Evidence map›Paper›PMID 34997031›Full record

ArticleScientific reports2022

Identifying diabetes from conjunctival images using a novel hierarchical multi-task network.

Xinyue Li, Chenjie Xia, Xin Li, Shuangqing Wei, Sujun Zhou, Xuhui Yu, Jiayue Gao, Yanpeng Cao, Hong Zhang

Open access · goldAbstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.0field-weighted citation impact, top 27% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 5 institutions in 2 countries.

Xinyue Li *Eye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, 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 LiSchool of Electrical Engineering and Computer Science, 2002 Digital Media Center, Louisiana State University, 340 E. Parker Blvd, Baton Rouge, LA, 70803, USA.
Shuangqing WeiSchool of Electrical Engineering and Computer Science, 2002 Digital Media Center, Louisiana State University, 340 E. Parker Blvd, Baton Rouge, LA, 70803, USA.
Sujun ZhouEye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China.
Xuhui YuEye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China.
Jiayue GaoEye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China.
Yanpeng CaoState 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. caoyp@zju.edu.cn.
Hong ZhangEye Hospital, The First Affiliated Hospital of Harbin Medical University, No.143, Yiman Street, Nangang District, Harbin City, 150001, Heilongjiang Province, China. zhanghong@hrbmu.edu.cn.
Louisiana State University · USFirst Affiliated Hospital of Harbin Medical University · CNZhejiang University · CNEducation Department of Heilongjiang Province · CNHarbin Medical University · CN

Funding

Higher Education Reform Project of Heilongjiang Province, China SJGY20180332National Natural Science Foundation of China U20A20363Natural Science Foundation of Heilongjiang Province, China LH2020H039
6 · The paper itself

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.

Indexed as

AlgorithmsDiagnosis, Computer-AssistedDiagnostic Techniques, OphthalmologicalImage Interpretation, Computer-AssistedCase-Control StudiesConjunctivaDiabetes Mellitus, Type 2Diabetic AngiopathiesHumansMicrovesselsPredictive Value of TestsProspective StudiesReproducibility of Results

Identifiers

PMID34997031
PMCPMC8742044
OpenAlexW4205471335

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.