Abbasi Habashi Soheila, Koyuncu Murat, Alizadehsani Roohallah
Department of Computer Engineering, Atilim University, 06830 Ankara, Turkey.
Department of Information Systems Engineering, Atilim University, 06830 Ankara, Turkey.
Diagnostics (Basel). 2023 May 16;13(10):1749. doi: 10.3390/diagnostics13101749.
Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.
严重急性呼吸综合征冠状病毒2(SARS-CoV-2)引发了一种名为COVID-19的疾病,它是一类急性呼吸综合征,对全球经济和医疗系统造成了重大影响。这种病毒通过一种名为逆转录聚合酶链反应(RT-PCR)检测的传统技术进行诊断。然而,RT-PCR通常会产生大量假阴性和错误结果。目前的研究表明,COVID-19也可以通过成像检查来诊断,包括CT扫描、X光和血液检测。然而,由于成本高、辐射剂量大以及设备数量不足,X光和CT扫描并不总是能用于患者筛查。因此,需要一种成本更低、速度更快的诊断模型来识别COVID-19的阳性和阴性病例。血液检测操作简便,成本低于RT-PCR和成像检测。由于COVID-19感染期间常规血液检测中的生化参数会发生变化,它们可能为医生提供有关COVID-19诊断的确切信息。本研究回顾了一些新出现的基于人工智能(AI)的方法,这些方法利用常规血液检测来诊断COVID-19。我们收集了有关研究资源的信息,并查阅了从IEEE、Springer、Elsevier和MDPI等各种出版商精心挑选的92篇文章。然后,将这92项研究分为两个表格,其中包含使用机器学习和深度学习模型在使用常规血液检测数据集的情况下诊断COVID-19的文章。在这些研究中,用于诊断COVID-19的随机森林和逻辑回归是最广泛使用的机器学习方法,最广泛使用的性能指标是准确率、灵敏度、特异性和AUC。最后,我们通过讨论和分析这些使用机器学习和深度学习模型以及常规血液检测数据集进行COVID-19检测的研究来得出结论。这项调查可以作为新手/初级研究人员进行COVID-19分类研究的起点。