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Li X, Xia C, Li X, Wei S, Zhou S, Yu X, Gao J, Cao Y, Zhang H. Identifying diabetes from conjunctival images using a novel hierarchical multi-task network. Sci Rep 2022; 12:264. [PMID: 34997031 PMCID: PMC8742044 DOI: 10.1038/s41598-021-04006-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 12/06/2021] [Indexed: 11/15/2022] Open
Abstract
Diabetes can cause microvessel impairment. However, these conjunctival pathological changes are not easily recognized, limiting their potential as independent diagnostic indicators. Therefore, we designed a deep learning model to explore the relationship between conjunctival features and diabetes, and to advance automated identification of diabetes through conjunctival images. Images were collected from patients with type 2 diabetes and healthy volunteers. A hierarchical multi-tasking network model (HMT-Net) was developed using conjunctival images, and the model was systematically evaluated and compared with other algorithms. The sensitivity, specificity, and accuracy of the HMT-Net model to identify diabetes were 78.70%, 69.08%, and 75.15%, respectively. The performance of the HMT-Net model was significantly better than that of ophthalmologists. The model allowed sensitive and rapid discrimination by assessment of conjunctival images and can be potentially useful for identifying diabetes.
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Affiliation(s)
- Xinyue Li
- Eye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China
- Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, 150001, China
- Eye Department, Shanghai Children 's Hospital, Shanghai Jiaotong University, Shanghai, China
| | - Chenjie Xia
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Room 230, Building 1, Yuquan Campus, 38 Zhe Da Road, Hangzhou, 310027, Zhejiang Province, China
| | - Xin Li
- School of Electrical Engineering and Computer Science, 2002 Digital Media Center, Louisiana State University, 340 E. Parker Blvd, Baton Rouge, LA, 70803, USA
| | - Shuangqing Wei
- School of Electrical Engineering and Computer Science, 2002 Digital Media Center, Louisiana State University, 340 E. Parker Blvd, Baton Rouge, LA, 70803, USA
| | - Sujun Zhou
- Eye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China
- Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, 150001, China
| | - Xuhui Yu
- Eye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China
- Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, 150001, China
| | - Jiayue Gao
- Eye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China
- Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, 150001, China
| | - Yanpeng Cao
- State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Room 230, Building 1, Yuquan Campus, 38 Zhe Da Road, Hangzhou, 310027, Zhejiang Province, China.
| | - Hong Zhang
- Eye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China.
- Key Laboratory of Basic and Clinical Research of Heilongjiang Province, Harbin, 150001, China.
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Babenko B, Mitani A, Traynis I, Kitade N, Singh P, Maa AY, Cuadros J, Corrado GS, Peng L, Webster DR, Varadarajan A, Hammel N, Liu Y. Detection of signs of disease in external photographs of the eyes via deep learning. Nat Biomed Eng 2022; 6:1370-1383. [PMID: 35352000 PMCID: PMC8963675 DOI: 10.1038/s41551-022-00867-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 02/15/2022] [Indexed: 01/14/2023]
Abstract
Retinal fundus photographs can be used to detect a range of retinal conditions. Here we show that deep-learning models trained instead on external photographs of the eyes can be used to detect diabetic retinopathy (DR), diabetic macular oedema and poor blood glucose control. We developed the models using eye photographs from 145,832 patients with diabetes from 301 DR screening sites and evaluated the models on four tasks and four validation datasets with a total of 48,644 patients from 198 additional screening sites. For all four tasks, the predictive performance of the deep-learning models was significantly higher than the performance of logistic regression models using self-reported demographic and medical history data, and the predictions generalized to patients with dilated pupils, to patients from a different DR screening programme and to a general eye care programme that included diabetics and non-diabetics. We also explored the use of the deep-learning models for the detection of elevated lipid levels. The utility of external eye photographs for the diagnosis and management of diseases should be further validated with images from different cameras and patient populations.
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Affiliation(s)
- Boris Babenko
- grid.420451.60000 0004 0635 6729Google Health, Palo Alto, CA USA
| | - Akinori Mitani
- grid.420451.60000 0004 0635 6729Google Health, Palo Alto, CA USA ,Artera, Mountain View, CA, USA
| | - Ilana Traynis
- Google Health via Advanced Clinical, Deerfield, IL USA
| | - Naho Kitade
- grid.420451.60000 0004 0635 6729Google Health, Palo Alto, CA USA
| | - Preeti Singh
- grid.420451.60000 0004 0635 6729Google Health, Palo Alto, CA USA
| | - April Y. Maa
- grid.189967.80000 0001 0941 6502Department of Ophthalmology, Emory University School of Medicine, Atlanta, GA USA ,grid.484324.d0000 0004 0420 9995Regional Telehealth Services, Technology-based Eye Care Services (TECS) Division, Veterans Integrated Service Network (VISN) 7, Decatur, GA USA
| | | | - Greg S. Corrado
- grid.420451.60000 0004 0635 6729Google Health, Palo Alto, CA USA
| | - Lily Peng
- grid.420451.60000 0004 0635 6729Google Health, Palo Alto, CA USA
| | - Dale R. Webster
- grid.420451.60000 0004 0635 6729Google Health, Palo Alto, CA USA
| | | | - Naama Hammel
- grid.420451.60000 0004 0635 6729Google Health, Palo Alto, CA USA
| | - Yun Liu
- grid.420451.60000 0004 0635 6729Google Health, Palo Alto, CA USA
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