Table 5.

Main contributions of studies applied ml approaches for assessment severity of COVID‐19 and prediction of the disease progression and mortality risk using medical imaging

Reference	Modality	COVID‐19 patient cohort	Main contributions
113	Chest X‐ray	131 images of 84 patients	• Proposed deep‐learning CNN model with Dense regression layer to produce different severity scores from portable X‐ray for predicting COVID‐19 severity. • Compared traditional and transfer learning that showed better performance.
114	Chest CT scans	Severe group:97 scans of 32 patients Non‐severe group: 452 scans of 164 patients	• Developed a DL model based on U‐Net 29 equipped with the ResNet‐34 115 to segment lung lesions of thick‐section CT scans. • Computed biomarkers (POI and iHU) used as inputs for logistic regression model to classify severe and non‐severe COVID‐19, achieving AUC of 0.97. • Computed changes of lung lesion volume to assess COVID‐19 progressing.
116	Chest CT scans	23812 CT images from 408 patients	• Developed multiple instance learning model using ResNet‐34 115 as backbone to distinguishing severe from non‐severe COVID‐19, achieving AUC of 0.892. • The model was also applied for identifying patients with mild COVID‐19 at hospital admission, who progressed to severe disease. It achieved AUCs of 0.955 and 0.923 in two different subgroups.
117	Chest CT scans	72 serial CT of 24 patients	• Developed a DL model based on U‐Net 29 for automated lung segmentation.• Utilized quantification of infected regions to assess COVID‐19 progression.• Created heatmaps to visualize the progression.
118	Chest X‐ray	581 patients	• A convolutional Siamese neural network was built using DenseNet121 50 to provide the pulmonary X‐ray severity score, and assess the disease severity. • To build the model, a training set of X‐ray images was manually annotated using modified version of RALE scoring system, and the model was pretrained with chest X‐ray images from CheXpert 27 for better performance.
23	Chest CT, and clinical characteristics.	Stable group: 222 patients Progressive group: 25 patients	• Applied multivariate logistic regression to identify critical features to construct a nomogram. It was revealed that the features including age, CT severity score, and NLR were the significant, independent risk predictors. • Constructed the nomogram incorporating the predictors to predict the progression risk of patients at admission time.
119	Ultrasound	58 lung ultrasound videos from 20 patients	• An unsupervised and automatic model was proposed to detect and localize the pleural line in ultrasound data using HMM and Viterbi algorithm, achieving 94% and 84% in terms of accuracy for linear and convex probes, respectively. • Depending on pleural line, SVM classifier evaluated the severity of COVID‐19 with accuracy rates of 94% and 88%, respectively, for linear and convex probes.
25	Ultrasound	277 lung ultrasound videos from 35 patients	• Introduce a fully annotated version of the ICLUS‐DB database. • Propose a novel DL network to predict the severity score at frame level, and present uninorms‐based method 120 to estimate the severity score at video level.
121	Chest CT scan.	30 patients	• Utilized a conditional logistic regression model to identify critical predictors of CT scan features to predict the mortality in nonelderly COVID‐19 patients without underlying comorbidities. • It was revealed that the CT severity score was the significant mortality predictor, with the highest specificity and sensitivity of 0.87 and 0.83, respectively.

Abbreviations: AUC, area under the receiver‐operating characteristics curve; CNN, convolutional neural network; DL, deep learning; HMM, hidden Markov model; ICLUS‐DB, Italian COVID‐19 lung ultrasound database; iHU, the average infection HU; NLR, neutrophil‐to‐lymphocyte ratio; POI, the portion of infection; RALE, radiographic assessment of lung edema; SVM, support vector machine.