Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging

Yan-Ran (Joyce) Wang, Kai Yang, Yi Wen, Peng-Cheng Wang, Yuepeng Hu, Yongfan Lai, Yufeng Wang, Kankan Zhao, Siyi Tang, Angela Zhang, Huayi Zhan, Minjie Lu, Xiuyu Chen, Shujuan Yang, Zhixiang Dong, Yining Wang, Hui Liu, Lei Zhao, Lu Huang, Yunling Li, Lianming Wu, Zixian Chen, Yi Luo, Dongbo Liu, Pengbo Zhao, Keldon Lin, Joseph C. Wu, Shihua Zhao
May, 2024
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Abstract

Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and plays a crucial role in diagnosing cardiovascular disease (CVD). However, its widespread application has been limited by the heavy resource burden of CMR interpretation. Here, to address this challenge, we developed and validated computerized CMR interpretation for screening and diagnosis of 11 types of CVD in 9,719 patients. We propose a two-stage paradigm consisting of noninvasive cine-based CVD screening followed by cine and late gadolinium enhancement-based diagnosis. The screening and diagnostic models achieved high performance (area under the curve of 0.988 ± 0.3% and 0.991 ± 0.0%, respectively) in both internal and external datasets. Furthermore, the diagnostic model outperformed cardiologists in diagnosing pulmonary arterial hypertension, demonstrating the ability of artificial intelligence-enabled CMR to detect previously unidentified CMR features. This proof-of-concept study holds the potential to substantially advance the efficiency and scalability of CMR interpretation, thereby improving CVD screening and diagnosis.

Type
Journal article
Publication
Nature Medicine, 2024
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Peng-Cheng Wang
Peng-Cheng Wang
PhD Student in USC

My research interest is in Biomedical Artificial Intelligence, focusing on the interdisciplinary areas that encompass Machine Learning, Computer Vision, Signal and Image Processing, Medical Image Analysis, Biomedical Imaging (MRI), and data science.