An Intelligent Grading Model for Myopic Maculopathy Based on Long-Tailed Learning.

Purpose: To develop an intelligent grading model for myopic maculopathy based on a long-tail learning framework, using the improved loss function LTBSoftmax. The model addresses the long-tail distribution problem in myopic maculopathy data to provide preliminary grading, aiming to improve grading capability and efficiency.

Methods: This study includes a data set of 7529 color fundus photographs. Experienced ophthalmologists meticulously annotated the ground truth. A new intelligent grading model for myopic maculopathy was constructed using the improved loss function LTBSoftmax, which predicts lesions by locally enhancing feature extraction with ND Block. Standard grading metrics were selected to evaluate the LTBSoftmax model.

Results: The improved model demonstrated excellent performance in diagnosing four types of myopic maculopathy, achieving a κ coefficient of 88.89%. Furthermore, the model's size is 18.7 MB, which is relatively smaller compared to traditional models, indicating that the model not only achieves a high level of agreement with expert diagnoses but is also more efficient in terms of both storage and computational resources. These metrics further validate the model's well-conceived design and superiority in practical applications.

Conclusions: The intelligent grading system, using long-tailed learning strategies, effectively improves the classification of myopic maculopathy, offering a practical grading tool for clinicians, particularly in areas with limited resources.

Translational relevance: This model translates long-tail learning research into a practical grading tool for myopic maculopathy. It addresses data imbalance with the improved LTBSoftmax loss function, achieving high accuracy and efficiency. By enhancing feature extraction with ND Block, it provides reliable grading support for clinicians, especially in resource-limited settings.