Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia

Association of excess visceral fat and severe illness in hospitalized COVID-19 patients in Japan: a retrospective cohort study

BACKGROUND/OBJECTIVES

Obesity, defined by body mass index (BMI), is a well-known risk factor for the severity of coronavirus disease 2019 (COVID-19). Adipose tissue distribution has also been implicated as an important factor in the body's response to infection, and excess visceral fat (VF), which is prevalent in Japanese, may contribute significantly to the severity. Therefore, this study aimed to evaluate the association of obesity and VF with COVID-19 severe illness in Japan.

SUBJECTS/METHODS

This retrospective cohort study involved 550 COVID-19 patients admitted to a tertiary care hospital with BMI and body composition data, including VF. The primary endpoint was severe illness, including death, due to COVID-19 during hospitalization. Logistic regression analysis was applied to examine the quartiles of BMI and VF on severe illness after adjusting for covariates such as age, sex, subcutaneous fat, paraspinal muscle radiodensity, and comorbidities affecting VF (COPD, cancer within 5 years, immunosuppressive agent use).

RESULTS

The median age was 56.0 years; 71.8% were males. During hospitalization, 82 (14.9%) experienced COVID-19 severe illness. In the multivariate logistic regression analysis, Q4 of BMI was not significantly associated with severe illness compared to Q1 of BMI (OR 1.03; 95% CI 0.37-2.86; p = 0.95). Conversely, Q3 and Q4 of VF showed a higher risk for severe illness compared to Q1 of VF (OR 2.68; 95% CI 1.01-7.11; p = 0.04, OR 3.66; 95% CI 1.30-10.26; p = 0.01, respectively). Stratified analysis by BMI and adjusted for covariates showed the positive association of VF with severe illness only in the BMI < 25 kg/m2 group.

CONCLUSIONS

High BMI was not an independent risk factor for COVID-19 severe illness in hospitalized patients in Japan, whereas excess VF significantly influenced severe illness, especially in patients with a BMI < 25 kg/m2.

Accuracy of artificial intelligence CT quantification in predicting COVID-19 subjects' prognosis

BACKGROUND

Artificial intelligence (AI)-aided analysis of chest CT expedites the quantification of abnormalities and may facilitate the diagnosis and assessment of the prognosis of subjects with COVID-19.

OBJECTIVES

This study investigates the performance of an AI-aided quantification model in predicting the clinical outcomes of hospitalized subjects with COVID-19 and compares it with radiologists' performance.

SUBJECTS AND METHODS

A total of 90 subjects with COVID-19 (men, n = 59 [65.6%]; age, 52.9±16.7 years) were recruited in this cross-sectional study. Quantification of the total and compromised lung parenchyma was performed by two expert radiologists using a volumetric image analysis software and compared against an AI-assisted package consisting of a modified U-Net model for segmenting COVID-19 lesions and an off-the-shelf U-Net model augmented with COVID-19 data for segmenting lung volume. The fraction of compromised lung parenchyma (%CL) was calculated. Based on clinical results, the subjects were divided into two categories: critical (n = 45) and noncritical (n = 45). All admission data were compared between the two groups.

RESULTS

There was an excellent agreement between the radiologist-obtained and AI-assisted measurements (intraclass correlation coefficient = 0.88, P < 0.001). Both the AI-assisted and radiologist-obtained %CLs were significantly higher in the critical subjects (P = 0.009 and 0.02, respectively) than in the noncritical subjects. In the multivariate logistic regression analysis to distinguish the critical subjects, an AI-assisted %CL ≥35% (odds ratio [OR] = 17.0), oxygen saturation level of <88% (OR = 33.6), immunocompromised condition (OR = 8.1), and other comorbidities (OR = 15.2) independently remained as significant variables in the models. Our proposed model obtained an accuracy of 83.9%, a sensitivity of 79.1%, and a specificity of 88.6% in predicting critical outcomes.

CONCLUSIONS

AI-assisted measurements are similar to quantitative radiologist-obtained measurements in determining lung involvement in COVID-19 subjects.

Clinical utilization of artificial intelligence-based COVID-19 pneumonia quantification using chest computed tomography - a multicenter retrospective cohort study in Japan

BACKGROUND

Computed tomography (CT) imaging and artificial intelligence (AI)-based analyses have aided in the diagnosis and prediction of the severity of COVID-19. However, the potential of AI-based CT quantification of pneumonia in assessing patients with COVID-19 has not yet been fully explored. This study aimed to investigate the potential of AI-based CT quantification of COVID-19 pneumonia to predict the critical outcomes and clinical characteristics of patients with residual lung lesions.

METHODS

This retrospective cohort study included 1,200 hospitalized patients with COVID-19 from four hospitals. The incidence of critical outcomes (requiring the support of high-flow oxygen or invasive mechanical ventilation or death) and complications during hospitalization (bacterial infection, renal failure, heart failure, thromboembolism, and liver dysfunction) was compared between the groups of pneumonia with high/low-percentage lung lesions, based on AI-based CT quantification. Additionally, 198 patients underwent CT scans 3 months after admission to analyze prognostic factors for residual lung lesions.

RESULTS

The pneumonia group with a high percentage of lung lesions (N = 400) had a higher incidence of critical outcomes and complications during hospitalization than the low percentage group (N = 800). Multivariable analysis demonstrated that AI-based CT quantification of pneumonia was independently associated with critical outcomes (adjusted odds ratio [aOR] 10.5, 95% confidence interval [CI] 5.59-19.7), as well as with oxygen requirement (aOR 6.35, 95% CI 4.60-8.76), IMV requirement (aOR 7.73, 95% CI 2.52-23.7), and mortality rate (aOR 6.46, 95% CI 1.87-22.3). Among patients with follow-up CT scans (N = 198), the multivariable analysis revealed that the pneumonia group with a high percentage of lung lesions on admission (aOR 4.74, 95% CI 2.36-9.52), older age (aOR 2.53, 95% CI 1.16-5.51), female sex (aOR 2.41, 95% CI 1.13-5.11), and medical history of hypertension (aOR 2.22, 95% CI 1.09-4.50) independently predicted persistent residual lung lesions.

CONCLUSIONS

AI-based CT quantification of pneumonia provides valuable information beyond qualitative evaluation by physicians, enabling the prediction of critical outcomes and residual lung lesions in patients with COVID-19.

Predicting the COVID-19 Patients Status Using Chest CT Scan Findings: A Risk Assessment Model Based on Decision Tree Analysis

BACKGROUND

The role of chest computed tomography (CT) to diagnose coronavirus disease 2019 (COVID-19) is still an open field to be explored. The aim of this study was to apply the decision tree (DT) model to predict critical or non-critical status of patients infected with COVID-19 based on available information on non-contrast CT scans.

METHODS

This retrospective study was performed on patients with COVID-19 who underwent chest CT scans. Medical records of 1078 patients with COVID-19 were evaluated. The classification and regression tree (CART) of decision tree model and k-fold cross-validation were used to predict the status of patients using sensitivity, specificity, and area under the curve (AUC) assessments.

RESULTS

The subjects comprised of 169 critical cases and 909 non-critical cases. The bilateral distribution and multifocal lung involvement were 165 (97.6%) and 766 (84.3%) in critical patients, respectively. According to the DT model, total opacity score, age, lesion types, and gender were statistically significant predictors for critical outcomes. Moreover, the results showed that the accuracy, sensitivity and specificity of the DT model were 93.3%, 72.8%, and 97.1%, respectively.

CONCLUSIONS

The presented algorithm demonstrates the factors affecting health conditions in COVID-19 disease patients. This model has the potential characteristics for clinical applications and can identify high-risk subpopulations that need specific prevention. Further developments including integration of blood biomarkers are underway to increase the performance of the model.

Effectiveness of remdesivir with corticosteroids for COVID-19 patients in intensive care unit: A hospital-based observational study

The effectiveness of remdesivir on survival in coronavirus disease 2019 (COVID-19), especially in cases treated in the intensive care unit (ICU), is controversial. We investigated the effectiveness of remdesivir with corticosteroids on the survival of COVID-19 patients in a real ICU clinical practice. For laboratory-confirmed COVID-19 patients admitted to the ICU of a tertiary hospital in Tokyo (April 2020-November 2021) and who received corticosteroids, the effectiveness of remdesivir for survival, stratified by interval length (within 9 or 10+ days), was retrospectively analyzed using Cox regression model. A total of 168 patients were included: 35 with no remdesivir use (control), 96 with remdesivir use within 9 days, and 37 with remdesivir use with an interval of 10+ days. In-hospital mortality was 45.7%, 10.4%, and 16.2%, respectively. After adjusting for possible covariates including comorbidities, laboratory data, oxygen demand, or level of pneumonia, remdesivir use within 9 days from symptom onset reduced mortality risk (hazard ratio [HR]: 0.10; 95% confidence interval (CI): 0.025-0.428) compared to the control group. However, remdesivir use with an interval of 10+ days showed no significant association with mortality (HR: 0.42; 95% CI: 0.117-1.524). Among COVID-19 patients who received corticosteroids in ICU, remdesivir use within 9 days from symptom onset was associated with reduced in-hospital mortality risk.

CHEST CT USAGE IN COVID-19 PNEUMONIA: MULTICENTER STUDY ON RADIATION DOSES AND DIAGNOSTIC QUALITY IN BRAZIL

We assessed variations in chest CT usage, radiation dose and image quality in COVID-19 pneumonia. Our study included all chest CT exams performed in 533 patients from 6 healthcare sites from Brazil. We recorded patients' age, gender and body weight and the information number of CT exams per patient, scan parameters and radiation doses (volume CT dose index-CTDIvol and dose length product-DLP). Six radiologists assessed all chest CT exams for the type of pulmonary findings and classified CT appearance of COVID-19 pneumonia as typical, indeterminate, atypical or negative. In addition, each CT was assessed for diagnostic quality (optimal or suboptimal) and presence of artefacts. Artefacts were frequent (367/841), often related to respiratory motion (344/367 chest CT exams with artefacts) and resulted in suboptimal evaluation in mid-to-lower lungs (176/344) or the entire lung (31/344). There were substantial differences in CT usage, patient weight, CTDIvol and DLP across the participating sites.

Application of Artificial Intelligence in COVID-19 Diagnosis and Therapeutics

The coronavirus disease 2019 (COVID-19) pandemic began at the end of December 2019, giving rise to a high rate of infections and causing COVID-19-associated deaths worldwide. It was first reported in Wuhan, China, and since then, not only global leaders, organizations, and pharmaceutical/biotech companies, but also researchers, have directed their efforts toward overcoming this threat. The use of artificial intelligence (AI) has recently surged internationally and has been applied to diverse aspects of many problems. The benefits of using AI are now widely accepted, and many studies have shown great success in medical research on tasks, such as the classification, detection, and prediction of disease, or even patient outcome. In fact, AI technology has been actively employed in various ways in COVID-19 research, and several clinical applications of AI-equipped medical devices for the diagnosis of COVID-19 have already been reported. Hence, in this review, we summarize the latest studies that focus on medical imaging analysis, drug discovery, and therapeutics such as vaccine development and public health decision-making using AI. This survey clarifies the advantages of using AI in the fight against COVID-19 and provides future directions for tackling the COVID-19 pandemic using AI techniques.