Clinical information prompt‑driven retinal fundus image for brain health evaluation

作者：Nuo Tong, Ying Hui, Shui-Ping Gou, Ling‑Xi Chen, Xianghong Wang, Shuohua Chen, Jing Li, Xiaoshuai Li, Yuntao Wu, Shouling Wu, Zhenchang Wang, Jing Sun, Han Lv · 发表于：Military Medical Research · 年份：2025 · DOI：10.1186/s40779-025-00630-2 · 被引用次数：8 · 研究领域：Retinal Imaging and Analysis、Brain Tumor Detection and Classification、Ophthalmology and Visual Impairment Studies

Abstract Background Brain volume measurement serves as a critical approach for assessing brain health status. Considering the close biological connection between the eyes and brain, this study aims to investigate the feasibility of estimating brain volume through retinal fundus imaging integrated with clinical metadata, and to offer a cost-effective approach for assessing brain health. Methods Based on clinical information, retinal fundus images, and neuroimaging data derived from a multicenter, population-based cohort study, the KaiLuan Study, we proposed a cross-modal correlation representation (CMCR) network to elucidate the intricate co-degenerative relationships between the eyes and brain for 755 subjects. Specifically, individual clinical information, which has been followed up for as long as 12 years, was encoded as a prompt to enhance the accuracy of brain volume estimation. Independent internal validation and external validation were performed to assess the robustness of the proposed model. Root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) metrics were employed to quantitatively evaluate the quality of synthetic brain images derived from retinal imaging data. Results The proposed framework yielded average RMSE, PSNR, and SSIM values of 98.23, 35.78 dB, and 0.64, respectively, which significantly outperformed 5 other methods: multi-channel Variational Autoencoder (mcVAE), Pixel-to-Pixel (Pixel2pixel), tran...