Performances of artificial intelligence in detecting pathologic myopia: a systematic review and meta-analysis
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Updated: Aug 1, 2025
Inducement and Evaluation of a Murine Model of Experimental Myopia
Published on: January 22, 2019
Yue Zhang1,2, Yilin Li3, Jing Liu1,2
1Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
Background/Objective:
Pathologic myopia (PM) is a major cause of severe visual impairment and blindness, and current applications of artificial intelligence (AI) have covered the diagnosis and classification of PM. This meta-analysis and systematic review aimed to evaluate the overall performance of AI-based models in detecting PM and related complications.
Methods:
We searched PubMed, Scopus, Embase, Web of Science and IEEE Xplore for eligible studies before Dec 20, 2022. The methodological quality of included studies was evaluated using the Quality Assessment for Diagnostic Accuracy Studies (QUADAS-2). We calculated the pooled sensitivity (SEN), specificity (SPE) and the summary area under the curve (AUC) using a random effects model, to evaluate the performance of AI in the detection of PM based on fundus or optical coherence tomography (OCT) images.
Results:
22 studies were included in the systematic review, and 14 of them were included in the quantitative analysis. Of all included studies, SEN and SPE ranged from 80.0% to 98.7% and from 79.5% to 100.0% for PM detection, respectively. For the detection of PM, the summary AUC was 0.99 (95% confidence interval (CI) 0.97 to 0.99), and the pooled SEN and SPE were 0.95 (95% CI 0.92 to 0.96) and 0.97 (95% CI: 0.94 to 0.98), respectively. For the detection of PM-related choroid neovascularization (CNV), the summary AUC was 0.99 (95% CI: 0.97 to 0.99).
Conclusion:
Our review demonstrated the excellent performance of current AI algorithms in detecting PM and related complications based on fundus and OCT images.
Insights
Artificial intelligence (AI) shows excellent performance in detecting pathologic myopia (PM) and its complications using fundus and OCT images. This systematic review confirms AI
Area of Science:
- Ophthalmology and Medical Imaging
- Artificial Intelligence in Healthcare
- Diagnostic Accuracy Studies
Background:
- Pathologic myopia (PM) is a leading cause of vision impairment and blindness.
- Existing AI applications focus on PM diagnosis and classification.
- This study evaluates AI performance in detecting PM and related complications.
Approach:
- Systematic review and meta-analysis of studies published before December 20, 2022.
- Searched major scientific databases (PubMed, Scopus, Embase, Web of Science, IEEE Xplore).
- Assessed study quality using QUADAS-2 and pooled diagnostic accuracy metrics (SEN, SPE, AUC) for AI models using fundus or OCT images.
Key Points:
- Included 22 studies, with 14 in quantitative analysis.
- AI demonstrated high sensitivity (0.95) and specificity (0.97) for PM detection, with an AUC of 0.99.
- AI achieved an AUC of 0.99 for detecting PM-related choroidal neovascularization (CNV).
Conclusions:
- AI algorithms exhibit excellent performance in detecting pathologic myopia.
- AI accurately identifies PM-related complications like CNV from fundus and OCT images.
- AI holds significant promise for improving the diagnosis and management of PM.
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