The ‘iGlaucoma’ is a clinically effective glaucoma diagnostic tool to detect glaucoma from humphrey VFs, although the target population will need to be carefully identified with glaucoma expertise input.Fei LiDiping SongHan ChenJian XiongXingyi LiHua ZhongGuangxian TangSujie FanDennis S. C. LamWeihua PanYajuan ZhengYing LiGuoxiang QuJunjun HeZhe WangLing JinRouxi ZhouYunhe SongYi SunWeijing ChengChunman YangYazhi FanYingjie LiHengli ZhangYe YuanYang XuYunfan XiongLingfei JinAiguo LvLingzhi NiuYuhong LiuShaoli LiJiani ZhangLinda M. ZangwillAlejandro F. FrangiTin AungChing-yu ChengYu QiaoXiulan ZhangDaniel S. W. TingNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-8 (2020) |
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DOAJ |
| collection |
DOAJ |
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EN |
| topic |
Computer applications to medicine. Medical informatics R858-859.7 |
| spellingShingle |
Computer applications to medicine. Medical informatics R858-859.7 Fei Li Diping Song Han Chen Jian Xiong Xingyi Li Hua Zhong Guangxian Tang Sujie Fan Dennis S. C. Lam Weihua Pan Yajuan Zheng Ying Li Guoxiang Qu Junjun He Zhe Wang Ling Jin Rouxi Zhou Yunhe Song Yi Sun Weijing Cheng Chunman Yang Yazhi Fan Yingjie Li Hengli Zhang Ye Yuan Yang Xu Yunfan Xiong Lingfei Jin Aiguo Lv Lingzhi Niu Yuhong Liu Shaoli Li Jiani Zhang Linda M. Zangwill Alejandro F. Frangi Tin Aung Ching-yu Cheng Yu Qiao Xiulan Zhang Daniel S. W. Ting Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection |
| description |
Abstract By 2040, ~100 million people will have glaucoma. To date, there are a lack of high-efficiency glaucoma diagnostic tools based on visual fields (VFs). Herein, we develop and evaluate the performance of ‘iGlaucoma’, a smartphone application-based deep learning system (DLS) in detecting glaucomatous VF changes. A total of 1,614,808 data points of 10,784 VFs (5542 patients) from seven centers in China were included in this study, divided over two phases. In Phase I, 1,581,060 data points from 10,135 VFs of 5105 patients were included to train (8424 VFs), validate (598 VFs) and test (3 independent test sets—200, 406, 507 samples) the diagnostic performance of the DLS. In Phase II, using the same DLS, iGlaucoma cloud-based application further tested on 33,748 data points from 649 VFs of 437 patients from three glaucoma clinics. With reference to three experienced expert glaucomatologists, the diagnostic performance (area under curve [AUC], sensitivity and specificity) of the DLS and six ophthalmologists were evaluated in detecting glaucoma. In Phase I, the DLS outperformed all six ophthalmologists in the three test sets (AUC of 0.834–0.877, with a sensitivity of 0.831–0.922 and a specificity of 0.676–0.709). In Phase II, iGlaucoma had 0.99 accuracy in recognizing different patterns in pattern deviation probability plots region, with corresponding AUC, sensitivity and specificity of 0.966 (0.953–0.979), 0.954 (0.930–0.977), and 0.873 (0.838–0.908), respectively. The ‘iGlaucoma’ is a clinically effective glaucoma diagnostic tool to detect glaucoma from humphrey VFs, although the target population will need to be carefully identified with glaucoma expertise input. |
| format |
article |
| author |
Fei Li Diping Song Han Chen Jian Xiong Xingyi Li Hua Zhong Guangxian Tang Sujie Fan Dennis S. C. Lam Weihua Pan Yajuan Zheng Ying Li Guoxiang Qu Junjun He Zhe Wang Ling Jin Rouxi Zhou Yunhe Song Yi Sun Weijing Cheng Chunman Yang Yazhi Fan Yingjie Li Hengli Zhang Ye Yuan Yang Xu Yunfan Xiong Lingfei Jin Aiguo Lv Lingzhi Niu Yuhong Liu Shaoli Li Jiani Zhang Linda M. Zangwill Alejandro F. Frangi Tin Aung Ching-yu Cheng Yu Qiao Xiulan Zhang Daniel S. W. Ting |
| author_facet |
Fei Li Diping Song Han Chen Jian Xiong Xingyi Li Hua Zhong Guangxian Tang Sujie Fan Dennis S. C. Lam Weihua Pan Yajuan Zheng Ying Li Guoxiang Qu Junjun He Zhe Wang Ling Jin Rouxi Zhou Yunhe Song Yi Sun Weijing Cheng Chunman Yang Yazhi Fan Yingjie Li Hengli Zhang Ye Yuan Yang Xu Yunfan Xiong Lingfei Jin Aiguo Lv Lingzhi Niu Yuhong Liu Shaoli Li Jiani Zhang Linda M. Zangwill Alejandro F. Frangi Tin Aung Ching-yu Cheng Yu Qiao Xiulan Zhang Daniel S. W. Ting |
| author_sort |
Fei Li |
| title |
Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection |
| title_short |
Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection |
| title_full |
Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection |
| title_fullStr |
Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection |
| title_full_unstemmed |
Development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection |
| title_sort |
development and clinical deployment of a smartphone-based visual field deep learning system for glaucoma detection |
| publisher |
Nature Portfolio |
| publishDate |
2020 |
| url |
https://doaj.org/article/481e146d7ee14aa8bb8e0910e851c4af |
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