Ribeiro Pedro, Marques João Alexandre Lobo, Rodrigues Pedro Miguel
CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Universidade Católica Portuguesa, Rua de Diogo Botelho 1327, 4169-005 Porto, Portugal.
Laboratory of Applied Neurosciences, University of Saint Joseph, Macao SAR 999078, China.
Bioengineering (Basel). 2023 Feb 3;10(2):198. doi: 10.3390/bioengineering10020198.
Since the beginning of 2020, Coronavirus Disease 19 (COVID-19) has attracted the attention of the World Health Organization (WHO). This paper looks into the infection mechanism, patient symptoms, and laboratory diagnosis, followed by an extensive assessment of different technologies and computerized models (based on Electrocardiographic signals (ECG), Voice, and X-ray techniques) proposed as a diagnostic tool for the accurate detection of COVID-19. The found papers showed high accuracy rate results, ranging between 85.70% and 100%, and F1-Scores from 89.52% to 100%. With this state-of-the-art, we concluded that the models proposed for the detection of COVID-19 already have significant results, but the area still has room for improvement, given the vast symptomatology and the better comprehension of individuals' evolution of the disease.
自2020年初以来，新型冠状病毒肺炎（COVID-19）引起了世界卫生组织（WHO）的关注。本文探讨了其感染机制、患者症状和实验室诊断，随后对作为准确检测COVID-19的诊断工具所提出的不同技术和计算机模型（基于心电图信号（ECG）、语音和X射线技术）进行了广泛评估。所发现的论文显示出较高的准确率结果，介于85.70%至100%之间，F1分数在89.52%至100%之间。基于这种最新技术水平，我们得出结论，所提出的用于检测COVID-19的模型已经取得了显著成果，但鉴于该疾病广泛的症状表现以及对个体病情演变的更好理解，该领域仍有改进空间。
