Zhang Hong-Qi, Arif Muhammad, Thafar Maha A, Albaradei Somayah, Cai Peiling, Zhang Yang, Tang Hua, Lin Hao
The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
Front Med (Lausanne). 2025 Mar 13;12:1529335. doi: 10.3389/fmed.2025.1529335. eCollection 2025.
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.
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.
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.
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.
病理性近视（PM）是一种严重的视力损害，可能导致不可逆的视力损伤甚至失明。及时诊断和有效管理病理性近视具有重要意义。鉴于全球近视病例数量不断增加，迫切需要开发一种自动化、准确且具有高度可解释性的病理性近视诊断技术。
我们提出了一种名为PMPred-AE的计算模型，该模型基于带有注意力机制优化的EfficientNetV2-L。此外，采用梯度加权类激活映射（Grad-CAM）技术为模型的决策过程提供直观的视觉解释。
实验结果表明，PMPred-AE在自动检测病理性近视方面表现出色，在训练、验证和测试数据集中的准确率分别为98.50%、98.25%和97.25%。此外，PMPred-AE在做出检测决策时能够专注于病理性近视图像的特定区域。
所开发的PMPred-AE模型能够可靠地提供准确的病理性近视检测。此外，Grad-CAM技术还用于为模型的决策过程提供直观的视觉解释。这种方法为医疗保健专业人员提供了一种用于可解释人工智能决策过程的有效工具。
