ArticleCommunications biology2021
Development of deep learning-based detecting systems for pathologic myopia using retinal fundus images.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 6 of them syntheses that pooled it.
What it found
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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.
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.
Who cites it
18 citing papers in PubMed, 6 syntheses or guidelines pooled it, 44 citations in OpenAlex.
- Artificial intelligence for pathologic myopia classification based on the META-PM system: a systematic review and meta-analysis.Frontiers in physiology · 2026Pooled it
- Machine Learning Approaches in High Myopia: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Classification of neurodegenerative diseases using brain effective connectivity and machine learning techniques: a systematic review.Frontiers in neurology · 2025Pooled it
- Artificial intelligence in pathologic myopia: a review of clinical research studies.Frontiers in medicine · 2025Pooled it
- Performance of artificial intelligence for the detection of pathological myopia from colour fundus images: a systematic review and meta-analysis.Eye (London, England) · 2024Pooled it
- Performances of artificial intelligence in detecting pathologic myopia: a systematic review and meta-analysis.Eye (London, England) · 2023Pooled it
- Advancements in AI-based quantitative analysis of fundus tessellation and its application in myopia research.Frontiers in medicine · 2026Review
- Artificial intelligence applications in refractive error management: A systematic review and meta-analysis.PLOS digital health · 2025Article
- Self-supervised learning-enhanced deep learning method for identifying myopic maculopathy in high myopia patients.iScience · 2024Article
- Artificial intelligence in chorioretinal pathology through fundoscopy: a comprehensive review.International journal of retina and vitreous · 2024Review
- Development and validation of predictive models for myopia onset and progression using extensive 15-year refractive data in children and adolescents.Journal of translational medicine · 2024Article
- Initiation of China Alliance of Research in High Myopia (CHARM): protocol for an AI-based multimodal high myopia research biobank.BMJ open · 2023Article
- Evaluation of a novel deep learning based screening system for pathologic myopia.International journal of ophthalmology · 2023Article
- Artificial intelligence-aided diagnosis and treatment in the field of optometry.International journal of ophthalmology · 2023Article
- Development of a deep learning algorithm for myopic maculopathy classification based on OCT images using transfer learning.Frontiers in public health · 2022Article
- Artificial intelligence-based pathologic myopia identification system in the ophthalmology residency training program.Frontiers in cell and developmental biology · 2022Article
- Deep learning methods for improving the accuracy and efficiency of pathological image analysis.Science progressArticle
- Artificial intelligence and digital solutions for myopia.Taiwan journal of ophthalmologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 5 institutions in 1 country.
Funding
Abstract
Globally, cases of myopia have reached epidemic levels. High myopia and pathological myopia (PM) are the leading cause of visual impairment and blindness in China, demanding a large volume of myopia screening tasks to control the rapid growing myopic prevalence. It is desirable to develop the automatically intelligent system to facilitate these time- and labor- consuming tasks. In this study, we designed a series of deep learning systems to detect PM and myopic macular lesions according to a recent international photographic classification system (META-PM) classification based on color fundus images. Notably, our systems recorded robust performance both in the test and external validation dataset. The performance was comparable to the general ophthalmologist and retinal specialist. With the extensive adoption of this technology, effective mass screening for myopic population will become feasible on a national scale.
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What OpenQuestion holds
Registered trials
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.