Generation of Fundus Fluorescein Angiography Videos for Health Care Data Sharing.

Abstract

Importance Medical data sharing faces strict restrictions. Text-to-video generation shows potential for creating realistic medical data while preserving privacy, offering a solution for cross-center data sharing and medical education. Objective To develop and evaluate a text-to-video generative artificial intelligence (AI)-driven model that converts the text of reports into dynamic fundus fluorescein angiography (FFA) videos, enabling visualization of retinal vascular and structural abnormalities. Design, Setting, and Participants This study retrospectively collected anonymized FFA data from a tertiary hospital in China. The dataset included both the medical records and FFA examinations of patients assessed between November 2016 and December 2019. A text-to-video model was developed and evaluated. The AI-driven model integrated the wavelet-flow variational autoencoder and the diffusion transformer. Main Outcomes and Measures The AI-driven model's performance was assessed through objective metrics (Fréchet video distance, learned perceptual image patch similarity score, and visual question answering score [VQAScore]). The domain-specific evaluation for the generated FFA videos was measured by the bidirectional encoder representations from transformers score (BERTScore). Image retrieval was evaluated using a Recall@K score. Each video was rated for quality by 3 ophthalmologists on a scale of 1 (excellent) to 5 (very poor). Results A total of 3625 FFA videos were included (2851 videos [78.6%] for training, 387 videos [10.7%] for validation, and 387 videos [10.7%] for testing). The AI-generated FFA videos demonstrated retinal abnormalities from the input text (Fréchet video distance of 2273, a mean learned perceptual image patch similarity score of 0.48 [SD, 0.04], and a mean VQAScore of 0.61 [SD, 0.08]). The domain-specific evaluations showed alignment between the generated videos and textual prompts (mean BERTScore, 0.35 [SD, 0.09]). The Recall@K scores were 0.02 for K = 5, 0.04 for K = 10, and 0.16 for K = 50, yielding a mean score of 0.073, reflecting disparities between AI-generated and real clinical videos and demonstrating privacy-preserving effectiveness. For assessment of visual quality of the FFA videos by the 3 ophthalmologists, the mean score was 1.57 (SD, 0.44). Conclusions and Relevance This study demonstrated that an AI-driven text-to-video model generated FFA videos from textual descriptions, potentially improving visualization for clinical and educational purposes. The privacy-preserving nature of the model may address key challenges in data sharing while trying to ensure compliance with confidentiality standards.