Liastuti Lies Dina, Budi Siswanto Bambang, Sukmawan Renan, Jatmiko Wisnu, Nursakina Yosilia, Putri Rindayu Yusticia Indira, Jati Grafika, Nur Aqsha Azhary
Department of Cardiology and Vascular Medicine, Faculty of Medicine Universitas Indonesia, National Cardiovascular Center Harapan Kita Hospital, 15810 Jakarta, Indonesia.
Department of Cardiology and Vascular Medicine, Faculty of Medicine Universitas Indonesia, Dr. Cipto Mangunkusumo Hospital, 10430 Jakarta, Indonesia.
Rev Cardiovasc Med. 2022 Dec 12;23(12):402. doi: 10.31083/j.rcm2312402. eCollection 2022 Dec.
Heart failure remains a considerable burden to healthcare in Asia. Early intervention, mainly using echocardiography, to assess cardiac function is crucial. However, due to limited resources and time, the procedure has become more challenging during the COVID-19 pandemic. On the other hand, studies have shown that artificial intelligence (AI) is highly potential in complementing the work of clinicians to diagnose heart failure accurately and rapidly.
We systematically searched Europe PMC, ProQuest, Science Direct, PubMed, and IEEE following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and our inclusion and exclusion criteria. The 14 selected works of literature were then assessed for their quality and risk of bias using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies).
A total of 2105 studies were retrieved, and 14 were included in the analysis. Five studies posed risks of bias. Nearly all studies included datasets in the form of 3D (three dimensional) or 2D (two dimensional) images, along with apical four-chamber (A4C) and apical two-chamber (A2C) being the most common echocardiography views used. The machine learning algorithm for each study differs, with the convolutional neural network as the most common method used. The accuracy varies from 57% to 99.3%.
To conclude, current evidence suggests that the application of AI leads to a better and faster diagnosis of left heart failure through echocardiography. However, the presence of clinicians is still irreplaceable during diagnostic processes and overall clinical care; thus, AI only serves as complementary assistance for clinicians.
心力衰竭仍是亚洲医疗保健领域的一项重大负担。早期干预，主要是使用超声心动图来评估心脏功能，至关重要。然而，由于资源和时间有限，在新冠疫情期间，这一过程变得更具挑战性。另一方面，研究表明，人工智能在辅助临床医生准确、快速诊断心力衰竭方面具有巨大潜力。
我们按照系统评价和Meta分析的首选报告项目（PRISMA）指南以及我们的纳入和排除标准，系统检索了欧洲生物医学中心（Europe PMC）、ProQuest、科学Direct、PubMed和电气与电子工程师协会（IEEE）。然后，使用QUADAS-2（诊断准确性研究的质量评估）对选定的14篇文献作品进行质量和偏倚风险评估。
共检索到2105项研究，14项纳入分析。5项研究存在偏倚风险。几乎所有研究都包括三维（3D）或二维（2D）图像形式的数据集，心尖四腔心（A4C）和心尖两腔心（A2C）是最常用的超声心动图视图。每项研究的机器学习算法各不相同，卷积神经网络是最常用的方法。准确率从57%到99.3%不等。
总之，目前的证据表明，人工智能的应用可通过超声心动图更好、更快地诊断左心衰竭。然而，在诊断过程和整体临床护理中，临床医生的存在仍然不可替代；因此，人工智能仅作为临床医生的辅助手段。
