Imperial College London
Alternate

DrMassoudZolgharni

Faculty of Medicine, National Heart & Lung Institute

Visiting Professor
 
 
 
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Contact

 

m.zolgharni Website

 
 
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Location

 

ICTEM buildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zolgharni:2017:10.1111/echo.13587,
author = {Zolgharni, M and Negoita, M and Dhutia, NM and Mielewczik, M and Manoharan, K and Sohaib, SMA and Finegold, JA and Sacchi, S and Cole, GD and Francis, DP},
doi = {10.1111/echo.13587},
journal = {ECHOCARDIOGRAPHY-A JOURNAL OF CARDIOVASCULAR ULTRASOUND AND ALLIED TECHNIQUES},
pages = {956--967},
title = {Automatic detection of end-diastolic and end-systolic frames in 2D echocardiography},
url = {http://dx.doi.org/10.1111/echo.13587},
volume = {34},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
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.
AU  - Zolgharni,M
AU  - Negoita,M
AU  - Dhutia,NM
AU  - Mielewczik,M
AU  - Manoharan,K
AU  - Sohaib,SMA
AU  - Finegold,JA
AU  - Sacchi,S
AU  - Cole,GD
AU  - Francis,DP
DO  - 10.1111/echo.13587
EP  - 967
PY  - 2017///
SN  - 0742-2822
SP  - 956
TI  - Automatic detection of end-diastolic and end-systolic frames in 2D echocardiography
T2  - ECHOCARDIOGRAPHY-A JOURNAL OF CARDIOVASCULAR ULTRASOUND AND ALLIED TECHNIQUES
UR  - http://dx.doi.org/10.1111/echo.13587
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000405576100001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/51345
VL  - 34
ER  -
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