PMPred-AE: a computational model for the Detection and Interpretation of Pathological Myopia based on artificial intelligence

Hong-Qi Zhang ¹ Muhammad Arif ² Maha A Thafar ³ Somayah Albaradei ⁴ Peiling CAI ⁵ Yang Zhang ⁶ Hua Tang ⁷ Hao Lin ^1*

• ¹ University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China
• ² Hamad bin Khalifa University, Doha, Qatar
• ³ Taif University, Ta'if, Saudi Arabia
• ⁴ King Abdulaziz University, Jeddah, Makkah, Saudi Arabia
• ⁵ Chengdu University, Chengdu, Sichuan Province, China
• ⁶ Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China
• ⁷ Southwest Medical University, Luzhou, Sichuan, 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.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 decisionmaking 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.