Table 3.
The summary of studies in terms of the applications of AI in clinical research
| Reference | Task | Data source & size | Method | Results |
|---|---|---|---|---|
| Su et al. (March 2021) [164] | Explore albumin level between patients with COVID-19 and patients with sepsis. | 308 patients with COVID-19 and 363 patients with Sepsis | Chow's test, linear mixed-effects models, Fisher's exact test, t-test, and Wilcoxon rank-sum test | Two phases of alterations in albumin levels for patients with COVID-19 were found, which were not presented with patients with sepsis. |
| Liang et al. (May 2021) [85] | Estimate the risk of developing critical illness for patients with COVID-19 | 72 potential predictors were considered from 1,590 patients with COVID-19 in the 575 hospitals of 31 provincial administrative regions in China as of January 31, 2020. | Least Absolute Shrinkage and Selection Operator (LASSO) and Logistic Regression (LR) models | AUC=0.88 (95% CI, 0.84–0.93) on a validation cohort with 710 patients. |
| Burn et al. (October 2020) [165] | Explore the characteristics of patients with COVID-19 and influenza | 34,128 adult patients with COVID-19 and 84,585 patients with influenza (United States: 8,362, South Korea: 7,341, Spain: 18,425) |
Data-driven approach | Compared to patients with influenza, patients with COVID-19 were more male, younger, and with fewer comorbidities and lower medication use. |
| Roth et al. (May 2021) [166] | Investigate the characteristics of patients with COVID-19 in terms of in-hospital mortality in the United States | 20,736 adults with a diagnosis of COVID-19 in the US between March and November 2020. | A multiple mixed-effects logistic regression | The mortality rates for patients with COVID-19 were different between the months of March and April and later months in 2020, which were not fully explained by changes in age, sex, comorbidities, and disease severity. |
| Williams et al. (May 2021) [86] | Predict hospitalization, intensive services, and death for patients with COVID-19 | The cohort for model development has More than 2 million patients diagnosed with influenza or flu-like symptoms any time prior to 2020. The cohort for model validation included 43,061 COVID-19 patients form South Korea, Spain and the United States. |
Data-driven approach | The ranges of AUC on validation in terms of three outcomes including hospitalization, intensive services, and death were 0.73–0.81, 0.73–0.91, and 0.82–0.90, respectively. |
| Liang et al. (July 2020) [90] | Predict the risk of COVID-19 patients developing critical illness | 74 baseline clinical features at admission from 1,590 patients with COVID-19 in the 575 hospitals of 31 provincial administrative regions in China as of January 31, 2020. | Feedforward neural network. | The proposed model was validated on three separate cohorts including 1,393 patients and showed the concordance index of 0.890, 0.852, and 0.967, respectively. |
| Yang et al. (December 2020) [167] | Investigate population drifting in terms of COVID-19 patients | 21 routine blood tests from 5,785 patients in ED of New York Presbyterian Hospital/Weill Cornell Medical Center (NYPH/WCMC) between March 11 and June 30,2020. | Density-based spatial clustering of applications with noise (DBSCAN) and the Unified manifold approximation and projection (UMAP), t-test, Fisher's exact test. | The number of SARS-CoV-2 patients with the COVID-19 HRP became less and less from March to June 2020. |
| Zhang et al. (June 2020) [5] | Diagnose COVID-19 | 532,506 human lung CT scan images from 3,777 patients, China Consortium of Chest CT Image Investigation (CC—CCII) | CNN | Internal validation: Accuracy=92.49%; External validation: Accuracy=90.70%. |
| Wang et al. (May 2020) [75] |
Diagnose COVID-19 | Lung CT images: 5,372 patients from seven cities or provinces in China. | A fully automatic DL model (DenseNet121-FPN) | AUC 0.87 and 0.88 on two validation sets in distinguishing COVID-19 from other pneumonia and AUC 0.86 in distinguishing COVID-19 from viral pneumonia. |
| Ozturk et al. (June 2020) [78] | Diagnose COVID-19 | X-ray images: 127 COVID-19 cases, 500 no-finding, 500 pneumonia. The Cohen JP and the ChestX-ray8 databases |
CNN | An accuracy of 98.08% for classifying COVID-19 and No-findings and 87.02% for classifying COVID-19, No-findings, and Pneumonia. |
| Chen et al. (October 2020) [87] |
Predict the severity of COVID-19 | 52 features from 362 patients with COVID-19 including 214 non-severe and 148 severe cases in China. |
RF | 95% accuracy when considering all features and 99% accuracy when only using top 10 important features selected by Gini impurity. |
| Xu et al. (October 2020) [73] |
Diagnose COVID-19 | 618 CT images in total. 219 samples from 110 patients with COVID-19; 224 samples from 224 patients with IAVP; 175 samples from 175 healthy cases. These samples are from China. |
CNN | Accuracy = 86.7% |
| Avila et al. (June 2020) [88] | Predict COVID-19 | 510 patients including 73 positives for COVID-19 and 437 negatives were from the emergency department of Hospital Israelita Albert Einstein (HIAE, São Paulo, Brazil). |
Gaussian Naïve Bayes (NB) | 100% sensitivity and 22.6% specificity, 76.7% for both sensitivity and specificity, and 0% sensitivity and 100% specificity when prior values were set to 0.9999, 0.2933, 0.001, respectively. |
| An et al. (October 2020) [89] | Predict mortality for patients with COVID-19 | Sociodemographic and medical information from 10,237 patients with COVID-19 in a nationwide Korean cohort. |
LASSO, SVM and RF | The LASSO model obtained best AUC (0.962 (0.945- 0.979)), and identified several significant predictors such as old age and preexisting DM or cancer. |
| Mei et al. (May 2020) [71] | Diagnose COVID-19 | CT scan images and non-image information such as demographic and laboratory tests from 905 patients between 17 January 2020 and 3 March 2020 from 18 medical centers in 13 provinces in China. | CNN+MLP | AUC=0.92 on a test set with 279 patients. |
| Ardakani et al. (June 2020) [74] | Diagnose COVID-19 | 1,020 CT images from 108 patients in Iran University of Medical Sciences (IUMS) hospital. |
CNN (ResNet-101) | AUC = 0.994, Sensitivity = 100%, Specificity = 99.02%, Accuracy = 99.51%. |
| Yang et al. (November 2020) [72] | Predict COVID-19 | Demographic information (i.e., age, sex, race) and 27 routine lab tests from 3,356 SARS-CoV-2 RT-PCR tested patients. These tests were from NYPH/WCM dataset. |
Gradient boosting decision tree (GBDT) | AUC = 0.854 (95% CI: 0.829–0.878). |
| Roy et al. (August 2020) [83] | Diagnose COVID-19 | Italian COVID-19 Lung Ultrasound DataBase: 277 lung ultrasound videos from 35 patients, corresponding to 58,924 images. | Spatial Transformer Networks and CNN | Accurate prediction and localization of COVID-19 imaging biomarkers in three tasks including frame-based classification, video-level grading and pathological artifact segmentation. |
| Narin et al. (May 2020) [76] | Diagnose COVID-19 | 341 images from COVID-19 patients, 2,800 normal chest images, 1,493 viral pneumonia and 2,772 bacterial chest X-ray images | CNN | 96.1%, 99.5%, and 99.7% accuracy on three datasets, respectively. |
| Jain et al. (September 2020) [79] | Diagnose COVID-19 | 1,832 X-ray images strengthened from original 1,215 X-ray images by using data augmentation techniques |
CNN (ResNet-50) | Training-validation-testing: accuracy, recall, and precision were 99.77%, 97.14%, and 97.14%, respectively. 5-fold cross validation: average accuracy, sensitivity, specificity, precision, and F1-score were 98.93%, 98.93%, 98.66%, 96.39%, and 98.15%, respectively. |
| Wang et al. (November 2020) [77] | Diagnose COVID-19 | Two datasets including 1,102 and 625 chest X-ray images, respectively. | CNN and SVM | 99.33%, and 95.02% accuracy on two datasets, respectively. |
| Loey et al. (April 2020) [84] | Detect COVID-19 | 8,100 chest X-ray images strengthened from original 306 chest X-ray images by using data augmentation techniques. | GAN with deep transfer learning | Testing sets: 100% accuracy; Validation set: 99.9% accuracy. |
| Li et al. (September 2020) [100] | Diagnose COVID-19; Identify subphenotypes |
Public dataset: 413 patients with COVID-19 and 1,071 patients with influenza | XGBoost model; a self-organizing map (SOM) |
Sensitivity = 92.5%; Specificity = 97.9%; Identified 4 subphenotypes which showed much difference in terms of gender distribution and levels of CRP and serum immune cells. |
| Zhou et al. (April 2020) [168] | Identify subphenotypes | Mexican Government COVID-19 open data including 778,692 COVID-19 patients. | meta-clustering technique | Identify 3 clusters which showed different recovery rates |
| Su et al. (July 2020) [102] | Identify subphenotypes | NYP-WCMC eligible 318 patients extracted from 1,661 patients with COVID-19 and NYP-LMH eligible 84 patients extracted from 458 patients with COVID-19. | Dynamic time warping and hierarchical agglomerative clustering method | Discovered distinct worsening and recovering subphenotypes within three strata including mild, intermediate, and severe strata. |
| V.Bhavani (December 2020) [103] | Identify subphenotypes | 696 hospitalized patients in University of Chicago Medicine | Group-based trajectory modeling (GBTM) | Discovered 4 subphenotypes which were different in experiencing cytokine storm, coagulopathy, and cardiac and renal injury. |
| Lascarrou et al. (March 2021) [97] | Identify subphenotypes | 416 COVID-19 patients with moderate to severe ARDS at 21 intensive care units in Belgium and France. | Hierarchical clustering method | Identified 3 subphenotypes which have different characteristics on comorbidities, mortality, sex, the duration of symptoms, plateau and driving pressure. |
| Legrand et al. (October 2020) [96] | Identify subphenotypes | 608 patients in at eight teaching hospitals of the Assistance Pub- lique-Hôpitaux de Paris | Consensus cluster analysis method | Identified 3 subphenotypes which are different in terms of a history of chronic hypertension, the presence of fever, respiratory and non-respiratory symptoms, and age. |
| Schinkel et al. (February 2021) [98] | Identify subphenotypes | 2,019 patients collected from COVID Predict project in the Netherlands. | Consensus cluster analysis method | Identified 3 subphenotypes which showed much difference in terms of demographics, comorbidities, and clinical outcomes. |
| Su et al. (July 2021) [99] | Identify subphenotypes | Development cohort with 8,199 patients and internal and external validation cohorts both with 3,519 patients. Those patients were from five major medical centers in New York City (NYC), between March 1 and June 12, 2020. | Data-driven (agglomerative hierarchical clustering model) | Identified 4 subphenotypes which showed much difference in terms of demographics, clinical variables, comorbidities, clinical outcomes, and medication treatments |