COVID-19 diagnosis and disease severity prediction assessment through an innovative AI-based model

Introduction: CT imaging has been widely used during the COVID-19 pandemic to diagnose and assess disease severity. Its use for diagnosis is not indicated apart from specific settings such as triage of patients for referral to RTPCR testing or severity assessment. Nowadays, the added value of AI-based models is still unknown and has to be addressed. Method(s): We evaluated the added value of an automated lung involvement assessment tool, named icolung. Since software version 7.0, icolung automatically extracts the Severity Score proposed by Pan F. et al., (2020, Radiology), to help radiologists assess the severity of lung involvement in COVID-19 infected patients. We evaluate retrospectively a group of 785 COVID-19 positive patients compared to a group of 1049 COVID-19 negative patients. We used the severity score (SS) in order to predict the positivity of COVID-19 PCR testing and evaluated the potential impact in the prediction of patients' outcome. Result(s): The icolung SS allows to identify infected (PCR-proven) COVID-19 patients with a sensitivity of 83% and a specificity of 77% (AUC of 0.86, 95% CI 0.85-0.88) for patients with a SS of more than 1.5 on a scale of 0 to 25. An SS of > 7.5 identifies patients at risk of ICU admission with a sensitivity of 70% and specificity of 65% (AUC of 0.74, p<0.0001). Conclusion(s): The severity score as estimated via icolung allows to identify positive PCR-tested COVID-19 patients and helps to predict ICU admission. This automated evaluation tool can support clinicians with the in-hospital management of patients (suspected to be) infected with COVID-19.

(Early) Economic Evaluation of the AI-based software 'icolung' for the detection and prognosis of COVID cases from CT scans

Early detection of COVID-19 infection, followed by appropriate patient management, has the potential to reduce costs related to developing severe forms of the disease, as well as spreading of the disease, if left undetected. Our objective was to evaluate the impact of an AI-based chest CT analysis software (icolung, icometrix) for the detection and prognosis of COVID-19 cases in patients receiving a CT scan in a hospital setting in Belgium. We developed a decision analytic model comparing routine practice scenario where patients receiving a CT scan in the hospital are not screened for COVID-19 with a scenario where icolung is used to analyze CT scans for the detection and prognosis of COVID-19 cases. We evaluated the impact of the technology in preventing the further spreading of the infection in the community and in reducing the length of hospitalization of COVID-19 patients. In the base case using a relatively low COVID-19 prevalence of 0.36%, icolung is cost-effective in preventing COVID19 transmission in the community, costing 8.221 to prevent one infection. At low prevalence of the disease and low risk of hospitalization, the technology is not cost-effective in reducing the length of hospitalization. However, icolung may be cost-effective in situations with high disease prevalence (>30%) or high risk of hospitalization (>6%) such as patients suffering from chronic oncological diseases and benefiting from recurring thoracic imaging. This model provides initial evidence of cost-effectiveness of AI-based chest CT analysis software and may help to provide guidance regarding further health care research and policy.

Role of BAL in COVID-19 patients: a prospective multicentre study

The present study is part of DRAGON, a prospective multicentre European project aimed at improving the diagnosis of COVID-19. The primary aim of this study is to evaluate BAL role in detecting coexisting infections. Secondary aims are BAL impact on the management of COVID patients, characteristics of BAL cellularity in COVID patients, and safety of BAL in COVID patients and for healthcare providers. The study was carried out in 2021. It involved hospitalized patients in non-ICU wards at Careggi University Hospital in Florence, at CHU of Liege and at Morgagni Hospital Bologna University/Forli. All patients underwent BAL for microbiological and cytological analysis. Coinfections were detected in 35 out of 115 patients. In 34% of cases we demonstrated the presence of lymphocytic alveolitis;in 49% of cases a neutrophilic alveolitis and in 7% of cases we observed the presence of a mixed lymphocytic/neutrophilic alveolitis. All patients tested positive for Sars-Cov-2 PCR nasal swabs on admission. BAL was positive for Sars-Cov-2 in all cases, 7 PCR nasal swab performed at the time of the BAL were negative. No major adverse events were demonstrated in the 24 hours after BAL in enrolled patients. There were no cases of infection among health care workers involved in bronchoscopic procedures. Coinfections in COVID-19 patients are common. BAL is a safe tool to identify the presence of coinfections and help clinicians manage these patients correctly. BAL cellularity in covid patients shows a predominance of neutrophils, particularly in cases of co-infection. Our data suggests an earlier negativisation of nasopharyngeal swab compared to BAL.

Deep learning model for the quantification of lung abnormalities in ILD patients

Lung fibrosis quantification from CT scans is prone to large inter and intra observer variability and its correlation with PFT is essential in the definition of disease progression. There is the need for a reliable and reproducible tool for abnormalities quantification. For this reason, a deep learning abnormalities quantification model was used to explore the correlation with PFT in ILD patients. The abnormalities segmentation model is based on 2D U-Net combined with Res Next as encoder and deep supervision and was trained on axial unenhanced chest CT scans of 199 COVID-19 patients and externally validated on 50 COVID-19 patients. Whole lungs were segmented using RadiomiX toolbox. Validation of the quantification performance was explored in a cohort of 20 ILD patients. The model performed the automatic segmentation of all abnormalities and calculate the ratio on the total lung volume ((abnormalities volume/whole lungs volume) * 100). This value is then correlated with the Forced Vital Capacity (FVC) and Diffusion Lung Capacity for carbon monoxide (DLCO) for each patient with Pearson correlation coefficient (rho). The deep learning segmentation algorithm achieved good performances (mean DSC 0.6 +/- 0.1) on the external test set. The percentage volume of disease region correlated with FVC and DLCO were the rho = -0.70402, -0.58133, respectively (P <. 001 for all). The developed algorithm performed similarly to radiologists for disease-extent contouring, which correlated with pulmonary function to assess CT images from patients with ILD. This automatic quantification tool could help in the prognosis and diagnosis of ILDs, based on the lung abnormalities extent.

Does Facemask Impact Diagnostic During Pulmonary Auscultation ?

Facemasks have been widely used in hospitals, especially since the emergence of the coronavirus 2019 (COVID-19) pandemic, often severely affecting respiratory functions. Masks protect patients from contagious airborne transmission, and are thus more specifically important for chronic respiratory disease (CRD) patients. However, masks also increase air resistance and thus work of breathing, which may impact pulmonary auscultation and diagnostic acuity, the primary respiratory examination This study is the first to assess the impact of facemasks on clinical auscultation diagnostic. Lung sounds from 29 patients were digitally recorded using an electronic stethoscope. For each patient, one recording was taken wearing a surgical mask and one without. Recorded signals were segmented in breath cycles using an autocorrelation algorithm. In total, 87 breath cycles were identified from sounds with mask, and 82 without mask. Time-frequency analysis of the signals was used to extract comparison features such as peak frequency, median frequency, band power, or spectral integration. All the features extracted in frequency content, its evolution, or power did not significantly differ between respiratory cycles with or without mask. This early stage study thus suggests minor impact on clinical diagnostic outcomes in pulmonary auscultation. However, further analysis is necessary such as on adventitious sounds characteristics differences with or without mask, to determine if facemask could lead to no discernible diagnostic outcome in clinical practice. Copyright (C) 2021 The Authors.

Late Breaking Abstract - CNN-RNN network to classify COVID, Influenza and non-infectious cases on CT imaging

COVID-19 associated lung diseases can mimic radiological characteristics of other viral lung diseases such as influenza which may lead to misdiagnosis. In this study, we proposed an Artificial Intelligence framework based on a combination of a Convolutional Neural network architecture and a Recurrent Neural Network architecture to classify CT volumes with COVID-19, Influenza, and no-infection. The model was trained on a dataset of 300 patients (100 patients in each class). Each set of 15 consecutive axial slices with the associated label of the corresponding CT volume was input as a 3 channel input at 5 time points to the CNN-RNN network. Benchmarked against RT-PCR confirmed cases of COVID-19 and Influenza, our model, when evaluated on an independent validation set of 400 CT patients, can accurately classify CT volumes of patients with COVID-19, Influenza, or no-infection with a sensitivity of 96% (COVID-19) and 95% (Influenza) (Tablel). Figurel shows the percentage of correctly classified and misclassified cases in each class. Our model provides rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.

Late Breaking Abstract - Development and validation of an automated radiomic CT signature for detecting? COVID-i9

The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status and pushed healthcare systems beyond the limits. We aim to develop a fully automatic framework to detect COVID-19 by applying artificial intelligence (Al). A fully automated Al framework was developed to extract radiomics features from chest CT scans to detect COVID-19 patients. We curated and analysed the data from a total of 1381 patients. A cohort of 181 RT-PCR confirmed COVID-19 patients and 1200 control patients was included for model development. An independent dataset of 697 patients was used to validate the model. The datasets were collected from CHU Liège, Belgium. Model performance was assessed by the area under the receiver operating characteristic curve (AUC). Assuming 15% disease prevalence, a comprehensive analysis of classification performance in terms of accuracy, sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) was performed for all possible decision thresholds. The final curated dataset used for model development and testing consisted of chest CT scans of 1224 patients and 641 patients, respectively. The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset. Assuming the cost of false negatives is twice as high as the cost of false positives, the optimal decision threshold resulted in an accuracy of 85.18%, a sensitivity of 69.52, a specificity of 91.63%, an NPV of 94.46% and a PPV of 59.44%. Our Al framework can accurately detect COVID-19. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the implementation of isolation procedures and early intervention.

Could KL-6 levels in COVID-19 help to predict lung disease?

BACKGROUND: Coronavirus disease COVID-19 has become a public health emergency of international concern. Together with the quest for an effective treatment, the question of the post-infectious evolution of affected patients in healing process remains uncertain. Krebs von den Lungen 6 (KL-6) is a high molecular weight mucin-like glycoprotein produced by type II pneumocytes and bronchial epithelial cells. Its production is raised during epithelial lesions and cellular regeneration. In COVID-19 infection, KL-6 serum levels could therefore be of interest for diagnosis, prognosis and therapeutic response evaluation. MATERIALS AND METHODS: Our study retrospectively compared KL-6 levels between a cohort of 83 COVID-19 infected patients and two other groups: healthy subjects (n = 70) on one hand, and a heterogenous group of patients suffering from interstitial lung diseases (n = 31; composed of 16 IPF, 4 sarcoidosis, 11 others) on the other hand. Demographical, clinical and laboratory indexes were collected. Our study aims to compare KL-6 levels between a COVID-19 population and healthy subjects or patients suffering from interstitial lung diseases (ILDs). Ultimately, we ought to determine whether KL-6 could be a marker of disease severity and bad prognosis. RESULTS: Our results showed that serum KL-6 levels in COVID-19 patients were increased compared to healthy subjects, but to a lesser extent than in patients suffering from ILD. Increased levels of KL-6 in COVID-19 patients were associated with a more severe lung disease. DISCUSSION AND CONCLUSION: Our results suggest that KL-6 could be a good biomarker to assess ILD severity in COVID-19 infection. Concerning the therapeutic response prediction, more studies are necessary.

[Chest radiological lesions in COVID-19 : from classical imaging to artificial intelligence]

In the course of the pandemic induced by the appearance of a new coronavirus (SARS-CoV-2;COVID-19) causing acute respiratory distress syndrome (ARDS), we had to rethink the diagnostic approach for patients suffering from respiratory symptoms. Indeed, although the use of RT-PCR remains the keystone of the diagnosis, the delay in diagnosis as well as the overload of the microbiological platforms have led us to make almost systematic the use of thoracic imaging for taking in charge of patients. In this context, thoracic imaging has shown a major interest in diagnostic aid in order to better guide the management of patients admitted to hospital. The most common signs encountered are particularly well described in thoracic computed tomography. Typical imaging combines bilateral, predominantly peripheral and posterior, multi-lobar, ground glass opacities. Of note, it is common to identify significant lesions in asymptomatic patients, with imaging sometimes preceding the onset of symptoms. Beyond conventional chest imaging, many teams have developed new artificial intelligence tools to better help clinicians in decision-making.

Single-center experience of patients with interstitial lung diseases during the early days of the COVID-19 pandemic.

INTRODUCTION: Patients with interstitial lung diseases (ILD) can be suspected to be at risk of experiencing a rapid flare-up due to COVID-19. However, no specific data are currently available for these patients. METHODS: We retrospectively analyzed a cohort of 401 patients with ILD and determined the proportion of patients hospitalized for proven severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and specific symptoms of COVID-19. RESULTS: We found that 1% of patients (n = 4) were hospitalized (1 in ICU) for COVID-19. In total, 310 of the 401 patients answered the phone call. Only 33 patients (0.08%) experienced specific symptoms of SARS-CoV-2 infection. CONCLUSION: Our study did not demonstrate any increased occurrence of severe COVID-19 in ILD patients compared to the global population. Based on our findings, we could not make any conclusion on the incidence rate of SARS-CoV-2 infection in patients with ILDs, or on the overall outcome of immunocompromised patients affected by COVID-19.