Classification of acute myocardial ischemia by artificial neural network using echocardiographic strain waveforms.
Echocardiographic strain waveforms are highly variable, so their interpretation is experience-dependent and subjective. We tested whether an artificial neural network (ANN) can distinguish between strain waveforms obtained at baseline and during experimentally induced acute ischemia. An open-chest model of coronary occlusion and acute ischemia was used in 14 adult pigs. Strain waveforms were obtained using a GE Vivid 7 ultrasound system. An ANN design was implemented in MATLAB, and backpropagation and "leave-one-out" processes were used to train and test it. Specificity of 86% and sensitivity of 87% suggest that ANNs could aid in diagnostic prescreening of echocardiographic strain waveforms.
McMahon
Eileen M
EM
Mayo Clinic College of Medicine, 13400 East Shea Boulevard, Scottsdale, AZ 85259, USA.
Korinek
Josef
J
Yoshifuku
Shiro
S
Sengupta
Partho P
PP
Manduca
Armando
A
Belohlavek
Marek
M
D000208:Acute Disease; D000818:Animals; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation*; D004195:Disease Models, Animal; D004452:Echocardiography / Q000295:instrumentation*; D004562:Electrocardiography / Q000295:instrumentation*; D005103:Expert Systems; D005260:Female; D007091:Image Processing, Computer-Assisted / Q000295:instrumentation*; D009200:Myocardial Contraction / Q000502:physiology*; D009203:Myocardial Infarction / Q000000981:diagnostic imaging* / Q000503:physiopathology; D017202:Myocardial Ischemia / Q000000981:diagnostic imaging* / Q000503:physiopathology; D016571:Neural Networks, Computer; D011379:Prognosis; D012985:Software Design; D013552:Swine