Визначення Загального Стандарту Для 2d-Спекл-Тре-Кінг Ехокардіографії (Проект Консенсусу Робочої Групи Асоціації Серцево-Судинних  Хірургів України Та Українського Товариства Кардіологів)

Background. Recent studies show that cardiovascular diseases, including coronary heart disease, are the leading causes of death and one of the main factors of disability worldwide. The detection of cases of this type of disease over the past 30 years has increased from 271 million to 523 million and the number of deaths – from 12.1 million to 18.6 million. Cardiovascular diseases are the main cause of death among the population of Ukraine and, according to this indicator, the country remains one of the world leaders. Coronary heart disease is the leading factor in the loss of health in Ukraine and modern diagnostic methods, including machine learning algorithms, are increasingly being used for timely detection. Objective. According to the data of speckle-tracking echocardiography using the random forest method, construct classification algorithms for diagnosing violations of the kinematics of left ventricular contractions in patients with coronary heart disease at rest, and when using an echostress test with a dobutamine test. Methods. Speckle-tracking echocardiography was used to examine 40 patients with coronary heart disease and 16 in whom no cardiac pathology was found. Echocardiography was recorded in B mode in three positions: along the long axis, in 4-chamber, and 2-chamber positions. In total, 6245 frames of the video stream were used: 1871 – without cardiac abnormalities, and 4374 – in the presence of pathology during the examination. 56 patients (2509 frames of video data) were examined without the use of a dobutamine test and 38 patients (3736 frames of video data) – using an echostress test with a dobutamine test if no disturbances were found at rest. Dobutamine doses of 10, 20, and 40 mcg were administered under the supervision of an anesthesiologist. The data of texture analysis of images were used as informative features. To build an algorithm for detecting coronary heart disease the random forest algorithm was applied. Results. At the first stage of the study, the diagnostic algorithms norma–pathology for the state of rest and dobutamine doses of 10, 20, and 40 mcg were constructed. Before applying the algorithm the samples were randomly divided into training (70%) and test (30%). The classifiers were evaluated for accuracy, sensitivity, and specificity. According to the test samples, the accuracy of diagnostic conclusions varied from 97 to 99%. At the second stage of the study, to increase the versatility of the models, the classifier was built for all images, without dividing them into dobutamine doses. The accuracy for the test samples also ranged from 96.6 to 97.8%. To construct diagnostic algorithms by the random forest method the data of texture analysis of images were used. Conclusions. High-precision classification models were obtained using the random forest algorithm. The developed models can be applied to the analysis of echocardiograms obtained in B mode on equipment that is not equipped with the speckle tracking technology.

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“…СТЕ реєструвалась у B-режимі в трьох позиціях (рис. 1): по довгій вісі, у 4-та 2-камерній позиціях [1,2].…”

Section: матеріал і методиunclassified

Random Forest Algorithm Construction for the Diagnosis of Coronary Heart Disease Based on Echocardiography Video Data Streams

Настенко

Maksymenko

Potashev

et al. 2021

Innov Biosyst Bioeng

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“…Одним із найсучасніших методів діагностики ішемічної хвороби серця (ІХС) є ехострес-тест (ЕСТ) із застосуванням медикаментозних (зазвичайдобутаміну) та фізичних навантажень у поєднанні зі спекл-трекінг ехокардіографією (СТЕ) [1,2]. Він дає змогу суттєво уточнити покази та протипокази до коронароангіографії (КАГ), що істотно знижує вартість лікувально-діагностичних заходів.…”

Section: вступunclassified

Comparative Analysis of Classification Algorithms in the Analysis of Medical Images From Speckle Tracking Echocardiography Video Data

Petrunina

Shevaga

Babenko

et al. 2021

Innov Biosyst Bioeng

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Background. Machine learning allows applying various intelligent algorithms to produce diagnostic and/or prognostic models. Such models can be used to determine the functional state of the heart, which is diagnosed by speckle-tracking echocardiography. To determine the patient's heart condition in detail, a classification approach is used in machine learning. Each of the classification algorithms has a different performance when applied to certain situations. Therefore, the actual task is to determine the most efficient algorithm for solving a specific task of classifying the patient's heart condition when applying the same speckle-tracking echocardiography data set. Objective. We are aimed to evaluate the effectiveness of the application of prognostic models of logistic regression, the group method of data handling (GMDH), random forest, and adaptive boosting (AdaBoost) in the construction of algorithms to support medical decision-making on the diagnosis of coronary heart disease. Methods. Video data from speckle-tracking echocardiography of 40 patients with coronary heart disease and 16 patients without cardiac pathology were used for the study. Echocardiography was recorded in B-mode in three positions: long axis, 4-chamber, and 2-chamber. Echocardiography frames that reflect the systole and diastole of the heart (308 samples in total) were taken as objects for classification. To obtain informative features of the selected objects, the genetic GMDH approach was applied to identify the best structure of harmonic textural features. We compared the efficiency of the following classification algorithms: logistic regression method, GMDH classifier, random forest method, and AdaBoost method. Results. Four classification models were constructed for each of the three B-mode echocardiography positions. For this purpose, the data samples were divided into 3: training sample (60%), validation sample (20%), and test sample (20%). Objective evaluation of the models on the test sample showed that the best classification method was random forest (90.3% accuracy on the 4-chamber echocardiography position, 74.2% on the 2-chamber, and 77.4% on the long axis). This was also confirmed by ROC analysis, wherein in all cases, the random forest was the most effective in classifying cardiac conditions. Conclusions. The best classification algorithm for cardiac diagnostics by speckle-tracking echocardiography was determined. It turned out to be a random forest, which can be explained by the ensemble approach of begging, which is inherent in this classification method. It will be the mainstay of further research, which is planned to be performed to develop a full-fledged decision support system for cardiac diagnostics.

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Dobutamine Stress Echocardiography with B-Mode Speckle Tracking in Patients after Acute Coronary Syndromes: Diagnostic and Prognostic Value

Potashev

Salo

Gavrylyshyn

et al. 2020

ujcvs

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Background. Speckle tracking echocardiography (STE) provides valuable data on myocardial function. STE during dobutamine stress echocardiography (DSE) allows reliable myocardial ischemia diagnosis. Aim. To evaluate STE during DSE in patients with coronary artery disease (CAD) after acute coronary syndrome (ACS) and its accuracy in ischemia diagnosis, assessment of myocardial viability and coronary reserve. Methods. We examined 58 patients (55 [94,8 %] men and 3 [5,2 %] women) men) with CAD after ACS admitted to our Institute for defining indications for coronary interventions. All the patients underwent DSE with STE for longitudinal strain quantification. Results. Mean left ventricular ejection fraction (LVEF) was 52.4±5.8%. Significant CAD according to coronary angiography (CAG) prior to DSE was proved in 38 (65.5%) patients. All the patients had a history of ACS up to 12 years before the examination (38 [65.5%] patients after percutaneous coronary intervention (PCI), including 18 [31.0%] primary PCI; 9 [15.5%] patients after coronary artery bypass grafting (CABG); 3 [5.2%] after graft PCI, 1 [1.72%] patient after aortic valve replacement (AVR) with dilated cardiomyopathy (DCM) phenotype). There were no significant complications during DSE: 5 (8.6%) cases of relatively low grade transient arrhythmia with no need for intervention. As per DSE results, we performed 35 (60.3%) revascularization interventions: 31 (58.6%) PCI’s and 4 (6.9%) CABG procedures with good outcomes. It was shown that DSE with STE sensitivity and specificity were 89.7% and 94.7% (AUC 0.92), respectively, with positive and negative predictive values of 97.2% and 81.8%, respectively, with extremely high OR 157.5, p<0.0001. Combined quantification of ΔGLS and ΔWMSI showed insignificantly lower sensitivity (86.3% [p=0.57]) and specificity (87.8% [p=0.19]) vs. integral semiquantitative ischemia markers with significantly lower overall method accuracy (AUC 0.79, p=0.047). Conclusions. DSE with STE as a visualization method is a safe and optimal method for ischemia diagnosis, as well as for myocardial viability and coronary reserve assessment in patients with CAD after ACS with the aim of risk stratification and defining indications for interventions and myocardial revascularization.

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Cited by 7 publications

References 20 publications

Random Forest Algorithm Construction for the Diagnosis of Coronary Heart Disease Based on Echocardiography Video Data Streams

Random Forest Algorithm Construction for the Diagnosis of Coronary Heart Disease Based on Echocardiography Video Data Streams

Comparative Analysis of Classification Algorithms in the Analysis of Medical Images From Speckle Tracking Echocardiography Video Data

Dobutamine Stress Echocardiography with B-Mode Speckle Tracking in Patients after Acute Coronary Syndromes: Diagnostic and Prognostic Value

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