Holistic AI analysis of hybrid cardiac perfusion images for mortality prediction.
Anna M MichalowskaWenhao ZhangAakash ShanbhagRobert Jh MillerMark LemleyGiselle RamirezMikolaj BuchwaldAditya KillekarPaul B KavanaghAttila FeherRobert J H MillerAndrew J EinsteinTerrence D RuddyJoanna X LiangValerie BuiloffDavid OuyangDaniel S BermanDamini DeyPiotr J SlomkaPublished in: medRxiv : the preprint server for health sciences (2024)
In patients with normal perfusion, the comprehensive model (0.76 [0.65-0.86]) had significantly better performance than the AI CTAC (0.72 [0.61-0.83]) and AI hybrid (0.73 [0.62-0.84]) models (p<0.001, for all).CTAC significantly enhances AI risk stratification with MPI SPECT/CT beyond its primary role - attenuation correction. A comprehensive multimodality approach can significantly improve mortality prediction compared to MPI information alone in patients undergoing cardiac SPECT/CT.
Keyphrases
- artificial intelligence
- contrast enhanced
- patients undergoing
- deep learning
- computed tomography
- cardiovascular events
- image quality
- dual energy
- left ventricular
- magnetic resonance imaging
- machine learning
- pet ct
- risk factors
- magnetic resonance
- convolutional neural network
- optical coherence tomography
- healthcare
- cardiovascular disease
- heart failure
- atrial fibrillation
- social media