Related Experiment Video
Updated: Oct 16, 2026
Quantitative Fundus Autofluorescence for the Evaluation of Retinal Diseases
Published on: March 11, 2016
FFA-SegGPT: A Two-Stage Cross-Modal AI System to Optimize the Clinical Workflow of Diabetic Retinopathy FFA
Fan Gan1,2, Xian Chen1,3, Yinan Shao4
1Department of Ophthalmology, The Affiliated Eye Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People's Republic of China.
Purpose:
The purpose of this study was to develop an interpretable, two-stage artificial intelligence (AI) system (fundus fluorescein angiography [FFA]-SegGPT) designed to optimize the clinical workflow of diabetic retinopathy (DR) FFA interpretation and reduce the risk of factual inaccuracies common in end-to-end models.
Methods:
We developed a cross-modal system that decouples lesion quantification from report generation. A segmentation module first explicitly segments non-perfusion areas (NPAs) and neovascularization (NV). These segmentation maps then serve as visual constraints to guide an evidence-based report generator, ensuring diagnostic text is strictly grounded in detected lesions.
Results:
In a simulated clinical workflow, AI-aided reporting significantly reduced mean interpretation time by 50% (from 8.13 ± 0.47 to 4.09 ± 0.21 minutes, P < 0.001) and improved median report quality (4.0 vs. 3.5, P < 0.001) compared to manual interpretation. Objectively, the model achieved high Dice Similarity Coefficients (NV = 85.35% and NPAs = 86.49%) and maintained high semantic fidelity for report generation (BERTScore = 0.91), with robust generalization demonstrated across three external cohorts.
Conclusions:
FFA-SegGPT enhances both the efficiency and quality of DR diagnostics. By decoupling lesion quantification from report generation, the system provides a transparent, dual-output mechanism that reduces the risk of factual inaccuracies, offering an interpretable workflow to facilitate safe clinical adoption.
Translational Relevance:
This interpretable AI system optimizes the clinical workflow of DR by providing verifiable lesion segmentation and automated reporting, potentially reducing clinician workload and standardizing diagnostic quality.
