Evidence map›Paper›PMID 34751744›Full record

ArticleTranslational vision science & technology2021

Automatic Screening and Identifying Myopic Maculopathy on Optical Coherence Tomography Images Using Deep Learning.

Xin Ye, Jun Wang, Yiqi Chen, Zhe Lv, Shucheng He, Jianbo Mao, Jiahao Xu, Lijun Shen

Abstract read
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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 32 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 6 pooled it
–field-weighted citation impact
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

32 citing papers in PubMed, 6 syntheses or guidelines pooled it.

  1. Pooled it
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  7. Shaping the future of myopia: artificial intelligence for vitreoretinal complications of high and pathologic myopia.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
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  10. IMI-Instrumentation for Myopia Management.Investigative ophthalmology & visual science · 2025
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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

8 authors.

Xin YeSchool of Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Jun WangSchool of Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Yiqi ChenSchool of Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Zhe LvSchool of Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Shucheng HeSchool of Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Jianbo MaoSchool of Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Jiahao XuSchool of Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Lijun ShenSchool of Ophthalmology and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The purpose of this study was to engineer deep learning (DL) models that can identify myopic maculopathy in patients with high myopia based on optical coherence tomography (OCT) images. Methods: An artificial intelligence (AI) system was developed using 2342 qualified OCT macular images from 1041 patients with pathologic myopia admitted to the Affiliated Eye Hospital of Wenzhou Medical University (WMU). We adopted an ResNeSt101 architecture to train five independent models to identify the following five myopic maculopathies: macular choroidal thinning, macular Bruch membrane (BM) defects, subretinal hyper-reflective material (SHRM), myopic traction maculopathy (MTM), and dome-shaped macula (DSM). We tested the models with an independent test dataset that included 450 images obtained from 297 patients with high myopia. Focal loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index. The performance was quantified using the area under the receiver operating characteristic (AUC), sensitivity, specificity, and confusion matrix. Results: For the identification of myopic maculopathy, the AUCs of receiver operating characteristic (ROC) curves were 0.927 to 0.974 for 5 myopic maculopathies. Our AI system achieved sensitivities equal to or even better than those of junior retinal specialists (56.16-99.73%). The diagnosis of it is also interpretable that we provide visual explanations clearly via heatmaps. Conclusions: We developed a convolutional neural network (CNN)-based DL AI system for detection and classification of myopic maculopathy in patients with high myopia using OCT macular images. Our AI system achieved sensitivities equal to or even better than those of junior retinal specialists. Translational Relevance: This AI system can be widely applied in sophisticated situations in large-scale high myopia screening.

Indexed as

Deep LearningMacular DegenerationMyopiaArtificial IntelligenceHumansTomography, Optical Coherence

Identifiers

PMID34751744
PMCPMC8590175

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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.