Predicting Mechanical Ventilation and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study

Bae, Joseph; Kapse, Saarthak; Singh, Gaurav; Gattu, Rishabh; Ali, Syed Asif; Shah, Neal; Marshall, Colin; Pierce, Jonathan D.; Phatak, Tej; Gupta, Amit; Green, Jeremy; Madan, Nikhil; Prasanna, Prateek

doi:10.3390/diagnostics11101812

Cited by 33 publications

(25 citation statements)

References 44 publications

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“…Similar to the proposed study, Aljouie et al [17] and Bae et al [18], proposed a model for two tasks, i.e., mortality and ventilator support prediction for COVID-19-hospitalized patients. In the first study, the author utilized different categories of data and found that comorbidity alone can help in predicting the mortality of COVID-19 patients.…”

Section: Ai-based Studies To Predict Early Mortality In Covid-19 Pati...mentioning

confidence: 81%

“…Similarly, ref. [15][16][17][18]23] found age as one of the key features for predicting intubation in COVID-19 patients. However, Bae et al [18] used radiomics features and two demographic features (age and gender) to predict mortality and ventilator support.…”

Section: Discussionmentioning

confidence: 99%

“…[15][16][17][18]23] found age as one of the key features for predicting intubation in COVID-19 patients. However, Bae et al [18] used radiomics features and two demographic features (age and gender) to predict mortality and ventilator support. The radiomic scores were assigned by experienced radiologists, who found that radiomic features greatly enhanced the performance of the algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…Aljouie et al [15] achieved an AUC of 0.83 and a balanced accuracy of 0.80 using RF. Meanwhile, in Bae et al's study [18], the DL model achieved an AUC of 0.83, sensitivity of 0.79, and specificity of 0.74. As previously discussed, the number of patient samples in Aljouie et al was larger than that of the Bae et al study; furthermore, the study also utilized different types of patient data such as clinical, comorbidity, demographic, and CXR.…”

Section: Ai-based Studies To Predict Early Mortality In Covid-19 Pati...mentioning

confidence: 93%

“…A highest AUC of 0.87 and a balanced accuracy of 0.81 were achieved using the features selected from ReliefF, classification with SVM, and data augmentation with random undersampling. Similar to Aljouie, Bae et al [18] also performed a study that predicted ventilator support and mortality of COVID-19-hospitalized patients using CXR and radiomic features. However, the study only utilized radiomic data.…”

Section: Ai-based Studies For Early Identification Of Covid-19 Patien...mentioning

confidence: 99%

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Explainable Artificial Intelligence Approach for the Early Prediction of Ventilator Support and Mortality in COVID-19 Patients

Aslam

2022

Computation

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Early prediction of mortality and risk of deterioration in COVID-19 patients can reduce mortality and increase the opportunity for better and more timely treatment. In the current study, the DL model and explainable artificial intelligence (EAI) were combined to identify the impact of certain attributes on the prediction of mortality and ventilatory support in COVID-19 patients. Nevertheless, the DL model does not suffer from the curse of dimensionality, but in order to identify significant attributes, the EAI feature importance method was used. The DL model produced significant results; however, it lacks interpretability. The study was performed using COVID-19-hospitalized patients in King Abdulaziz Medical City, Riyadh. The dataset contains the patients’ demographic information, laboratory investigations, and chest X-ray (CXR) findings. The dataset used suffers from an imbalance; therefore, balanced accuracy, sensitivity, specificity, Youden index, and AUC measures were used to investigate the effectiveness of the proposed model. Furthermore, the experiments were conducted using original and SMOTE (over and under sampled) datasets. The proposed model outperforms the baseline study, with a balanced accuracy of 0.98 and an AUC of 0.998 for predicting mortality using the full-feature set. Meanwhile, for predicting ventilator support a highest balanced accuracy of 0.979 and an AUC of 0.981 was achieved. The proposed explainable prediction model will assist doctors in the early prediction of COVID-19 patients that are at risk of mortality or ventilatory support and improve the management of hospital resources.

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Section: Ai-based Studies To Predict Early Mortality In Covid-19 Pati...mentioning

confidence: 81%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Ai-based Studies To Predict Early Mortality In Covid-19 Pati...mentioning

confidence: 93%

Section: Ai-based Studies For Early Identification Of Covid-19 Patien...mentioning

confidence: 99%

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Explainable Artificial Intelligence Approach for the Early Prediction of Ventilator Support and Mortality in COVID-19 Patients

Aslam

2022

Computation

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Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study

Shiri,

Salimi,

Saberi

et al. 2024

Int J Imaging Syst Tech

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To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests.

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Chest X‐ray lung imaging features in pediatric COVID‐19 and comparison with viral lower respiratory infections in young children

Niño

Moltó

Aguilar

et al. 2021

Pediatric Pulmonology

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Rationale Chest radiography (CXR) is a noninvasive imaging approach commonly used to evaluate lower respiratory tract infections (LRTIs) in children. However, the specific imaging patterns of pediatric coronavirus disease 2019 (COVID‐19) on CXR, their relationship to clinical outcomes, and the possible differences from LRTIs caused by other viruses in children remain to be defined. Methods This is a cross‐sectional study of patients seen at a pediatric hospital with polymerase chain reaction (PCR)‐confirmed severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) (n = 95). Patients were subdivided in infants (0–2 years, n = 27), children (3–10 years, n = 27), and adolescents (11–19 years, n = 41). A sample of young children (0–2 years, n = 68) with other viral lower respiratory infections (LRTI) was included to compare their CXR features with the subset of infants (0–2 years) with COVID‐19. Results Forty‐five percent of pediatric patients with COVID‐19 were hospitalized and 20% required admission to intensive care unit (ICU). The most common abnormalities identified were ground‐glass opacifications (GGO)/consolidations (35%) and increased peribronchial markings/cuffing (33%). GGO/consolidations were more common in older individuals and perihilar markings were more common in younger subjects. Subjects requiring hospitalization or ICU admission had significantly more GGO/consolidations in CXR (p < .05). Typical CXR features of pediatric viral LRTI (e.g., hyperinflation) were more common in non‐COVID‐19 viral LRTI cases than in COVID‐19 cases (p < .05). Conclusions CXR may be a complemental exam in the evaluation of moderate or severe pediatric COVID‐19 cases. The severity of GGO/consolidations seen in CXR is predictive of clinically relevant outcomes. Hyperinflation could potentially aid clinical assessment in distinguishing COVID‐19 from other types of viral LRTI in young children.

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Predicting Mechanical Ventilation and Mortality in COVID-19 Using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study

Cited by 33 publications

References 44 publications

Explainable Artificial Intelligence Approach for the Early Prediction of Ventilator Support and Mortality in COVID-19 Patients

Explainable Artificial Intelligence Approach for the Early Prediction of Ventilator Support and Mortality in COVID-19 Patients

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning: A large multicentric cohort study

Chest X‐ray lung imaging features in pediatric COVID‐19 and comparison with viral lower respiratory infections in young children

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