ArticleFrontiers in medicine2025
PMPred-AE: a computational model for the detection and interpretation of pathological myopia based on artificial intelligence.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
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- Brain Stroke Classification Using CT Scans with Transformer-Based Models and Explainable AI.Diagnostics (Basel, Switzerland) · 2025Article
- A deep learning model for epidermal growth factor receptor prediction using ensemble residual convolutional neural network.Scientific reports · 2025Article
- NeXtMD: a new generation of machine learning and deep learning stacked hybrid framework for accurate identification of anti-inflammatory peptides.BMC biology · 2025Article
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Corrections and comments
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Authors and funding
8 authors.
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Abstract
Introduction: Pathological myopia (PM) is a serious visual impairment that may lead to irreversible visual damage or even blindness. Timely diagnosis and effective management of PM are of great significance. Given the increasing number of myopia cases worldwide, there is an urgent need to develop an automated, accurate, and highly interpretable PM diagnostic technology. Methods: We proposed a computational model called PMPred-AE based on EfficientNetV2-L with attention mechanism optimization. In addition, Gradient-weighted class activation mapping (Grad-CAM) technology was used to provide an intuitive and visual interpretation for the model's decision-making process. Results: The experimental results demonstrated that PMPred-AE achieved excellent performance in automatically detecting PM, with accuracies of 98.50, 98.25, and 97.25% in the training, validation, and test datasets, respectively. In addition, PMPred-AE can focus on specific areas of PM image when making detection decisions. Discussion: The developed PMPred-AE model is capable of reliably providing accurate PM detection. In addition, the Grad-CAM technology was also used to provide an intuitive and visual interpretation for the decision-making process of the model. This approach provides healthcare professionals with an effective tool for interpretable AI decision-making process.
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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 Socratic graph.