Automatic detection of end-diastolic and end-systolic frames in 2D echocardiography.
Background Correctly selecting the end-diastolic and end-systolic frames on a 2D echocardiogram is important and challenging, for both human experts and automated algorithms. Manual selection is time-consuming and subject to uncertainty, and may affect the results obtained, especially for advanced m...
| Publicado en: | Echocardiography Vol. 34; no. 7; pp. 956 - 968 |
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| Autores principales: | , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Jul2017
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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=124130530&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 124130530 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 07422822 GSE jtl: Echocardiography issn: 07422822 maglogo: Y pubinfo: dt: Jul2017 vid: 34 iid: 7 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 124130530 124130530 124130530 10.1111/echo.13587 124130530 ppf: 956 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Automatic detection of end-diastolic and end-systolic frames in 2D echocardiography. aug: au: Zolgharni, Massoud Negoita, Madalina Dhutia, Niti M. Mielewczik, Michael Manoharan, Karikaran Sohaib, S. M. Afzal Finegold, Judith A. Sacchi, Stefania Cole, Graham D. Francis, Darrel P. affil: Faculty of Medicine, Imperial College London, London United Kingdom sug: subj: Echocardiography Methods Ultrasonography Methods Diastole Classification Systole Classification Hemodynamics Evaluation Human Algorithms Heart Radiography ab: Background Correctly selecting the end-diastolic and end-systolic frames on a 2D echocardiogram is important and challenging, for both human experts and automated algorithms. Manual selection is time-consuming and subject to uncertainty, and may affect the results obtained, especially for advanced measurements such as myocardial strain. Methods and Results We developed and evaluated algorithms which can automatically extract global and regional cardiac velocity, and identify end-diastolic and end-systolic frames. We acquired apical four-chamber 2D echocardiographic video recordings, each at least 10 heartbeats long, acquired twice at frame rates of 52 and 79 frames/s from 19 patients, yielding 38 recordings. Five experienced echocardiographers independently marked end-systolic and end-diastolic frames for the first 10 heartbeats of each recording. The automated algorithm also did this. Using the average of time points identified by five human operators as the reference gold standard, the individual operators had a root mean square difference from that gold standard of 46.5 ms. The algorithm had a root mean square difference from the human gold standard of 40.5 ms ( P<.0001). Put another way, the algorithm-identified time point was an outlier in 122/564 heartbeats (21.6%), whereas the average human operator was an outlier in 254/564 heartbeats (45%). Conclusion An automated algorithm can identify the end-systolic and end-diastolic frames with performance indistinguishable from that of human experts. This saves staff time, which could therefore be invested in assessing more beats, and reduces uncertainty about the reliability of the choice of frame. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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