Evidence map›Paper›PMID 34751742›Full record

ArticleTranslational vision science & technology2021

Automated Analysis of Choroidal Sublayer Morphologic Features in Myopic Children Using EDI-OCT by Deep Learning.

Junmeng Li, Lei Zhu, Ruilin Zhu, Yanye Lu, Xin Rong, Yadi Zhang, Xiaopeng Gu, Yuwei Wang, Zhiyue Zhang, Qiushi Ren and 2 more

Open access · goldAbstract read
In one paragraph

Article in Translational vision science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
3.4field-weighted citation impact, top 7% 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

16 citing papers in PubMed, 1 synthesis or guideline pooled it, 28 citations in OpenAlex.

  1. Pooled it
  2. Artificial intelligence in pediatric myopia - a narrative review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Insights into artificial intelligence in myopia management: from a data perspective.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024
    Review
  10. Article
  11. Article
  12. Article
  13. Artificial intelligence technology for myopia challenges: A review.Frontiers in cell and developmental biology · 2023
    Review
  14. Advances in OCT Imaging in Myopia and Pathologic Myopia.Diagnostics (Basel, Switzerland) · 2022
    Review
  15. Article
  16. 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

12 authors at 2 institutions in 1 country.

Junmeng LiDepartment of Ophthalmology, Peking University First Hospital, Beijing, China.
Lei ZhuInstitute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China.
Ruilin ZhuDepartment of Ophthalmology, Peking University First Hospital, Beijing, China.
Yanye LuInstitute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, China.
Xin RongDepartment of Ophthalmology, Peking University First Hospital, Beijing, China.
Yadi ZhangDepartment of Ophthalmology, Peking University First Hospital, Beijing, China.
Xiaopeng GuDepartment of Ophthalmology, Peking University First Hospital, Beijing, China.
Yuwei WangDepartment of Ophthalmology, Peking University First Hospital, Beijing, China.
Zhiyue ZhangDepartment of Ophthalmology, Peking University First Hospital, Beijing, China.
Qiushi RenDepartment of Biomedical Engineering, Peking University, Beijing, China.
Bei RongDepartment of Ophthalmology, Peking University First Hospital, Beijing, China.
Liu YangDepartment of Ophthalmology, Peking University First Hospital, Beijing, China.
Peking University · CNPeking University First Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The purpose of this study was to analyze the choroidal sublayer morphologic features in emmetropic and myopic children using an automatic segmentation model, and to explore the relationship between choroidal sublayers and spherical equivalent refraction (SER). Methods: We collected data on 92 healthy children (92 eyes) from the Ophthalmology Department of Peking University First Hospital. The data were allocated to three groups: emmetropia (+0.50 diopters [D] to -0.50 D), low myopia (-0.75 D to -3.00 D), and moderate myopia (-3.25 D to -5.75 D). We performed standardized optical coherence tomography (OCT) and developed a new segmentation technique to measure choroidal thickness (CT), large-vessel choroidal layer (LVCL), medium-vessel choroidal layer (MVCL), and small-vessel choroidal layer thickness (SVCL), and evaluated the choroidal vascular system (choroidal vascular volume [VV], choroidal vascular index [CVI], and choroidal vascular density [CVD]). Results: All choroidal sublayers (LVCL, MVCL, and SVCL) were significantly thinner in myopic than in emmetropic eyes (P < 0.05), the thinnest choroidal region being the nasal outer subfield (P < 0.05). In all choroidal regions of SVCL, a positive correlation was found between SER and thickness ratio (P < 0.001). In most subfields of MVCL, a similar correlation was found (P < 0.050), the exceptions being the two nasal subfields (0.050 < P < 0.300). In contrast, the thickness ratio of LVCL decreased in all subfields (P < 0.050). VV correlated with SER negatively in LVCL in all subfields (all P < 0.001) and most subfields in MVCL except for two temporal subfields (0.050 < P < 0.200). However, no significant correlations were found between CVI and SER in LVCL (P > 0.050) and MVCL (with the exception being the temporal inner subfield, P = 0.011). Conclusions: Thickness of choroidal sublayers was reduced with higher myopic SER, whereas changes in thickness ratio varied between sublayers. No significant correlations between CVI and SER suggested that both choroidal stromal and vascular volume decreases proportionately. Translational Relevance: Automatic segmentation model will be helpful for future clinical trials to quantify choroidal sublayer morphologic features in myopia.

Indexed as

Deep LearningMyopiaChildChoroidEmmetropiaHumansTomography, Optical Coherence

Identifiers

PMID34751742
PMCPMC8590176
OpenAlexW3213096532

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.