Strain Curve Classification Using Supervised Machine Learning Algorithm with Physiologic Constraints.
Speckle tracking echocardiography (STE) enables quantification of myocardial deformation by a generation of spatiotemporal strain curves or time-strain curves (TSCs). Currently, only assessment of peak global longitudinal strain is employed in clinical practice because of the uncertainty in the accu...
| Publicado en: | Ultrasound in Medicine & Biology Vol. 46; no. 9; pp. 2424 - 2439 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Sep2020
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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=145040995&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145040995 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03015629 JJ6 jtl: Ultrasound in Medicine & Biology issn: 03015629 maglogo: N pubinfo: dt: Sep2020 vid: 46 iid: 9 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 145040995 145040995 NLM32505614 145040995 10.1016/j.ultrasmedbio.2020.03.002 NLM32505614 145040995 ppf: 2424 ppct: 15 formats: tig: atl: Strain Curve Classification Using Supervised Machine Learning Algorithm with Physiologic Constraints. aug: au: Yahav, Amir Zurakhov, Grigoriy Adler, Omri Adam, Dan affil: Faculty of Biomedical Engineering, Technion—Israel Institute of Technology, Haifa, Israel sug: subj: Heart Physiology Echocardiography Methods Female Adult Human Male Heart Function Tests Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales Adult: 19-44 years Female Male ab: Speckle tracking echocardiography (STE) enables quantification of myocardial deformation by a generation of spatiotemporal strain curves or time-strain curves (TSCs). Currently, only assessment of peak global longitudinal strain is employed in clinical practice because of the uncertainty in the accuracy of STE. We describe a supervised machine learning, physiologically constrained, fully automatic algorithm, trained with labeled data, for classification of TSCs into physiologic or artifactual classes. The data set of 415 healthy patients, with three cine loops per patient, corresponding to the three standard 2-D longitudinal views, was processed using a previously published, in-house STE software termed K-SAD. We report an accuracy of 86.4% for classifying TSCs as physiologic, artifactual and undetermined curves. The positive predictive value for a physiologic strain curve is 89%. This is as a necessary step for a similar separation of pathologic conditions, to allow full utilization of the temporal information concealed in layer-specific segmental TSCs. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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