Evidence map›Paper›PMID 36333758›Full record

ArticleEye and vision (London, England)2022

Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos.

Luoying Hao, Yan Hu, Yanwu Xu, Huazhu Fu, Hanpei Miao, Ce Zheng, Jiang Liu

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Eye and vision (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.0field-weighted citation impact, top 14% 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

4 citing papers in PubMed, 10 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

7 authors at 5 institutions in 3 countries.

Luoying Hao *Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Yan Hu *Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.ORCID http://orcid.org/0000-0002-7663-3096
Yanwu XuIntelligent Healthcare Unit, Baidu, Beijing, China.
Huazhu FuInstitute of High Performance Computing, Agency for Science, Technology and Research, Singapore, Singapore.
Hanpei MiaoResearch Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China.
Ce ZhengDepartment of Ophthalmology, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China. zhengce@xinhuamed.com.cn.
Jiang LiuResearch Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China. liuj@sustech.edu.cn.
Southern University of Science and Technology · CNAgency for Science, Technology and Research · SGBaidu (China) · CNWenzhou Medical University · CNXinHua Hospital · CN

Funding

Guangdong Basic and Applied Basic Research Foundation 2021A1515012195Guangdong Provincial Department of Education 2020ZDZX3043Guangdong Provincial Key Laboratory 2020B121201001Hospital Funded Clinical Research, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine 21XJMR02the National Natural Science Foundation of China 8210072776The Science and Technology Innovation Committee of Shenzhen City 20200925174052004The Science and Technology Innovation Committee of Shenzhen City JCYJ20200109140820699
6 · The paper itself

Abstract

backgroundTo study the association between dynamic iris change and primary angle-closure disease (PACD) with anterior segment optical coherence tomography (AS-OCT) videos and develop an automated deep learning system for angle-closure screening as well as validate its performance.

methodsA total of 369 AS-OCT videos (19,940 frames)-159 angle-closure subjects and 210 normal controls (two datasets using different AS-OCT capturing devices)-were included. The correlation between iris changes (pupil constriction) and PACD was analyzed based on dynamic clinical parameters (pupil diameter) under the guidance of a senior ophthalmologist. A temporal network was then developed to learn discriminative temporal features from the videos. The datasets were randomly split into training, and test sets and fivefold stratified cross-validation were used to evaluate the performance.

resultsFor dynamic clinical parameter evaluation, the mean velocity of pupil constriction (VPC) was significantly lower in angle-closure eyes (0.470 mm/s) than in normal eyes (0.571 mm/s) (P < 0.001), as was the acceleration of pupil constriction (APC, 3.512 mm/s

conclusionsThe results showed, comparatively, that the iris of angle-closure eyes stretches less in response to illumination than in normal eyes. Furthermore, the dynamic feature of iris motion could assist in angle-closure classification.

Indexed as

Angle-closureAS-OCT videosDeep learningGlaucomaIris change

Identifiers

PMID36333758
PMCPMC9636810
OpenAlexW4308339714

What OpenQuestion holds

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Registered trials

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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.