| 1. |
- Rademakers, Frank, et al.
(författare)
-
Determining optimal noninvasive parameters for the prediction of left ventricular remodeling in chronic ischemic patients
- 2013
-
Ingår i: Scandinavian Cardiovascular Journal. - : Informa Healthcare. - 1401-7431 .- 1651-2006. ; 47:6, s. 329-334
-
Tidskriftsartikel (refereegranskat)abstract
- Objectives. DOPPLER-CIP aims to determine the optimal noninvasive parameters (myocardial function, perfusion, ventricular blood flow, cell integrity) and methodology (ergometry, echocardiography, scintigraphy, MRI) in a given ischemic substrate that best predicts the impact of an intervention (or the lack thereof) on adverse morphological ventricular remodeling and functional recovery. Moreover, the relative predictive value of each of these parameters, in respect to the cost of extracting this information in order to enable optimization of cost-effectiveness for improved health care, will be determined by this project. Design. DOPPLER-CIP is a multi-center registry study. All patients with ischemic heart disease included in this study undergo at least two noninvasive stress imaging examinations at baseline. The presence/or absence of left ventricular (LV) remodeling will be assessed after a follow-up of 2 years, during which all cardiac events will be registered. Results. 676 patients were included. Currently, baseline data analysis is almost finished and the follow-up is ongoing. Conclusions. After completion, DOPPLER-CIP will provide evidence-based guidelines toward the most effective use of cardiac imaging in the chronically ischemic heart disease patient. The study will generate information, knowledge, and insight into the new imaging methodologies and into the pathophysiology of chronic ischemic heart disease.
|
|
| 2. |
- Tabassian, Mahdi, et al.
(författare)
-
Machine learning of the spatio-temporal characteristics of echocardiographic deformation curves for infarct classification
- 2017
-
Ingår i: The International Journal of Cardiovascular Imaging. - : SPRINGER. - 1569-5794 .- 1875-8312 .- 1573-0743. ; 33:8, s. 1159-1167
-
Tidskriftsartikel (refereegranskat)abstract
- The aim of this study was to analyze the whole temporal profiles of the segmental deformation curves of the left ventricle (LV) and describe their interrelations to obtain more detailed information concerning global LV function in order to be able to identify abnormal changes in LV mechanics. The temporal characteristics of the segmental LV deformation curves were compactly described using an efficient decomposition into major patterns of variation through a statistical method, called Principal Component Analysis (PCA). In order to describe the spatial relations between the segmental traces, the PCA-derived temporal features of all LV segments were concatenated. The obtained set of features was then used to build an automatic classification system. The proposed methodology was applied to a group of 60 MRI-delayed enhancement confirmed infarct patients and 60 controls in order to detect myocardial infarction. An average classification accuracy of 87% with corresponding sensitivity and specificity rates of 89% and 85%, respectively was obtained by the proposed methodology applied on the strain rate curves. This classification performance was better than that obtained with the same methodology applied on the strain curves, reading of two expert cardiologists as well as comparative classification systems using only the spatial distribution of the end-systolic strain and peak-systolic strain rate values. This study shows the potential of machine learning in the field of cardiac deformation imaging where an efficient representation of the spatio-temporal characteristics of the segmental deformation curves allowed automatic classification of infarcted from control hearts with high accuracy.
|
|
