Artificial intelligence can detect left ventricular dilatation on contrast-enhanced thoracic computer tomography relative to cardiac magnetic resonance imaging.
Ashar AsifPia F P ChartersCharlotte A S ThompsonHend M E I KomberBenjamin J HudsonJonathan Carl Luis RodriguesPublished in: The British journal of radiology (2022)
We show, for the first time, that a fully-automated AI-derived analysis of maximal LV chamber axial diameter on non-ECG-gated thoracic CT is feasible in unselected real-world cases and that the derived measures can predict LV dilatation relative to cardiac magnetic resonance imaging, the non-invasive reference standard for determining cardiac chamber size. We have derived sex-specific cut-off values to screen for LV dilatation on routine contrast-enhanced thoracic CT. Future work should validate these thresholds and determine if technology can alter clinical outcomes in a cost-effective manner.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- artificial intelligence
- left ventricular
- diffusion weighted
- deep learning
- computed tomography
- machine learning
- magnetic resonance
- diffusion weighted imaging
- spinal cord
- big data
- dual energy
- high throughput
- heart rate
- heart failure
- cardiac resynchronization therapy
- aortic stenosis
- positron emission tomography
- acute coronary syndrome
- high intensity
- percutaneous coronary intervention
- body composition