USA: Advancements in medical technology are rapidly transforming the landscape of cardiovascular health screening and diagnosis. Among these breakthroughs, artificial intelligence (AI) is taking center stage, particularly in cardiac magnetic resonance imaging (MRI).
An artificial intelligence-based cardiac MRI interpretation outperformed cardiologists in diagnosing pulmonary arterial hypertension and showed promise in a proof-of-concept study.
"This proof-of-concept study holds the potential to substantially advance the scalability and efficiency of CMR interpretation, thereby improving screening and diagnosis of cardiovascular disease (CVD)," the researchers wrote in their study published in Nature Medicine.
Cardiovascular diseases remain a leading cause of mortality worldwide, underscoring the critical need for accurate and timely detection. Traditional screening and diagnosis methods often rely on invasive procedures or lack the precision necessary for early intervention. However, AI algorithms integration into cardiac MRI promises to revolutionize this process, offering unprecedented accuracy and efficiency.
Cardiac MRI is the gold standard for cardiac function assessment and is crucial in CVD diagnosis. However, its widespread application is limited by the heavy resource burden of CMR interpretation.
To address this challenge, Yan-Ran (Joyce) Wang, School of Medicine, Stanford University, Stanford, CA, USA, and colleagues developed and validated computerized CMR interpretation for screening and diagnosis of 11 types of CVD in 9,719 patients.
The research team proposed a two-stage paradigm consisting of noninvasive cine-based CVD screening followed by cine and late gadolinium enhancement-based diagnosis.
The following were the key findings of the study:
- 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.
- The diagnostic model outperformed cardiologists in diagnosing pulmonary arterial hypertension, demonstrating the ability of AI-enabled CMR to detect previously unidentified CMR features.
The findings demonstrate that end-to-end video-based deep learning models can detect cardiac anomalies and further classify distinct CVDs from CMR with high classification performance.
"If confirmed in clinical settings, our study has the potential to substantially advance the scalability and efficiency of CMR interpretation, paving the way for widespread CMR use in CVD screening and diagnosis," the researchers wrote.
In conclusion, AI-enabled cardiac MRI represents a paradigm shift in the screening and diagnosis of cardiovascular disease. By harnessing the power of artificial intelligence, healthcare providers can achieve earlier detection, more accurate diagnosis, and personalized treatment strategies. As this technology continues to evolve, it holds the potential to revolutionize cardiovascular care and improve patient outcomes globally.
Reference:
Wang, Y., Yang, K., Wen, Y., Wang, P., Hu, Y., Lai, Y., Wang, Y., Zhao, K., Tang, S., Zhang, A., Zhan, H., Lu, M., Chen, X., Yang, S., Dong, Z., Wang, Y., Liu, H., Zhao, L., Huang, L., . . . Zhao, S. (2024). Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging. Nature Medicine, 1-10. https://ift.tt/n4356mc
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