Literature DB >> 33785508

Development and evaluation of a deep learning model for the detection of multiple fundus diseases based on colour fundus photography.

Youxin Chen1,2, Weihong Yu1,2, Bing Li3,2, Huan Chen3,2, Bilei Zhang3,2, Mingzhen Yuan4, Xuemin Jin5, Bo Lei6, Jie Xu4, Wei Gu7, David Chuen Soong Wong8, Xixi He9, Hao Wang9, Dayong Ding9, Xirong Li10.   

Abstract

AIM: To explore and evaluate an appropriate deep learning system (DLS) for the detection of 12 major fundus diseases using colour fundus photography.
METHODS: Diagnostic performance of a DLS was tested on the detection of normal fundus and 12 major fundus diseases including referable diabetic retinopathy, pathologic myopic retinal degeneration, retinal vein occlusion, retinitis pigmentosa, retinal detachment, wet and dry age-related macular degeneration, epiretinal membrane, macula hole, possible glaucomatous optic neuropathy, papilledema and optic nerve atrophy. The DLS was developed with 56 738 images and tested with 8176 images from one internal test set and two external test sets. The comparison with human doctors was also conducted.
RESULTS: The area under the receiver operating characteristic curves of the DLS on the internal test set and the two external test sets were 0.950 (95% CI 0.942 to 0.957) to 0.996 (95% CI 0.994 to 0.998), 0.931 (95% CI 0.923 to 0.939) to 1.000 (95% CI 0.999 to 1.000) and 0.934 (95% CI 0.929 to 0.938) to 1.000 (95% CI 0.999 to 1.000), with sensitivities of 80.4% (95% CI 79.1% to 81.6%) to 97.3% (95% CI 96.7% to 97.8%), 64.6% (95% CI 63.0% to 66.1%) to 100% (95% CI 100% to 100%) and 68.0% (95% CI 67.1% to 68.9%) to 100% (95% CI 100% to 100%), respectively, and specificities of 89.7% (95% CI 88.8% to 90.7%) to 98.1% (95%CI 97.7% to 98.6%), 78.7% (95% CI 77.4% to 80.0%) to 99.6% (95% CI 99.4% to 99.8%) and 88.1% (95% CI 87.4% to 88.7%) to 98.7% (95% CI 98.5% to 99.0%), respectively. When compared with human doctors, the DLS obtained a higher diagnostic sensitivity but lower specificity.
CONCLUSION: The proposed DLS is effective in diagnosing normal fundus and 12 major fundus diseases, and thus has much potential for fundus diseases screening in the real world. © Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  diagnostic tests/investigation; imaging; retina

Mesh:

Year:  2021        PMID: 33785508     DOI: 10.1136/bjophthalmol-2020-316290

Source DB:  PubMed          Journal:  Br J Ophthalmol        ISSN: 0007-1161            Impact factor:   5.908


  2 in total

1.  Artificial Intelligence for Screening of Multiple Retinal and Optic Nerve Diseases.

Authors:  Li Dong; Wanji He; Ruiheng Zhang; Zongyuan Ge; Ya Xing Wang; Jinqiong Zhou; Jie Xu; Lei Shao; Qian Wang; Yanni Yan; Ying Xie; Lijian Fang; Haiwei Wang; Yenan Wang; Xiaobo Zhu; Jinyuan Wang; Chuan Zhang; Heng Wang; Yining Wang; Rongtian Chen; Qianqian Wan; Jingyan Yang; Wenda Zhou; Heyan Li; Xuan Yao; Zhiwen Yang; Jianhao Xiong; Xin Wang; Yelin Huang; Yuzhong Chen; Zhaohui Wang; Ce Rong; Jianxiong Gao; Huiliang Zhang; Shouling Wu; Jost B Jonas; Wen Bin Wei
Journal:  JAMA Netw Open       Date:  2022-05-02

2.  A cascade eye diseases screening system with interpretability and expandability in ultra-wide field fundus images: A multicentre diagnostic accuracy study.

Authors:  Jing Cao; Kun You; Jingxin Zhou; Mingyu Xu; Peifang Xu; Lei Wen; Shengzhan Wang; Kai Jin; Lixia Lou; Yao Wang; Juan Ye
Journal:  EClinicalMedicine       Date:  2022-09-05
  2 in total

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