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 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.


CONCLUSIONS
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.
Rahmouni
Hind W
HW
Department of Medicine, Cardiovascular Division, University of Pennsylvania, School of Medicine, Philadelphia, PA, USA.
Ky
Bonnie
B
Plappert
Ted
T
Duffy
Kevin
K
Wiegers
Susan E
SE
Ferrari
Victor A
VA
Keane
Martin G
MG
Kirkpatrick
James N
JN
Silvestry
Frank E
FE
St John Sutton
Martin
M
D001185:Artificial Intelligence; D004452:Echocardiography / Q000379:methods*; D004699:Endocardium / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D006331:Heart Diseases / Q000000981:diagnostic imaging / Q000473:pathology / Q000503:physiopathology*; D006352:Heart Ventricles / Q000000981:diagnostic imaging / Q000473:pathology / Q000503:physiopathology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D015203:Reproducibility of Results; D012720:Severity of Illness Index; D013318:Stroke Volume / Q000502:physiology*; D016277:Ventricular Function, Left / Q000502:physiology*