Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

Abstract

Background The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. Methods Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann–Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. Results No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. Conclusion By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted.

Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era

“…To our knowledge, this is the first study that demonstrates the previously developed and validated DL algorithm utilizing age and nine laboratory indicators accurately predict severity in patients with COVID-19 both in the pre-Omicron phase and Omicron periods [ 16 ]. Although there are studies on the AI topic of COVID-19 patients performed in Turkey, none of them has compared different variant periods [ 23 , 24 ].…”

“…However, due to the increased risk of infection spread from additional visits to radiology suites, national organizations advise against using radiological imaging for the diagnosis of COVID-19. Gülbay et al from Turkey have demonstrated that a machine learning algorithm made of clinical and DL-segmentation-based radiological criteria, trained with a balanced data set, can successfully predict COVID-19 patients who may need intensive care [ 24 ]. Radiological image data might be very useful in models.…”

Radiomics analysis in medical imaging research

“…Recent studies have also shown that radiomic analysis of computed tomography (CT) scans of the chest can differentiate patients with coronavirus disease 2019 (COVID‐19) from patients with other pneumonia of different aetiologies. 26 , 27 , 28 Radiomic analysis has been shown to predict (a) whether a patient who is positive for COVID‐19 needs to be hospitalised (b) to predict whether a patient who is positive COVID‐19 would need the intensive care unit (ICU) and/or ventilators 29 , 30 , 31 (c) how long a patient with COVID‐19 is expected to be hospitalised (d) future mortality risk from COVID‐19 and (e) predicting whether patients with COVID‐19 go on to develop long COVID‐19. 30 , 31 , 32 , 33 , 34 These data can be used to identify patients at increased risk for clinical deterioration from COVID‐19 and could help appropriately allocate COVID‐19 resources ahead of time including determining ICU beds/ventilators, CT scan intervals and clinicians/healthcare providers needed for patients.…”