Related Experiment Video
Updated: Aug 12, 2026
In Vivo Multimodal Imaging and Analysis of Mouse Laser-Induced Choroidal Neovascularization Model
Published on: January 21, 2018
A lightweight DeepME model based on improved YOLOv11 architecture for macular edema detection and treatment
Xue Bai1, Ming Yi2, Tianye Chen1
1Department of Ophthalmology, The First Hospital of China Medical University, Shenyang, Liaoning, China.
Objective:
We developed and validated an improved YOLOv11-based deep learning algorithm for accurate macular edema detection in optical coherence tomography (OCT) images, and built DeepME-a lightweight system for diagnosis and treatment recommendations.
Methods:
We compiled a comprehensive dataset combining hospital clinical data and public OCT resources, covering macular edema and other retinal diseases. External validation used an anti-vascular endothelial growth factor (anti-VEGF) cohort of 336 eyes from 300 patients with diabetic retinopathy or retinal vein occlusion. The improved YOLOv11n integrated the Convolutional Block Attention Module (CBAM) to enhance feature extraction. DeepME combined this detector with an optimized DeepSeek model, current clinical guidelines, and expert knowledge.
Results:
DeepME achieved better performance over standard YOLOv11: accuracy 0.980, specificity 0.990, sensitivity 0.970, precision 0.990, F1-score 0.980, and AUC 0.9993. Grad-CAM visualizations confirmed precise localization of cystoid macular edema within anatomically correct retinal layers. In the anti-VEGF cohort, central foveal thickness decreased significantly at one-month follow-up (p < 0.001), and DeepME showed substantial agreement with manual grading (p < 0.001), enabling rapid, accurate diagnosis and treatment guidance.
Conclusion:
This study introduces DeepME, a novel clinical decision support system that integrates an improved YOLOv11 detection architecture for comprehensive macular edema management. DeepME delivers high accuracy in evaluating anti-VEGF treatment response and shows strong potential for real-world clinical decision support.
