Automatic detection of end‐diastolic and end‐systolic frames in 2D echocardiography

• Published in: Echocardiography (Mount Kisco, N.Y.) Vol. 34; no. 7; pp. 956 - 967
• Main Authors: 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.
• Format: Journal Article
• Language: English
• Published: United States 01.07.2017
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; cardiac imaging; Diastole; echocardiography; Echocardiography - methods; Female; Heart - diagnostic imaging; Heart - physiology; Humans; Male; Middle Aged; myocardial velocity; Observer Variation; Reproducibility of Results; Systole; ultrasound imaging; ultrasound imaging; myocardial velocity; cardiac imaging; echocardiography
• ISSN: 0742-2822; 1540-8175; 1540-8175
• DOI: 10.1111/echo.13587

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.