Predicting the Severity of COVID-19 from Lung CT Images Using Novel Deep Learning

Deep learning models for CT image classification: a comprehensive literature review

Background and Objective

Computed tomography (CT) imaging plays a crucial role in the early detection and diagnosis of life-threatening diseases, particularly in respiratory illnesses and oncology. The rapid advancement of deep learning (DL) has revolutionized CT image analysis, enhancing diagnostic accuracy and efficiency. This review explores the impact of advanced DL methodologies in CT imaging, with a particular focus on their applications in coronavirus disease 2019 (COVID-19) detection and lung nodule classification.

Methods

A comprehensive literature search was conducted, examining the evolution of DL architectures in medical imaging from conventional convolutional neural networks (CNNs) to sophisticated foundational models (FMs). We reviewed publications from major databases, focusing on developments in CT image analysis using DL from 2013 to 2023. Our search criteria included all types of articles, with a focus on peer-reviewed research papers and review articles in English.

Key Content and Findings

The review reveals that DL, particularly advanced architectures like FMs, has transformed CT image analysis by streamlining interpretation processes and enhancing diagnostic capabilities. We found significant advancements in addressing global health challenges, especially during the COVID-19 pandemic, and in ongoing efforts for lung cancer screening. The review also addresses technical challenges in CT image analysis, including data variability, the need for large high-quality datasets, and computational demands. Innovative strategies such as transfer learning, data augmentation, and distributed computing are explored as solutions to these challenges.

Conclusions

This review underscores the pivotal role of DL in advancing CT image analysis, particularly for COVID-19 and lung nodule detection. The integration of DL models into clinical workflows shows promising potential to enhance diagnostic accuracy and efficiency. However, challenges remain in areas of interpretability, validation, and regulatory compliance. The review advocates for continued research, interdisciplinary collaboration, and ethical considerations as DL technologies become integral to clinical practice. While traditional imaging techniques remain vital, the integration of DL represents a significant advancement in medical diagnostics, with far-reaching implications for future research, clinical practice, and healthcare policy.

Clinical Features Predicting COVID-19 Severity Risk at the Time of Hospitalization

The global spread of COVID-19 has led to significant mortality and morbidity worldwide. Early identification of COVID-19 patients who are at high risk of developing severe disease can help in improved patient management, care, and treatment, as well as in the effective allocation of hospital resources. The severity prediction at the time of hospitalization can be extremely helpful in deciding the treatment of COVID-19 patients. To this end, this study presents an interpretable artificial intelligence (AI) model, named COVID-19 severity predictor (CoSP) that predicts COVID-19 severity using the clinical features at the time of hospital admission. We utilized a dataset comprising 64 demographic and laboratory features of 7,416 confirmed COVID-19 patients that were collected at the time of hospital admission. The proposed hierarchical CoSP model performs four-class COVID severity risk prediction into asymptomatic, mild, moderate, and severe categories. CoSP yielded better performance with good interpretability, as observed via Shapley analysis on COVID severity prediction compared to the other popular ML methods, with an area under the received operating characteristic curve (AUC-ROC) of 0.95, an area under the precision-recall curve (AUPRC) of 0.91, and a weighted F1-score of 0.83. Out of 64 initial features, 19 features were inferred as predictive of the severity of COVID-19 disease by the CoSP model. Therefore, an AI model predicting COVID-19 severity may be helpful for early intervention, optimizing resource allocation, and guiding personalized treatments, potentially enabling healthcare professionals to save lives and allocate resources effectively in the fight against the pandemic.

Impact of the COVID-19 pandemic and COVID vaccination campaign on imaging case volumes and medicolegal aspects

Purpose

The coronavirus pandemic (COVID-19) significantly impacted the global economy and health. Italy was one of the first and most affected countries. The objective of our study was to assess the impact of the pandemic and the vaccination campaign on the radiological examinations performed in a radiology department of a tertiary center in Southern Italy.

Materials and methods

We analyzed weekly and retrospectively electronic medical records of case volumes performed at the Radiology Department of "Mater Domini" University Hospital of Catanzaro from March 2020 to March 2022, comparing them with the volumes in the same period of the year 2019. We considered the origin of patients (outpatient, inpatient) and the type of examinations carried out (x-ray, mammography, CT, MRI, and ultrasound). A non-parametric test (Wilcoxon Signed Rank test) was applied to evaluate the average volumes.

Results

Total flows in the pandemic period from COVID-19 were lower than in the same pre-pandemic period with values of 552 (120) vs. 427 (149) median (IQR) (p < 0.001). The vaccination campaign allowed the resumption of the pre-vaccination pandemic with total flows 563 (113) vs. 427 (149) median (IQR) p < 0.001. In the post-vaccination period, the number of examinations was found to overlap with the pre-COVID period.

Conclusion

The pandemic impacted the volume of radiological examinations performed, particularly with the reduction of tests in outpatients. The vaccination allowed the return to the pre-COVID period imaging case volumes.

Development and Validation of a Robust and Interpretable Early Triaging Support System for Patients Hospitalized With COVID-19: Predictive Algorithm Modeling and Interpretation Study

BACKGROUND

Robust and accurate prediction of severity for patients with COVID-19 is crucial for patient triaging decisions. Many proposed models were prone to either high bias risk or low-to-moderate discrimination. Some also suffered from a lack of clinical interpretability and were developed based on early pandemic period data. Hence, there has been a compelling need for advancements in prediction models for better clinical applicability.

OBJECTIVE

The primary objective of this study was to develop and validate a machine learning-based Robust and Interpretable Early Triaging Support (RIETS) system that predicts severity progression (involving any of the following events: intensive care unit admission, in-hospital death, mechanical ventilation required, or extracorporeal membrane oxygenation required) within 15 days upon hospitalization based on routinely available clinical and laboratory biomarkers.

METHODS

We included data from 5945 hospitalized patients with COVID-19 from 19 hospitals in South Korea collected between January 2020 and August 2022. For model development and external validation, the whole data set was partitioned into 2 independent cohorts by stratified random cluster sampling according to hospital type (general and tertiary care) and geographical location (metropolitan and nonmetropolitan). Machine learning models were trained and internally validated through a cross-validation technique on the development cohort. They were externally validated using a bootstrapped sampling technique on the external validation cohort. The best-performing model was selected primarily based on the area under the receiver operating characteristic curve (AUROC), and its robustness was evaluated using bias risk assessment. For model interpretability, we used Shapley and patient clustering methods.

RESULTS

Our final model, RIETS, was developed based on a deep neural network of 11 clinical and laboratory biomarkers that are readily available within the first day of hospitalization. The features predictive of severity included lactate dehydrogenase, age, absolute lymphocyte count, dyspnea, respiratory rate, diabetes mellitus, c-reactive protein, absolute neutrophil count, platelet count, white blood cell count, and saturation of peripheral oxygen. RIETS demonstrated excellent discrimination (AUROC=0.937; 95% CI 0.935-0.938) with high calibration (integrated calibration index=0.041), satisfied all the criteria of low bias risk in a risk assessment tool, and provided detailed interpretations of model parameters and patient clusters. In addition, RIETS showed potential for transportability across variant periods with its sustainable prediction on Omicron cases (AUROC=0.903, 95% CI 0.897-0.910).

CONCLUSIONS

RIETS was developed and validated to assist early triaging by promptly predicting the severity of hospitalized patients with COVID-19. Its high performance with low bias risk ensures considerably reliable prediction. The use of a nationwide multicenter cohort in the model development and validation implicates generalizability. The use of routinely collected features may enable wide adaptability. Interpretations of model parameters and patients can promote clinical applicability. Together, we anticipate that RIETS will facilitate the patient triaging workflow and efficient resource allocation when incorporated into a routine clinical practice.