Early Detection of Left Ventricular Dysfunction With Machine Learning‐Based Strain Imaging in Aortic Stenosis Patients.
Purpose: Aortic stenosis (AS) is a common cardiovascular condition where early detection of left ventricular (LV) dysfunction is essential for timely intervention and optimal management. Current echocardiographic measurements, such as ejection fraction (EF), are insensitive to minor changes in LV fu...
| Publicado en: | Echocardiography Vol. 41; no. 11; pp. 1 - 17 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Nov2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=181057907&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 181057907 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07422822 GSE jtl: Echocardiography issn: 07422822 maglogo: Y pubinfo: dt: Nov2024 vid: 41 iid: 11 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 181057907 181057907 181057907 10.1111/echo.70007 181057907 ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Early Detection of Left Ventricular Dysfunction With Machine Learning‐Based Strain Imaging in Aortic Stenosis Patients. aug: au: Yahav, Amir Adam, Dan affil: Faculty of Biomedical Engineering, Technion – Israel Institute of Technology, Haifa, Israel sug: subj: Ventricular Dysfunction, Left Diagnosis Early Diagnosis Machine Learning Algorithms Diagnosis, Computer Assisted Aortic Valve Stenosis Human Female Male Cardiac Patients Machine Learning Echocardiography Quality Assurance Decision Trees Chest Pain ROC Curve Descriptive Statistics Funding Source Female Male ab: Purpose: Aortic stenosis (AS) is a common cardiovascular condition where early detection of left ventricular (LV) dysfunction is essential for timely intervention and optimal management. Current echocardiographic measurements, such as ejection fraction (EF), are insensitive to minor changes in LV function, and strain imaging is typically limited to the global longitudinal strain (GLS) parameter due to robustness issues. This study introduces a novel, fully automatic algorithm to enhance the detection of LV dysfunction in AS patients using multiple strain imaging parameters. Methods: We applied supervised machine‐learning techniques to classify data from 82 severe AS patients, 96 chest pain subjects, and 319 healthy volunteers. Results: Our model significantly outperformed EF and GLS in distinguishing AS patients from healthy volunteers (area under the curve [AUC] = 0.97 vs. 0.88 and 0.82, respectively). It also surpassed EF and GLS in differentiating AS patients from chest pain subjects (AUC = 0.95 vs. 0.90 and 0.55, respectively). Conclusion: This novel, clinically interpretable model leverages the potential of strain imaging to enhance diagnostic accuracy and guide clinical decision‐making in LV dysfunction, thereby improving clinical practice. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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