Table 2. Applications of AI in COVID-19 diagnosis.
| First author [year] (reference) | Country (region) | Modality | Model | Data source | Sample size | Application area | Result |
|---|---|---|---|---|---|---|---|
| Gomes et al. [2020] (32) | Brazil | DNA sequences | RF/NBC/IBL/MLP/SVM | NIAID Virus Pathogen Database/Analysis Resource (ViPR)/Genome Reference Consortium | Twenty-five different viruses from NIAID Virus Pathogen Database/Analysis Resource (ViPR): 347,363; viruses from Genome Reference Consortium: 103,959 | Laboratory-based diagnosis: RT-PCR method | RF (results from dataset with 30% overlap) sensitivity: 0.822222±0.05613; specificity: 0.99974±0.00001; AUC: 0.99884± 0.0025; MLP (results from dataset with 30% overlap) sensitivity: 0.97386±0.03052; specificity: 0.96151±0.00246; AUC: 0.97353±0.01863; MLP (results from dataset with 50% overlap) sensitivity: 0.98824±0.01198; specificity: 0.99860±0.00020; AUC: 0.99947±0.00056 |
| Cady et al. [2021] (33) | USA | Blood sample | SVM | Dataset: COVID-19 negative samples plus COVID-19 positive samples | Negative samples: obtained from the Lyme Disease Biobank prior to the COVID-19 pandemic; positive samples: obtained from donors within New York state or from the Wadsworth Center, New York State Department of Health | Laboratory-based diagnosis: antibody response | Selectivity of human blood serum: 100%, sensitivity of dried blood spot samples: 86.7% |
| Kukar et al. [2021] (34) | Switzerland | Blood sample | CRISP-DM based machine learning/DNN/RF/XGBoost | Dataset: COVID-19 negative cases plus COVID-19 positive cases | Healthy cases: 5,108 from March, 2012 to April, 2019; COVID-19 cases: 160 from March/April 2020; other cases (different bacterial and viral infections): 225 from March, 2012 to April, 2019 | Laboratory-based diagnosis: routine blood tests | Sensitivity: 81.9%, specificity: 97.9%, AUC: 0.97 |
| Wang et al. [2021] (35) | China | CT image | 3D U-Net++/ residual network (ResNet-50) |
Training dataset: 1,136 (723 were positive); testing dataset: 282 cases (154 were positive) | Healthy cases: 70; COVID-19 cases: 723; other cases (inflammation or tumors): 343 | Medical images diagnosis: CT image | Sensitivity: 0.974, specificity: 0.922, AUC: 0.991 |
| Xu et al. [2020] (36) | China | CT image | 3D CNN segmentation model/residual network (ResNet-18) | Training dataset: 528 CT samples (COVID-19: 189; IAVP: 194; healthy: 145); testing dataset: 90 CT samples (COVID-19: 30; IAVP: 30; healthy: 30) | Healthy cases/CT samples: 175/175; COVID-19 cases/CT samples: 110/219; other cases IAVP/CT samples: 224/224 | Medical images diagnosis: CT image | Accuracy: 86.7% |
| Narin et al. [2021] (37) | Globe | Chest X-ray radiographs | CNN based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) | Dataset-1: 341 (COVID-19)/2,800 (healthy); dataset-2: 341 (COVID-19)/1,493 (viral); dataset-3: 341 (COVID-19)/2,772 (bacterial) | Healthy X-ray samples: 2,800; COVID-19 X-ray samples: 341; other viral pneumonia X-ray samples: 1,493; other bacterial pneumonia X-ray samples: 2,772 | Medical images diagnosis: chest X-ray radiographs | Accuracy for dataset-1: 96.1%, accuracy for dataset-2: 99.5%, accuracy for dataset-3: 99.7% |
| Zhang et al. [2020] (38) | Globe | Chest X-ray radiographs | CAAD model | Dataset-1 cases/X-ray samples: 33/50 (COVID-19); 492/714 (other pneumonia); dataset-2 cases/X-ray samples: 37/50 (COVID-19); 516/717 (other pneumonia) | COVID-19 cases/X-ray samples: 70/100; other pneumonia cases/X-ray samples: 1,008/1,431 | Medical images diagnosis: chest X-ray radiographs | Sensitivity: 96.0%, specificity: 70.7% AUC: 0.952 |
| Arpaci et al. [2021] (39) | China | Clinical features | BayesNet/Logistic/IBk/CR/PART/J48 | 114 subjects from the Taizhou hospital of Zhejiang Province in China from January 17, 2020 to February 1, 2020 | COVID-19 cases: 32; non COVID-19 cases: 82 | Respiratory pattern and symptoms diagnosis | Accuracy of BayesNet: 71.93%; accuracy of Logistic: 80.7%; accuracy of IBk: 72.81%; accuracy of CR: 84.21%; accuracy of PART: 76.32%; accuracy of J48: 73.68% |
AI, artificial intelligence; COVID-19, coronavirus disease 2019; RF, random forests; NBC, naive Bayes classifier; IBL, instance-based learner; MLP, multilayer perceptron; SVM, support vector machine; DNN, deep neural networks; XGBoost, extreme gradient boosting machine; CAAD, confidence-aware anomaly detection model; BayesNet, Bayes classifier; Logistic, logistic-regression; IBk, lazy-classifier; CR, classification via regression; PART, rule-learner; J48, decision-tree; IAVP, influenza-A viral pneumonia; RT-PCR, reverse‐transcriptase polymerase chain reaction; AUC, area under the curve.