Multi-Scale Clinical-Guided Binocular Fusion Framework for Predicting New-Onset Hypertension Over a Four-Year Period

• Published in: 2024 IEEE International Symposium on Biomedical Imaging (ISBI) pp. 1 - 5
• Main Authors: Li, Haoshen, Chen, Zifan, Zhao, Jie, Chen, Heyun, Dong, Hexin, Yuan, Mingze, Dong, Bin, Zhang, Li
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27.05.2024
• Subjects: Accuracy; Codes; Deep learning; Feature extraction; Fundus Image; Fuses; Future New-Onset Hypertension Prediction; Hypertension; Multi-Scale Feature Fusion; Sensitivity
• ISSN: 1945-8452
• DOI: 10.1109/ISBI56570.2024.10635770

Hypertension is a major global health concern, linked to various cardiovascular diseases and associated with distinct ocular manifestations. While recent advances in artificial intelligence have enabled accurate diagnosis of current hypertension through fundus images, predicting the future onset of hypertension remains an uncharted domain. In this study, we introduce the multi-scale clinical-guided binocular fusion framework (MCBO), designed to predict the likelihood of developing hypertension within the next four years. MCBO uniquely integrates left and right fundus images and clinical data, utilizing a shared-weight multi-stage Transformer-based encoder. Our multi-scale clinical-guided module (MCM) ensures image feature extraction is clinically contextualized based on clinical information, and our binocular fusion module (BFM) fuses binocular information. Comparative performance against seven baseline models establishes MCBO's supremacy, with improvements of 6.7% in Area Under Curve (AUC), 6.9% in Accuracy (ACC), 5.1% in Sensitivity (SEN) and 5.5% in Specificity (SPE). This approach offers a promising avenue for proactive hypertension management, underscoring the potential of integrating Deep Learning with clinical data for enhanced healthcare outcomes. Our code is available at https://github.com/HaoshenLi/MCBO.