Related Experiment Video
Updated: Jul 25, 2026
A Step by Step Protocol for Subretinal Surgery in Rabbits
Published on: September 13, 2016
SABPI-Net: A Structure-Aware Bidirectional Proxy Interaction Network for Infantile Retinal Disease Diagnosis
Insights
Early detection of infant retinal diseases is crucial. A new AI model, SABPI-Net, significantly improves automated diagnosis accuracy for various infant retinal conditions, aiding timely treatment.
Area of Science:
- Ophthalmology
- Artificial Intelligence
- Medical Imaging
Background:
- Delayed diagnosis of infant retinal diseases leads to irreversible vision loss.
- Automated diagnosis is challenged by subtle lesions, diverse phenotypes, imaging variations, and imbalanced data.
- Existing foundation models struggle with these specific challenges in infant retinal imaging.
Purpose of the Study:
- To develop an advanced AI model for accurate and robust automated diagnosis of infant retinal diseases.
- To enhance the perception of fine retinal structures and ensure consistent performance across diverse imaging conditions.
- To improve diagnostic sensitivity for subtle and less common infant retinal diseases.
Main Methods:
- Proposed the structure-aware bidirectional proxy interaction network (SABPI-Net) within a universal learning framework.
- Incorporated a high-frequency mapping branch with a proxy interaction attention module for detailed structure perception.
- Utilized domain-agnostic embedding space self-matching and a tail-aware feature fusion strategy for fine-tuning.
Main Results:
- SABPI-Net achieved superior performance on three infant retinal disease classification tasks using the largest clinical dataset to date.
- Achieved 95.32% accuracy on mainstream clinical tasks, 73.58% on ROP five-stage classification, and 84.25% on multi-disease classification.
- Demonstrated significant improvements over 13 state-of-the-art methods, with accuracy gains of up to 4.71%.
Conclusions:
- SABPI-Net effectively addresses the challenges in automated infant retinal disease diagnosis.
- The model shows superior diagnostic accuracy and robustness compared to existing methods.
- This advancement holds significant potential for improving early detection and treatment of infant retinal diseases.
Abstract:
Delayed treatment of infantile retinal disease can reduce its effectiveness and may cause severe and irreversible damage. Automated diagnosis of infant retinal diseases faces challenges including subtle early lesions, diverse clinical phenotypes, imaging variations, and imbalanced data. To address these, which cannot be well addressed by existing general foundation models, we propose structure-aware bidirectional proxy interaction network (SABPI-Net) in a universal learning framework. SABPI-Net incorporates a high-frequency mapping branch, and employs a proposed proxy interaction attention module to enable effective interaction between its trunk feature encoding branch and the high-frequency mapping branch, thereby facilitating enhanced perception of retinal detail structures. Domain-agnostic embedding space self-matching, guided by a memory-bank low-frequency component replacement strategy, promotes domain-invariant learning and consistent model performance under diverse image styles. Finally, the tail-aware feature fusion strategy for fine-tuning further enhances the model's diagnostic sensitivity to tailed diseases. In this study, three classification tasks related to infant retinal diseases are implemented on the largest clinical infant retina dataset to date, covering 19 infant retinal diseases or normal conditions. SABPI-Net achieves superior performance compared to 13 SOTA methods, with 95.32% accuracy on mainstream clinical tasks, 73.58% on ROP five-stage classification, and 84.25% on multi-disease classification, representing improvements of 1.57%, 1.88%, and 4.71% respectively over the best competing methods. Extensive experiments demonstrate the effectiveness and superiority of SABPI-Net in diagnosing infant retinal diseases.
