An intelligent classifier for prognosis of cardiac resynchronization therapy based on speckle-tracking echocardiograms
| Autor: | Chun-Li Wang, Hsiao-Lung Chan, Pei-Kuang Chao |
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| Rok vydání: | 2010 |
| Předmět: |
Male
Support Vector Machine genetic structures Computer science medicine.medical_treatment Treatment outcome Cardiac resynchronization therapy Medicine (miscellaneous) Sensitivity and Specificity Cardiac Resynchronization Therapy Speckle pattern Ventricular Dysfunction Left Artificial Intelligence Predictive Value of Tests medicine Humans Computer vision cardiovascular diseases Forward selection Aged business.industry Patient Selection Pattern recognition Middle Aged Classification Prognosis Support vector machine Treatment Outcome Echocardiography cardiovascular system Classification methods Female Artificial intelligence business Classifier (UML) |
| Zdroj: | Artificial intelligence in medicine. 54(3) |
| ISSN: | 1873-2860 |
| Popis: | Purpose: Predicting response after cardiac resynchronization therapy (CRT) has been a challenge of cardiologists. About 30% of selected patients based on the standard selection criteria for CRT do not show response after receiving the treatment. This study is aimed to build an intelligent classifier to assist in identifying potential CRT responders by speckle-tracking radial strain based on echocardiograms. Methods and materials: The echocardiograms analyzed were acquired before CRT from 26 patients who have received CRT. Sequential forward selection was performed on the parameters obtained by peak-strain timing and phase space reconstruction on speckle-tracking radial strain to find an optimal set of features for creating intelligent classifiers. Support vector machine (SVM) with a linear, quadratic, and polynominal kernel were tested to build classifiers to identify potential responders and non-responders for CRT by selected features. Results: Based on random sub-sampling validation, the best classification performance is correct rate about 95% with 96-97% sensitivity and 93-94% specificity achieved by applying SVM with a quadratic kernel on a set of 3 parameters. The selected 3 parameters contain both indexes extracted by peak-strain timing and phase space reconstruction. Conclusions: An intelligent classifier with an averaged correct rate, sensitivity and specificity above 90% for assisting in identifying CRT responders is built by speckle-tracking radial strain. The classifier can be applied to provide objective suggestion for patient selection of CRT. |
| Databáze: | OpenAIRE |
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