Artificial Intelligence in Medicine ( IF 7.5 ) Pub Date : 2021-09-09 , DOI: 10.1016/j.artmed.2021.102165 Vilson Soares de Siqueira 1 , Moisés Marcos Borges 2 , Rogério Gomes Furtado 2 , Colandy Nunes Dourado 2 , Ronaldo Martins da Costa 3
![]() |
The echocardiogram is a test that is widely used in Heart Disease Diagnoses. However, its analysis is largely dependent on the physician's experience. In this regard, artificial intelligence has become an essential technology to assist physicians. This study is a Systematic Literature Review (SLR) of primary state-of-the-art studies that used Artificial Intelligence (AI) techniques to automate echocardiogram analyses. Searches on the leading scientific article indexing platforms using a search string returned approximately 1400 articles. After applying the inclusion and exclusion criteria, 118 articles were selected to compose the detailed SLR. This SLR presents a thorough investigation of AI applied to support medical decisions for the main types of echocardiogram (Transthoracic, Transesophageal, Doppler, Stress, and Fetal). The article's data extraction indicated that the primary research interest of the studies comprised four groups: 1) Improvement of image quality; 2) identification of the cardiac window vision plane; 3) quantification and analysis of cardiac functions, and; 4) detection and classification of cardiac diseases. The articles were categorized and grouped to show the main contributions of the literature to each type of ECHO. The results indicate that the Deep Learning (DL) methods presented the best results for the detection and segmentation of the heart walls, right and left atrium and ventricles, and classification of heart diseases using images/videos obtained by echocardiography. The models that used Convolutional Neural Network (CNN) and its variations showed the best results for all groups. The evidence produced by the results presented in the tabulation of the studies indicates that the DL contributed significantly to advances in echocardiogram automated analysis processes. Although several solutions were presented regarding the automated analysis of ECHO, this area of research still has great potential for further studies to improve the accuracy of results already known in the literature.
中文翻译:
![](https://static.x-mol.com/jcss/images/paperTranslation.png)
人工智能应用于支持超声心动图图像自动分析的医疗决策:系统评价
超声心动图是一种广泛用于心脏病诊断的测试。然而,其分析在很大程度上取决于医生的经验。在这方面,人工智能已成为协助医师的关键技术。本研究是对使用人工智能 (AI) 技术自动进行超声心动图分析的主要最新研究的系统文献综述 (SLR)。使用搜索字符串在领先的科学文章索引平台上搜索返回了大约 1400 篇文章。应用纳入和排除标准后,选择118篇文章组成详细的SLR。本 SLR 对应用于支持主要超声心动图类型(经胸、经食道、多普勒、压力和胎儿)的医疗决策的 AI 进行了彻底调查。文章' 数据提取表明,研究的主要研究兴趣包括四组:1)图像质量的提高;2) 心窗视野平面的识别;3) 心脏功能的量化和分析,以及;4)心脏疾病的检测和分类。这些文章被分类和分组以显示文献对每种类型的 ECHO 的主要贡献。结果表明,深度学习 (DL) 方法在心脏壁、左右心房和心室的检测和分割以及使用超声心动图获得的图像/视频对心脏病进行分类方面取得了最佳结果。使用卷积神经网络 (CNN) 及其变体的模型在所有组中都显示出最佳结果。研究列表中提供的结果所产生的证据表明,DL 对超声心动图自动分析过程的进步做出了重大贡献。尽管针对 ECHO 的自动分析提出了几种解决方案,但该研究领域仍有很大的潜力进行进一步研究,以提高文献中已知结果的准确性。