Table 2.
Key studies in AI for COVID-19 imaging
| Publication | Focus | Methodology | Results |
|---|---|---|---|
| Li, Lin, et al40 | Distinguishing COVID-19 from other pneumonia. | 4536 3D volumetric chest CT exams from 3506 patients acquired at 6 medical centers. Deep learning neural network methodology used. | COVID-19 identified on CT (AUROC 0.96). Community acquired pneumonia identified on CT (AUROC 0.95). Overlap found in CT imaging findings of all viral pneumonias with other chest diseases. |
| Wang, Linda, et al41 | Detection of COVID-19 on CXR. | 13,975 CXR images across 13,870 patient cases. Deep convolutional neural network. |
N/A. (Open-source tool for public use). |
| Wang, Shuai, et al42 | Detection of COVID-19 on CT. | CT images from 99 patients (of which 55 cases were of typical viral pneumonia and 44 of COVID-19). Convolutional neural net. | AUC of 0.90 (internal validation) and 0.78 (external validation). Sensitivity of 80.5% and 67.1%, specificity of 84.2% 76.4%, accuracy of 82.9% and 73.1%, the negative prediction value of 0.88 and 0.81. |
| Apostolopoulos, Ioannis D., et al43 | Detection of COVID-19 on CXR. |
1427 X-ray images (of which 224 images were of COVID-19 disease, 700 images of common bacterial pneumonia, and 504 images of normal conditions). Transfer Learning. | Accuracy, sensitivity, and specifcity obtained is 96.78%, 98.66%, and 96.46%, respectively. |
| Narin, Ali, et al44 | Comparing the performance of various deep learning methods for detection of COVID-19 on CXR. | Convolutional neural network-based models (ResNet50, InceptionV3 and Inception-ResNetV2 | Pre-trained ResNet50 model provides the highest classification performance with 98% accuracy among other 2 proposed models (97% accuracy for InceptionV3 and 87% accuracy for Inception-ResNetV2. |
| Afshar, Parnian, et al45 | Detection of COVID-19 on CXR. | Convolutional neural network. | Accuracy of 95.7%, Sensitivity of 90%, Specificity of 95.8%, and Area Under the Curve (AUC) of 0.97. Pretraining improved accuracy to 98.3% and specificity to 98.6%. |
| Fang M, et al46 | Detection of COVID-19 on CT. | 75 pneumonia patients (46 with COVID-19, 29 other types of pneumonias). Radiomics + Support vector machine | AUCs of 0.862 and 0.826 in the training set and the test set, respectively. Predictive ability is not affected by gender, age, chronic disease and degree of severity |
| Tang Z, et al47 | Severity assessment of COVID-19 | 176 COVID-19 patients. Random forest. | Accuracy 87.5% True positive rate 93.3%, True negative rate 74.5%. |