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PMPred-AE: a computational model for the detection and interpretation of pathological myopia based on artificial
Hong-Qi Zhang1, Muhammad Arif2, Maha A Thafar3
1The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
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.
Insights
A new AI model, PMPred-AE, accurately detects pathological myopia (PM) with high precision. This interpretable technology aids in early diagnosis and management of this serious visual impairment.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Imaging
Background:
- Pathological myopia (PM) is a significant cause of irreversible vision loss worldwide.
- Current diagnostic methods for PM require timely and accurate detection for effective management.
- There is a growing need for automated and interpretable technologies to diagnose PM.
Purpose of the Study:
- To develop an automated and interpretable computational model for pathological myopia detection.
- To enhance the accuracy and efficiency of PM diagnosis using artificial intelligence.
Main Methods:
- A computational model, PMPred-AE, was developed using EfficientNetV2-L with attention mechanism optimization.
- Gradient-weighted class activation mapping (Grad-CAM) was employed for visual interpretation of the model's decisions.
Main Results:
- PMPred-AE achieved high detection accuracies: 98.50% (training), 98.25% (validation), and 97.25% (testing).
- The model demonstrated the ability to focus on specific image regions relevant to PM detection.
- Grad-CAM provided intuitive visual explanations for the AI's diagnostic process.
Conclusions:
- The PMPred-AE model offers a reliable and accurate tool for automated pathological myopia detection.
- The integration of Grad-CAM enhances the interpretability of AI-driven diagnostic decisions in ophthalmology.
- This interpretable AI approach can serve as a valuable tool for healthcare professionals in managing PM.
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