The beating heart: artificial intelligence for cardiovascular application in the clinic.
Manuel Villegas-MartinezVictor de Villedon de NaideVivek MuthuranguAurélien BustinPublished in: Magma (New York, N.Y.) (2024)
Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.
Keyphrases
- artificial intelligence
- magnetic resonance imaging
- machine learning
- big data
- computed tomography
- deep learning
- low dose
- image quality
- end stage renal disease
- contrast enhanced
- chronic kidney disease
- left ventricular
- newly diagnosed
- high resolution
- heart failure
- electronic health record
- magnetic resonance
- high dose
- working memory
- type diabetes
- case report
- human health
- atrial fibrillation
- patient reported
- climate change
- low cost
- glycemic control
- adipose tissue
- insulin resistance
- diffusion weighted imaging
- risk assessment