ArticleTranslational vision science & technology2022
An Artificial-Intelligence-Based Automated Grading and Lesions Segmentation System for Myopic Maculopathy Based on Color Fundus Photographs.
Article in Translational vision science & technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 5 of them syntheses that pooled 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.
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
14 citing papers in PubMed, 5 syntheses or guidelines pooled it, 33 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
- 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
- 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 · 2026Review
- IMI-Instrumentation for Myopia Management.Investigative ophthalmology & visual science · 2025Review
- Review
- A Novel Artificial Intelligence-Based Classification of Highly Myopic Eyes Based on Visual Function and Fundus Features.Translational vision science & technology · 2024Article
- Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm.International journal of ophthalmology · 2023Article
- Evaluation of a novel deep learning based screening system for pathologic myopia.International journal of ophthalmology · 2023Article
- Automated detection of myopic maculopathy using five-category models based on vision outlooker for visual recognition.Frontiers in computational neuroscience · 2023Article
- Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images.Frontiers in neuroscience · 2022Article
- 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
17 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To develop deep learning models based on color fundus photographs that can automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and segment myopia-related lesions. Methods: Photographs were graded and annotated by four ophthalmologists and were then divided into a high-consistency subgroup or a low-consistency subgroup according to the consistency between the results of the graders. ResNet-50 network was used to develop the classification model, and DeepLabv3+ network was used to develop the segmentation model for lesion identification. The two models were then combined to develop the classification-and-segmentation-based co-decision model. Results: This study included 1395 color fundus photographs from 895 patients. The grading accuracy of the co-decision model was 0.9370, and the quadratic-weighted κ coefficient was 0.9651; the co-decision model achieved an area under the receiver operating characteristic curve of 0.9980 in diagnosing pathologic myopia. The photograph-level F1 values of the segmentation model identifying optic disc, peripapillary atrophy, diffuse atrophy, patchy atrophy, and macular atrophy were all >0.95; the pixel-level F1 values for segmenting optic disc and peripapillary atrophy were both >0.9; the pixel-level F1 values for segmenting diffuse atrophy, patchy atrophy, and macular atrophy were all >0.8; and the photograph-level recall/sensitivity for detecting lacquer cracks was 0.9230. Conclusions: The models could accurately and automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and monitor progression of the lesions. Translational Relevance: The models can potentially help with the diagnosis, screening, and follow-up for pathologic myopic in clinical practice.
Indexed as
Identifiers
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.