Abstract
The present study describes an auxiliary tool in the diagnosis of left ventricular (LV) segmental wall motion (WM) abnormalities based on color-coded echocardiographic WM images. An artificial neural network (ANN) was developed and validated for grading LV segmental WM using data from color kinesis (CK) images, a technique developed to display the timing and magnitude of global and regional WM in real time. We evaluated 21 normal subjects and 20 patients with LVWM abnormalities revealed by two-dimensional echocardiography. CK images were obtained in two sets of viewing planes. A method was developed to analyze CK images, providing quantitation of fractional area change in each of the 16 LV segments. Two experienced observers analyzed LVWM from two-dimensional images and scored them as: 1) normal, 2) mild hypokinesia, 3) moderate hypokinesia, 4) severe hypokinesia, 5) akinesia, and 6) dyskinesia. Based on expert analysis of 10 normal subjects and 10 patients, we trained a multilayer perceptron ANN using a back-propagation algorithm to provide automated grading of LVWM, and this ANN was then tested in the remaining subjects. Excellent concordance between expert and ANN analysis was shown by ROC curve analysis, with measured area under the curve of 0.975. An excellent correlation was also obtained for global LV segmental WM index by expert and ANN analysis (R2 = 0.99). In conclusion, ANN showed high accuracy for automated semi-quantitative grading of WM based on CK images. This technique can be an important aid, improving diagnostic accuracy and reducing inter-observer variability in scoring segmental LVWM.
Highlights
One of the techniques most commonly used to quantify left ventricular systolic function in patients is based on visual analysis of myocardial thickening and wall motion by an experienced and trained observer
Color kinesis images were obtained for 21 normal subjects (8 women and 13 men) aged 23 to 61 years, who were completely asymptomatic and who presented a normal two-dimensional echocardiogram, and for 20 patients (4 women and 16 men) aged 24 to 70 years who had segmental or diffuse left ventricular dysfunction as documented by two-dimensional echocardiography
The accuracy of the Artificial neural network (ANN) in identifying and grading left ventricular wall motion abnormalities was determined in a group of normal subjects and of patients presenting left ventricular systolic dysfunction
Summary
One of the techniques most commonly used to quantify left ventricular systolic function in patients is based on visual analysis of myocardial thickening and wall motion by an experienced and trained observer. This assessment is subjective, highly dependent on the observer’s experience and the results obtained are semi-quantitative. Artificial neural network (ANN) applications in clinical medicine, and in Cardiology, have increased in the last few years [4,5,6,7,8,9] This tool has the ability to recognize and classify complex patterns of biological information by learning from examples, to identify relations in input data, and to provide automated interpretation of clinical or diagnostic information
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