Mohammad-Rahimi Hossein, Nadimi Mohadeseh, Ghalyanchi-Langeroudi Azadeh, Taheri Mohammad, Ghafouri-Fard Soudeh
Dental Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences (TUMS), Tehran, Iran.
Front Cardiovasc Med. 2021 Mar 25;8:638011. doi: 10.3389/fcvm.2021.638011. eCollection 2021.
Coronavirus disease, first detected in late 2019 (COVID-19), has spread fast throughout the world, leading to high mortality. This condition can be diagnosed using RT-PCR technique on nasopharyngeal and throat swabs with sensitivity values ranging from 30 to 70%. However, chest CT scans and X-ray images have been reported to have sensitivity values of 98 and 69%, respectively. The application of machine learning methods on CT and X-ray images has facilitated the accurate diagnosis of COVID-19. In this study, we reviewed studies which used machine and deep learning methods on chest X-ray images and CT scans for COVID-19 diagnosis and compared their performance. The accuracy of these methods ranged from 76% to more than 99%, indicating the applicability of machine and deep learning methods in the clinical diagnosis of COVID-19.
2019年末首次发现的冠状病毒病(COVID-19)已在全球迅速传播,导致高死亡率。这种疾病可以通过对鼻咽和咽喉拭子进行逆转录聚合酶链反应(RT-PCR)技术诊断,灵敏度值在30%至70%之间。然而,据报道胸部CT扫描和X光图像的灵敏度值分别为98%和69%。在CT和X光图像上应用机器学习方法有助于COVID-19的准确诊断。在本研究中,我们回顾了在胸部X光图像和CT扫描上使用机器学习和深度学习方法进行COVID-19诊断的研究,并比较了它们的性能。这些方法的准确率在76%至99%以上,表明机器学习和深度学习方法在COVID-19临床诊断中的适用性。