ArticleInternational journal of ophthalmology2023
Evaluation of a novel deep learning based screening system for pathologic myopia.
Article in International journal of ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for pathologic myopia classification based on the META-PM system: a systematic review and meta-analysis.Frontiers in physiology · 2026Pooled it
- Artificial intelligence in pathologic myopia: a review of clinical research studies.Frontiers in medicine · 2025Pooled it
- Artificial intelligence applications in refractive error management: A systematic review and meta-analysis.PLOS digital health · 2025Article
- Keypoint localization and parameter measurement in ultrasound biomicroscopy anterior segment images based on deep learning.Biomedical engineering online · 2025Article
- Revealing age-related changes in the intraocular microenvironment and senescence modulators using aqueous humor proteomics and machine learning.Frontiers in cell and developmental biology · 2025Article
- A study on the variability and correlation of ocular biological measurement parameters in adult myopic patients.Frontiers in medicine · 2024Article
- Quantitative analysis of retinal vascular parameters changes in school-age children with refractive error using artificial intelligence.Frontiers in medicine · 2024Article
- Quantitative analysis of optic disc changes in school-age children with ametropia based on artificial intelligence.International journal of ophthalmology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo evaluate the clinical application value of the artificial intelligence assisted pathologic myopia (PM-AI) diagnosis model based on deep learning.
methodsA total of 1156 readable color fundus photographs were collected and annotated based on the diagnostic criteria of Meta-pathologic myopia (PM) (2015). The PM-AI system and four eye doctors (retinal specialists 1 and 2, and ophthalmologists 1 and 2) independently evaluated the color fundus photographs to determine whether they were indicative of PM or not and the presence of myopic choroidal neovascularization (mCNV). The performance of identification for PM and mCNV by the PM-AI system and the eye doctors was compared and evaluated
resultsFor PM identification, the sensitivity of the PM-AI system was 98.17%, which was comparable to specialist 1 (
conclusionIn comparison to the senior ophthalmologists, the PM-AI system based on deep learning exhibits excellent performance in PM and mCNV identification. The effectiveness of PM-AI system is an auxiliary diagnosis tool for clinical screening of PM and mCNV.
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