1. Identifying Novel Phenotypes of Elevated Left Ventricular End Diastolic Pressure Using Hierarchical Clustering of Features Derived From Electromechanical Waveform Data
3. Predicting cardiac disease from interactions of simultaneously-acquired hemodynamic and cardiac signals
4. First-In-Man Development of a Machine Learning Analytics Approach to Predict Elevated Left Ventricular Pressures
5. Coronary Artery Disease Learning and Algorithm Development Study: Early Analysis of Ejection Fraction Evaluation
2. Predicting cardiac disease from interactions of simultaneously-acquired hemodynamic and cardiac signals
3. Cardiac Phase Space Tomography: A novel method of assessing coronary artery disease utilizing machine learning
4. TCT-232 Diagnostic Accuracy of Machine Learned Algorithms Utilizing a Novel Form of Cardiac Phase Tomography (cPST) versus Single Photon Emission Tomography (SPECT) in the Assessment of CAD
5. TCT-233 Machine-Learned Algorithms Utilizing Novel Tomography for Evaluating Coronary Artery Disease
6. Noninvasive Detection of Coronary Artery Disease Using Resting Phase Signals and Advanced Machine Learning
7. TCT-154 Gender based Assessment of Coronary Artery Disease by Cardiac Phase Tomography Using Machine-Learned Algorithms
8. TCT-177 Assessing Coronary Artery Disease by Cardiac Phase Tomography Using Machine-Learned Algorithms in Obese and Elderly Subjects
