Evidence map›Paper›PMID 32973990›Full record

ArticleTherapeutic advances in chronic disease2020

Application of non-mydriatic fundus examination and artificial intelligence to promote the screening of diabetic retinopathy in the endocrine clinic: an observational study of T2DM patients in Tianjin, China.

Zhaohu Hao, Shanshan Cui, Yanjuan Zhu, Hailin Shao, Xiao Huang, Xia Jiang, Rong Xu, Baocheng Chang, Huanming Li

Open access · goldAbstract read
In one paragraph

Article in Therapeutic advances in chronic disease, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.3field-weighted citation impact, top 36% 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

6 citing papers in PubMed, 8 citations in OpenAlex.

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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 3 institutions in 1 country.

Zhaohu HaoNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin, China.ORCID https://orcid.org/0000-0002-3965-3805
Shanshan CuiNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin, China.
Yanjuan ZhuNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin, China.
Hailin ShaoDepartment of Metabolic Disease Management Center, Tianjin 4th Central Hospital, The 4th Central Hospital Affiliated to Nankai University, The 4th Center Clinical College of Tianjin Medical University, Tianjin, China.
Xiao HuangNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin, China.
Xia JiangDepartment of Endocrinology, Tianjin First Central Hospital, The First Center Clinical College of Tianjin Medical University, Tianjin, China.
Rong XuDepartment of Metabolic Disease Management Center, Tianjin 4th Central Hospital, The 4th Central Hospital Affiliated to Nankai University, The 4th Center Clinical College of Tianjin Medical University, Tianjin, China.
Baocheng ChangNHC Key Laboratory of Hormones and Development (Tianjin Medical University), Tianjin Key Laboratory of Metabolic Diseases, Tianjin Medical University Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin 300134, China.ORCID https://orcid.org/0000-0002-6674-6644
Huanming LiDepartment of Metabolic Disease Management Center, Tianjin 4th Central Hospital, The 4th Central Hospital Affiliated to Nankai University, The 4th Center Clinical College of Tianjin Medical University, No. 1 Zhongshan Road, Tianjin 300140, China.
Nankai University · CNTianjin Medical University · CNTianjin First Center Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe aimed to determine the role of non-mydriatic fundus examination and artificial intelligence (AI) in screening diabetic retinopathy (DR) in patients with diabetes in the Metabolic Disease Management Center (MMC) in Tianjin, China.

methodsAdult patients with type 2 diabetes mellitus who were first treated by MMC in Tianjin First Central Hospital and Tianjin 4th Center Hospital were divided into two groups according to the time that MMC was equipped with the non-mydriatic ophthalmoscope and AI system and could complete fundus examination independently (the former was the control group, the latter was the observation group). The observation indices were as follows: the incidence of DR, the fundus screening rate of the two groups, and fundus screening of diabetic patients with different course of disease.

resultsA total of 5039 patients were enrolled in this study. The incidence rate of DR was 18.6%, 29.8%, and 49.6% in patients with diabetes duration of ⩽1 year, 1-5 years, and >5 years, respectively. The screening rate of fundus in the observation group was significantly higher compared with the control group (81.3%

conclusionsIn the case of limited medical resources, MMC can carry out one-stop examination, treatment, and management of DR through non-mydratic fundus examination and AI assistance, thus incorporating the DR screening process into the endocrine clinic, so as to facilitate early diagnosis.

Indexed as

artificial intelligencediabetic retinopathyfundus screeningnon-mydratic fundus examination

Identifiers

PMID32973990
PMCPMC7491217
OpenAlexW3085227098

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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.