Table 2.

Applications of artificial intelligence in CT diagnosis of COVID-19.

Place of Study	Authors	Application used	Sample Size	Accuracy
China	Wang. et al. [7]	Modified inception transfer-learning model	1065 CT images (325 COVID-19 and 740 viral pneumonia)	Accuracy: 79.3% Specificity: 0.83 Sensitivity: 0.67
	Cheng et al. [9]	2D deep convolutional neural network	970 CT volumes of 496 patients with confirmed COVID-19 and 1385 negative cases	Accuracy: 94.98% AUC: 97.91% Sensitivity: 94.06%, Specificity: 95.47%
	Xu et al. [12]	3-dimensional deep learning model	A total of 618 CT samples were collected: 219 from 110 patients	Accuracy: 86.7%
	Li et al. [13]	COVID-19 detection neural network (COVNet)	4356 chest CT exams from 3322 patients	Accuracy: 95%
Toronto, Canada	Wang, Lin, Wong [10]	COVID-Net: A deep CNN	16,756 chest radiography images across 13,645 patient	Accuracy: 92.4%
Thailand, Hong Kong etc.	Shannon [16]	real-time RT-PCR assay	340 clinical specimens from 246 patients with confirmed or suspected SARS-CoV infection	Potential detection limit of <10 genomic copies per reaction
Global	Narin, Kaya, Pamuk [8]	Chest X-ray images of 50 normal and 50 COVID-19 patients	ResNet50, InceptionV3 and Inception- ResNetV2	ResNet: 50 98% Inception V3: 97% Inception-ResNetV2:87%