ArticleGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2026
Development and validation of machine learning for pathological changes in high myopia patients with tessellated fundus: a cross-sectional study.
Article in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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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.
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Who cites it
2 citing papers in PubMed.
- Aberrant Functional Lateralization and Interhemispheric Synergy in High Myopia: Insights From Neurotransmitter Distribution Signatures and Machine Learning Classification.Investigative ophthalmology & visual science · 2026Article
- Predicting hyperopic reserve in children based on school-based vision programs with ensemble learning.Frontiers in public health · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
purposePathological myopia causes irreversible damage, yet early changes in high myopia patients with tessellated fundus are often overlooked. The identification of such patients relies on OCT and fundus imaging, but early detection is difficult. Therefore, we developed and validated machine learning models using lifestyle and clinical parameters.
methodsCross-sectional data were collected from high myopia patients with tessellated fundus at the First Affiliated Hospital of Guangxi Medical University (May 2023-July 2024). Data were split (7:3) into training and test sets. Five-fold cross-validation within the training set was used during LASSO regression to select key variables. Machine learning models, including Random Forest, Support Vector Machine, Linear Support Vector Machine, and XGBoost, were constructed. Model performance was evaluated by AUC, sensitivity, specificity, calibration curves, and decision curve analysis.
results529 eyes were analyzed. Key modeling variables included education level, daily iPad usage time, daily reading time, daily reading time while lying down, lamp light source, daily outdoor activity time, spherical equivalent, axial length, corneal thickness, lens thickness, extent of PPA involvement, maximum radius of PPA, horizontal disc diameter, and disc tilt ratio. In the test set, the AUCs of the Random Forest, Support Vector Machine, Linear Support Vector Machine, and XGBoost models were 0.834, 0.815, 0.765, and 0.839, respectively. Both the training and test sets demonstrated good calibration and high clinical applicability.
conclusionsMachine learning models using lifestyle and clinical parameters can effectively screen for early pathological changes in high myopia with tessellated fundus, offering an auxiliary tool for clinicians.
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