Abstract：
BACKGROUND: Evaluation of standard echocardiographic examination with artificial intelligence may help in the diagnosis of myocardial viability and function recovery after acute coronary syndrome.
METHODS: Sixty-one consecutive patients with acute coronary syndrome were enrolled in the present study (43 men, mean age 61 ± 9 years). All patients underwent percutaneous coronary intervention (PCI). 533 segments of the heart echo images were used. After 12 ± 1 months of follow-up, patients had an echocardiographic evaluation. After PCI each patient underwent cardiac magnetic resonance (CMR) with late enhancement and low-dose dobutamine echocardiographic examination. For texture analysis, custom software was used (MaZda 5.20, Institute of Electronics).Linear and non-linear (neural network) discriminative analyses were performed to identify the optimal analytic method correlating with CMR regarding the necrosis extent and viability prediction after follow-up. Texture parameters were analyzed using machine learning techniques: Artificial Neural Networks, Namely Multilayer Perceptron, Nonlinear Discriminant Analysis, Support Vector Machine, and Adaboost algorithm.
RESULTS: The mean concordance between the CMR definition of viability and three classification models in Artificial Neural Networks varied from 42% to 76%. Echo-based detection of non-viable tissue was more sensitive in the segments with the highest relative transmural scar thickness: 51-75% and 76-99%. The best results have been obtained for images with contrast for red and grey components (74% of proper classification). In dobutamine echocardiography, the results of appropriate prediction were 67% for monochromatic images.
CONCLUSIONS: Detection and semi-quantification of scar transmurality are feasible in echocardiographic images analyzed with artificial intelligence. Selected analytic methods yielded similar accuracy, and contrast enhancement contributed to the prediction accuracy of myocardial viability after myocardial infarction in 12 months of follow-up.
METHODS: Sixty-one consecutive patients with acute coronary syndrome were enrolled in the present study (43 men, mean age 61 ± 9 years). All patients underwent percutaneous coronary intervention (PCI). 533 segments of the heart echo images were used. After 12 ± 1 months of follow-up, patients had an echocardiographic evaluation. After PCI each patient underwent cardiac magnetic resonance (CMR) with late enhancement and low-dose dobutamine echocardiographic examination. For texture analysis, custom software was used (MaZda 5.20, Institute of Electronics).Linear and non-linear (neural network) discriminative analyses were performed to identify the optimal analytic method correlating with CMR regarding the necrosis extent and viability prediction after follow-up. Texture parameters were analyzed using machine learning techniques: Artificial Neural Networks, Namely Multilayer Perceptron, Nonlinear Discriminant Analysis, Support Vector Machine, and Adaboost algorithm.
RESULTS: The mean concordance between the CMR definition of viability and three classification models in Artificial Neural Networks varied from 42% to 76%. Echo-based detection of non-viable tissue was more sensitive in the segments with the highest relative transmural scar thickness: 51-75% and 76-99%. The best results have been obtained for images with contrast for red and grey components (74% of proper classification). In dobutamine echocardiography, the results of appropriate prediction were 67% for monochromatic images.
CONCLUSIONS: Detection and semi-quantification of scar transmurality are feasible in echocardiographic images analyzed with artificial intelligence. Selected analytic methods yielded similar accuracy, and contrast enhancement contributed to the prediction accuracy of myocardial viability after myocardial infarction in 12 months of follow-up.
摘要：
背景：使用人工智能评估标准超声心动图检查可能有助于诊断急性冠脉综合征后的心肌活力和功能恢复。
方法：本研究连续纳入61例急性冠脉综合征患者（43例男性，平均年龄61±9岁)。所有患者均接受经皮冠状动脉介入治疗(PCI)。使用533段心脏回声图像。随访12±1个月,患者进行了超声心动图评估.PCI后，每位患者均接受了心脏磁共振（CMR），并进行了后期增强和低剂量多巴酚丁胺超声心动图检查。对于纹理分析，使用定制软件(MaZda5.20，电子研究所)。进行了线性和非线性（神经网络）判别分析，以确定与CMR相关的最佳分析方法，即随访后的坏死程度和生存力预测。使用机器学习技术分析纹理参数：人工神经网络，即多层感知器，非线性判别分析，支持向量机,和Adaboost算法。
结果：CMR对生存力的定义与人工神经网络中的三个分类模型之间的平均一致性从42％到76％不等。在相对透壁瘢痕厚度最高的节段中，基于回声的无活力组织检测更为敏感：51-75％和76-99％。对于具有红色和灰色分量的对比度的图像（适当分类的74％），已获得了最佳结果。在多巴酚丁胺超声心动图中，对于单色图像，适当预测的结果为67％。
结论：在人工智能分析的超声心动图图像中，对瘢痕穿壁性的检测和半定量是可行的。选定的分析方法产生了相似的准确性，和对比增强有助于预测心肌梗死后12个月随访的心肌生存力的准确性。
方法：本研究连续纳入61例急性冠脉综合征患者（43例男性，平均年龄61±9岁)。所有患者均接受经皮冠状动脉介入治疗(PCI)。使用533段心脏回声图像。随访12±1个月,患者进行了超声心动图评估.PCI后，每位患者均接受了心脏磁共振（CMR），并进行了后期增强和低剂量多巴酚丁胺超声心动图检查。对于纹理分析，使用定制软件(MaZda5.20，电子研究所)。进行了线性和非线性（神经网络）判别分析，以确定与CMR相关的最佳分析方法，即随访后的坏死程度和生存力预测。使用机器学习技术分析纹理参数：人工神经网络，即多层感知器，非线性判别分析，支持向量机,和Adaboost算法。
结果：CMR对生存力的定义与人工神经网络中的三个分类模型之间的平均一致性从42％到76％不等。在相对透壁瘢痕厚度最高的节段中，基于回声的无活力组织检测更为敏感：51-75％和76-99％。对于具有红色和灰色分量的对比度的图像（适当分类的74％），已获得了最佳结果。在多巴酚丁胺超声心动图中，对于单色图像，适当预测的结果为67％。
结论：在人工智能分析的超声心动图图像中，对瘢痕穿壁性的检测和半定量是可行的。选定的分析方法产生了相似的准确性，和对比增强有助于预测心肌梗死后12个月随访的心肌生存力的准确性。
