Clinical utility of automated assessment of left ventricular ejection fraction using artificial intelligence–assisted border detection

Background Ejection fraction (EF) calculated from 2-dimensional echocardiography provides important prognostic and therapeutic information in patients with heart disease. However, quantification of EF requires planimetry and is time-consuming. As a result, visual assessment is frequently used but is...

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Bibliographic Details
Published in:The American heart journal 2008-03, Vol.155 (3), p.562-570
Main Authors: Rahmouni, Hind W., MD, Ky, Bonnie, MD, Plappert, Ted, CVT, Duffy, Kevin, MD, Wiegers, Susan E., MD, FACC, Ferrari, Victor A., MD, FACC, Keane, Martin G., MD, FACC, Kirkpatrick, James N., MD, Silvestry, Frank E., MD, FACC, St. John Sutton, Martin, MD
Format: Article
Language:English
Subjects:
Artificial Intelligence
Biological and medical sciences
Cardiology
Cardiology. Vascular system
Cardiovascular
Cardiovascular system
Echocardiography - methods
Endocardium - diagnostic imaging
Endocardium - pathology
Female
Heart attacks
Heart Diseases - diagnostic imaging
Heart Diseases - pathology
Heart Diseases - physiopathology
Heart Ventricles - diagnostic imaging
Heart Ventricles - pathology
Heart Ventricles - physiopathology
Humans
Image Processing, Computer-Assisted - methods
Investigative techniques of hemodynamics
Investigative techniques, diagnostic techniques (general aspects)
Magnetic Resonance Imaging - methods
Male
Medical equipment
Medical sciences
Middle Aged
NMR
Nuclear magnetic resonance
Observer Variation
Reproducibility of Results
Severity of Illness Index
Software
Stroke Volume - physiology
Ventricular Function, Left - physiology
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Summary:Background Ejection fraction (EF) calculated from 2-dimensional echocardiography provides important prognostic and therapeutic information in patients with heart disease. However, quantification of EF requires planimetry and is time-consuming. As a result, visual assessment is frequently used but is subjective and requires extensive experience. New computer software to assess EF automatically is now available and could be used routinely in busy digital laboratories (>15 000 studies per year) and in core laboratories running large clinical trials. We tested Siemens AutoEF software (Siemens Medical Solutions, Erlangen, Germany) to determine whether it correlated with visual estimates of EF, manual planimetry, and cardiac magnetic resonance (CMR). Methods Siemens AutoEF is based on learned patterns and artificial intelligence. An expert and a novice reader assessed EF visually by reviewing transthoracic echocardiograms from consecutive patients. An experienced sonographer quantified EF in all studies using Simpson's method of disks. AutoEF results were compared to CMR. Results Ninety-two echocardiograms were analyzed. Visual assessment by the expert ( R = 0.86) and the novice reader ( R = 0.80) correlated more closely with manual planimetry using Simpson's method than did AutoEF ( R = 0.64). The correlation between AutoEF and CMR was 0.63, 0.28, and 0.51 for EF, end-diastolic and end-systolic volumes, respectively. Conclusion The discrepancies in EF estimates between AutoEF and manual tracing using Simpson's method and between AutoEF and CMR preclude routine clinical use of AutoEF until it has been validated in a number of large, busy echocardiographic laboratories. Visual assessment of EF, with its strong correlation with quantitative EF, underscores its continued clinical utility.
ISSN:0002-8703
1097-6744
DOI:10.1016/j.ahj.2007.11.002