Patients without Increased Lymphocyte Counts and Decreased CT Scores During the Early 2nd Week of Illness Onset may Develop to Severe COVID-19.

BACKGROUND: Lymphopenia and high CT score is associated with COVID-19 severity. Herein we describe the change pattern in lymphocyte count and CT score during hospitalization and explore a possible association with the severity of COVID-19. METHODS: In this retrospective study, 13 non-severe COVID-19 patients diagnosed at admission were enrolled. One patient progressed to severe disease. Change patterns in lymphocyte counts and CT scores of all patients were analyzed. RESULTS: Lymphocyte count increased gradually from day 5 post-illness onset (day 5 vs. day 15, p = 0.001). Lymphocyte count of the severe patient fluctuated at low levels throughout the 15-day period. Chest CT scores of non-severe patients increased significantly during the first 5 days of illness onset, but decreased gradually beginning day 9 (illness onset vs. day 5, p = 0.002, day 9 vs. day 15, p = 0.015). In the severe patient, CT score continued to increase over the 11 days post-illness onset period. CONCLUSIONS: Non-severe COVID-19 patients had significantly increased lymphocyte counts and decreased CT scores beginning day 5 and day 9 of illness onset, respectively. The patients without increased lymphocyte counts and decreased CT scores during the early 2nd week of illness onset may develop to severe COVID-19.

Oximetry at admission as a predictor of tomographic and functional impairment after 3-6 months in hospitalized patients with COVID-19.

OBJECTIVE: To investigate characteristics that may be associated with radiologic and functional findings following discharge in patients with severe coronavirus disease 2019 (COVID-19). METHODS: This single-center, prospective, observational cohort study comprised patients aged >18 years who were hospitalized with COVID-19 pneumonia, between May and October 2020. After 3 to 6 months of discharge, patients were clinically evaluated and underwent spirometry, a 6-minute walk test (6MWT), and chest computed tomography (CT). Statistical analysis was performed using association and correlation tests. RESULTS: A total of 134 patients were included (25/114 [22%] were admitted with severe hypoxemia). On the follow-up chest CT, 29/92 (32%) had no abnormalities, regardless of the severity of the initial involvement, and the mean 6MWT distance was 447 m. Patients with desaturation on admission had an increased risk of remaining CT abnormalities: patients with SpO2 between 88 and 92% had a 4.0-fold risk, and those with SpO2 < 88% had a 6.2-fold risk. The group with SpO2 < 88% also walked shorter distances than patients with SpO2 between 88 and 92%. CONCLUSION: Initial hypoxemia was found to be a good predictor of persistent radiological abnormalities in follow-up and was associated with low performance in 6MWT.

Learning without forgetting by leveraging transfer learning for detecting COVID-19 infection from CT images.

COVID-19, a global pandemic, has killed thousands in the last three years. Pathogenic laboratory testing is the gold standard but has a high false-negative rate, making alternate diagnostic procedures necessary to fight against it. Computer Tomography (CT) scans help diagnose and monitor COVID-19, especially in severe cases. But, visual inspection of CT images takes time and effort. In this study, we employ Convolution Neural Network (CNN) to detect coronavirus infection from CT images. The proposed study utilized transfer learning on the three pre-trained deep CNN models, namely VGG-16, ResNet, and wide ResNet, to diagnose and detect COVID-19 infection from the CT images. However, when the pre-trained models are retrained, the model suffers the generalization capability to categorize the data in the original datasets. The novel aspect of this work is the integration of deep CNN architectures with Learning without Forgetting (LwF) to enhance the model's generalization capabilities on both trained and new data samples. The LwF makes the network use its learning capabilities in training on the new dataset while preserving the original competencies. The deep CNN models with the LwF model are evaluated on original images and CT scans of individuals infected with Delta-variant of the SARS-CoV-2 virus. The experimental results show that of the three fine-tuned CNN models with the LwF method, the wide ResNet model's performance is superior and effective in classifying original and delta-variant datasets with an accuracy of 93.08% and 92.32%, respectively.

Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images.

Coronavirus disease 2019 (COVID-19) has seen a crucial outburst for both females and males worldwide. Automatic lung infection detection from medical imaging modalities provides high potential for increasing the treatment for patients to tackle COVID-19 disease. COVID-19 detection from lung CT images is a rapid way of diagnosing patients. However, identifying the occurrence of infectious tissues and segmenting this from CT images implies several challenges. Therefore, efficient techniques termed as Remora Namib Beetle Optimization_ Deep Quantum Neural Network (RNBO_DQNN) and RNBO_Deep Neuro Fuzzy Network (RNBO_DNFN) are introduced for the identification as well as classification of COVID-19 lung infection. Here, the pre-processing of lung CT images is performed utilizing an adaptive Wiener filter, whereas lung lobe segmentation is performed employing the Pyramid Scene Parsing Network (PSP-Net). Afterwards, feature extraction is carried out wherein features are extracted for the classification phase. In the first level of classification, DQNN is utilized, tuned by RNBO. Furthermore, RNBO is designed by merging the Remora Optimization Algorithm (ROA) and Namib Beetle Optimization (NBO). If a classified output is COVID-19, then the second-level classification is executed using DNFN for further classification. Additionally, DNFN is also trained by employing the newly proposed RNBO. Furthermore, the devised RNBO_DNFN achieved maximum testing accuracy, with TNR and TPR obtaining values of 89.4%, 89.5% and 87.5%.

Pediatric Point-of-Care Lung Ultrasonography: A Narrative Review.

Point-of-care lung ultrasonography is an evidence-based application that may play a vital role in the care of critically ill pediatric patients. Lung ultrasonography has the advantage of being available at the patient's bedside with results superior to chest radiography and comparable to chest computed tomography for most lung pathologies. It has a steep learning curve. It can be readily performed in both advanced healthcare systems and resource-scarce settings. The purpose of this review is to discuss the basic principles of lung ultrasonography and its applications in the evaluation and treatment of critically ill pediatric patients.

Relation of impulse oscillometry and spirometry with quantitative thorax computed tomography after COVID-19 pneumonia.

OBJECTIVE: This study aimed to investigate if there is any correlation between the quantitative computed tomography and the impulse oscillometry or spirometry results of post-COVID-19 patients. METHODS: The study comprised 47 post-COVID-19 patients who had spirometry, impulse oscillometry, and high-resolution computed tomography examinations at the same time. The study group consisted of 33 patients with quantitative computed tomography involvement, while the control group included 14 patients who did not have CT findings. The quantitative computed tomography technology was used to calculate percentages of density range volumes. The relationship between percentages of density range volumes for different quantitative computed tomography density ranges and impulse oscillometry-spirometry findings was statistically analyzed. RESULTS: In quantitative computed tomography, the percentage of relatively high-density lung parenchyma, including fibrotic areas, was 1.76±0.43 and 5.65±3.73 in the control and study groups, respectively. The percentages of primarily ground-glass parenchyma areas were found to be 7.60±2.86 and 29.25±16.50 in the control and study groups, respectively. In the correlation analysis, the forced vital capacity% predicted in the study group was correlated with DRV%[(-750)-(-500)] (volume of the lung parenchyma that has density between (-750)-(-500) Hounsfield units), but no correlation with DRV%[(-500)-0] was detected. Also, reactance area and resonant frequency were correlated with DRV%[(-750)-(-500)], while X5 was correlated with both DRV%[(-500)-0] and DRV%[(-750)-(-500)] density. Modified Medical Research Council score was correlated with predicted percentages of forced vital capacity and X5. CONCLUSION: After COVID-19, forced vital capacity, reactance area, resonant frequency, and X5 correlated with the percentages of density range volumes of ground-glass opacity areas in the quantitative computed tomography. X5 was the only parameter correlated with density ranges consistent with both ground-glass opacity and fibrosis. Furthermore, the percentages of forced vital capacity and X5 were shown to be associated with the perception of dyspnea.

Ultrahigh-Resolution Photon-Counting Detector CT of the Lungs: Association of Reconstruction Kernel and Slice Thickness With Image Quality.

BACKGROUND. Prior work has shown improved image quality for photon-counting detector (PCD) CT of the lungs compared with energy-integrating detector CT. A paucity of the literature has compared PCD CT of the lungs using different reconstruction parameters. OBJECTIVE. The purpose of this study is to the compare the image quality of ultra-high-resolution (UHR) PCD CT image sets of the lungs that were reconstructed using different kernels and slice thicknesses. METHODS. This retrospective study included 29 patients (17 women and 12 men; median age, 56 years) who underwent noncontrast chest CT from February 15, 2022, to March 15, 2022, by use of a commercially available PCD CT scanner. All acquisitions used UHR mode (1024 × 1024 matrix). Nine image sets were reconstructed for all combinations of three sharp kernels (BI56, BI60, and BI64) and three slice thicknesses (0.2, 0.4, and 1.0 mm). Three radiologists independently reviewed reconstructions for measures of visualization of pulmonary anatomic structures and pathologies; reader assessments were pooled. Reconstructions were compared with the clinical reference reconstruction (obtained using the BI64 kernel and a 1.0-mm slice thickness [BI641.0-mm]). RESULTS. The median difference in the number of bronchial divisions identified versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.5), BI600.4-mm (0.3), BI640.2-mm (0.5), and BI600.2-mm (0.2) (all p < .05). The median bronchial wall sharpness versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.3) and BI640.2-mm (0.3) and was lower for BI561.0-mm (-0.7) and BI560.4-mm (-0.3) (all p < .05). Median pulmonary fissure sharpness versus the clinical reference reconstruction was higher for reconstructions with BI640.4-mm (0.3), BI600.4-mm (0.3), BI560.4-mm (0.5), BI640.2-mm (0.5), BI600.2-mm (0.5), and BI560.2-mm (0.3) (all p < .05). Median pulmonary vessel sharpness versus the clinical reference reconstruction was lower for reconstructions with BI561.0-mm (-0.3), BI600.4-mm (-0.3), BI560.4-mm (-0.7), BI640.2-mm (-0.7), BI600.2-mm (-0.7), and BI560.2-mm (-0.7). Median lung nodule conspicuity versus the clinical reference reconstruction was lower for reconstructions with BI561.0-mm (-0.3) and BI560.4-mm (-0.3) (both p < .05). Median conspicuity of all other pathologies versus the clinical reference reconstruction was lower for reconstructions with BI561.0 mm (-0.3), BI560.4-mm (-0.3), BI640.2-mm (-0.3), BI600.2-mm (-0.3), and BI560.2-mm (-0.3). Other comparisons among reconstructions were not significant (all p > .05). CONCLUSION. Only the reconstruction using BI640.4-mm yielded improved bronchial division identification and bronchial wall and pulmonary fissure sharpness without a loss in pulmonary vessel sharpness or conspicuity of nodules or other pathologies. CLINICAL IMPACT. The findings of this study may guide protocol optimization for UHR PCD CT of the lungs.

The impact of the COVID-19 pandemic on nasopharyngeal carcinoma extent at FDG PET/MR staging: The NPCOVIPET study.

BACKGROUND: To evaluate the impact of coronavirus disease 2019 (COVID-19) pandemic on disease extent in patients with nasopharyngeal carcinoma (NPC) using 18 fuorodeoxyglucose (FDG) positron emission tomography (PET)/magnetic resonance imaging (MRI). METHODS: This retrospective cohort study included biopsy-proven, newly diagnosed NPC patients using whole-body FDG PET/MR staging in two selected intervals: 1 May 2017 to 31 January 2020 (Group A, the pre-COVID-19 period), and 1 February 2020 to 30 June 2021 (Group B, the COVID-19 period). RESULTS: Three-hundred and ninety patients were included. No significant difference was observed in terms of T classification, N classification, overall stage, N stations, and M stations between the two groups (p > 0.05). For the involved neck node levels, more patients had developed level Vc metastasis in the group B (p = 0.044). CONCLUSION: Although the overall stage was not affected, more patients with NPC had developed level Vc metastasis in the era of COVID-19.

Correlation of bone density measured on CT chest with the severity of COVID-19 infection: A retrospective study.

PURPOSE: This retrospective study investigated the correlation between bone mineral density (BMD) and COVID-19 severity among COVID-19 patients who underwent chest computed tomography (CT) scans. METHODS: This study was carried out at the King Abdullah Medical Complex in Jeddah, Saudi Arabia, one of the largest COVID-19 centers in the western province. All adult COVID-19 patients who had a chest CT between January 2020 and April 2022 were included in the study. The pulmonary severity scores (PSS) and vertebral BMD measurements were obtained from the patient's CT chest. Data from the patients' electronic records were collected. RESULTS: The average patient age was 56.4 years, and most (73.5%) patients were men. Diabetes (n = 66, 48.5%), hypertension (n = 56, 41.2%), and coronary artery disease (n = 17, 12.5%) were the most prevalent comorbidities. Approximately two-thirds of hospitalized patients required ICU admission (64%), and one-third died (30%). The average length of stay in the hospital was 28.4 days. The mean CT pneumonia severity score (PSS) was 10.6 at the time of admission. Patients with lower vertebral BMD (< = 100) numbered 12 (8.8%), while those with higher vertebral BMD (>100) numbered 124 (91.2%). Only 46 out of the total survived patients (n = 95) were admitted to the ICU versus all deceased (P<0.01). The logistic regression analysis revealed that an elevated PSS upon admission resulted in a reduced chance of survival. Age, gender, and BMD did not predict survival chances. CONCLUSION: The BMD had no prognostic advantage, and the PSS was the significant factor that could have predicted the outcome.

Lipase Elevation in Patients With COVID-19.

INTRODUCTION: Although coronavirus disease (COVID-19) has been associated with gastrointestinal manifestations, its effect on the pancreas remains unclear. We aimed to assess the frequency and characteristics of hyperlipasemia in patients with COVID-19. METHODS: A retrospective cohort study of hospitalized patients across 6 US centers with COVID-19. RESULTS: Of 71 patients, 9 (12.1%) developed hyperlipasemia, with 2 (2.8%) greater than 3 times upper limit of normal. No patient developed acute pancreatitis. Hyperlipasemia was not associated with poor outcomes or symptoms. DISCUSSION: Although a mild elevation in serum lipase was observed in some patients with COVID-19, clinical acute pancreatitis was not seen.

Halo Traction Followed by Halo Vest Immobilization for the Treatment of Chronic Atlantoaxial Rotatory Fixation: A Case Report and Tips for Avoiding Complications in Pediatric Patients.

CASE: A 4-year-old girl sustained a traumatic atlantoaxial rotatory subluxation. She presented at the treating facility 8 months after injury with cervical deformity, neck pain, gait instability, and decreased cervical motion. Her delay in presentation was partially because of international Corona Virus of 2019 (COVID-19) travel restrictions. The case was successfully treated with halo traction, followed by halo vest immobilization. CONCLUSION: Chronic atlantoaxial rotatory fixation can be treated nonsurgically with closed reduction and halo traction, but is associated with operative risks. Optimal pin placement is challenging in the pediatric skull and may be improved with a preoperative or intraoperative computed tomography (CT) scan.

CT features of pulmonary embolism in patients with COVID-19 pneumonia.

OBJECTIVE: The aim of this study was to describe the Computed Tomography (CT) features of pulmonary embolism in patients hospitalized for acute COVID-19 pneumonia and to evaluate the prognostic significance of these features. PATIENTS AND METHODS: This retrospective study included 110 consecutive patients who were hospitalized for acute COVID-19 pneumonia and underwent pulmonary computed tomography angiography (BTPA) on the basis of clinical suspicion. The diagnosis of COVID-19 infection was determined by CT findings typical of COVID-19 pneumonia and/or a positive result of a reverse transcriptase-polymerase chain reaction test. RESULTS: Of the 110 patients, 30 (27.3%) had acute pulmonary embolism and 71 (64.5%) had CT features of chronic pulmonary embolism. Of the 14 (12.7%) patients who died despite receiving therapeutic doses of heparin, 13 (92.9%) had CT features of chronic pulmonary embolism and 1 (7.1%) of acute pulmonary embolism. CT features of chronic pulmonary embolism were more common in deceased patients than in surviving patients (92.9% vs. 60.4%, p=0.01, respectively). Low oxygen saturation and high urine microalbumin creatinine ratio at admission in COVID-19 patients are important determinants of mortality after adjusting for sex and age in logistic procedures. CONCLUSIONS: CT features of chronic pulmonary embolism are common in COVID-19 patients undergoing Computed Tomography Pulmonary Angiography (CTPA) in the hospital. The coexistence of albuminuria, low oxygen saturation and CT features of chronic pulmonary embolism at admission in COVID-19 patients may herald fatal outcomes.

Extension of Lung Damage at Chest Computed Tomography in Severely Ill COVID-19 Patients Treated with Interleukin-6 Receptor Blockers Correlates with Inflammatory Cytokines Production and Prognosis.

Elevated inflammatory markers are associated with severe coronavirus disease 2019 (COVID-19), and some patients benefit from Interleukin (IL)-6 pathway inhibitors. Different chest computed tomography (CT) scoring systems have shown a prognostic value in COVID-19, but not specifically in anti-IL-6-treated patients at high risk of respiratory failure. We aimed to explore the relationship between baseline CT findings and inflammatory conditions and to evaluate the prognostic value of chest CT scores and laboratory findings in COVID-19 patients specifically treated with anti-IL-6. Baseline CT lung involvement was assessed in 51 hospitalized COVID-19 patients naive to glucocorticoids and other immunosuppressants using four CT scoring systems. CT data were correlated with systemic inflammation and 30-day prognosis after anti-IL-6 treatment. All the considered CT scores showed a negative correlation with pulmonary function and a positive one with C-reactive protein (CRP), IL-6, IL-8, and Tumor Necrosis Factor α (TNF-α) serum levels. All the performed scores were prognostic factors, but the disease extension assessed by the six-lung-zone CT score (S24) was the only independently associated with intensive care unit (ICU) admission (p = 0.04). In conclusion, CT involvement correlates with laboratory inflammation markers and is an independent prognostic factor in COVID-19 patients representing a further tool to implement prognostic stratification in hospitalized patients.

An unresponsive COVID-19 patient.

Neurological manifestations and complications are increasingly reported in coronavirus disease-19 (COVID-19) patients. Although pulmonary manifestations are more common, patients with severe disease may present with neurological symptoms such as in our case. We describe a case report of a 50-year-old male without previous known comorbidity who was found unresponsive due to COVID-19-related neurological complications. During this pandemic, an emergency radiologist should be well acquainted with various neurological manifestations of COVID-19. In this article, we will discuss the pathogenesis, imaging findings, and differentials of this disease.

Pediatric COVID-19: Correlations between Clinical and Imaging Perspectives.

Background: Although SARS-CoV-2 infection primarily affects adults, the increasing emergence of infected pediatric patients has been recently reported. However, there is a paucity of data regarding the value of imaging in relation to the clinical severity of this pandemic emergency. Objectives: To demonstrate the relationships between clinical and radiological COVID-19 findings and to determine the most effective standardized pediatric clinical and imaging strategies predicting the disease severity. Patients and Methods. This observational study enrolled eighty pediatric patients with confirmed COVID-19 infection. The studied patients were categorized according to the disease severity and the presence of comorbidities. Patients' clinical findings, chest X-ray, and CT imaging results were analyzed. Patients' evaluations using several clinical and radiological severity scores were recorded. The relations between clinical and radiological severities were examined. Results: Significant associations were found between severe-to-critical illness and abnormal radiological findings (p = 0.009). In addition, chest X-ray score, chest CT severity score, and rapid evaluation of anamnesis, PO2, imaging disease, and dyspnea-COVID (RAPID-COVID) score were significantly higher among patients with severe infection (p < 0.001, <0.001, and 0.001) and those with comorbidities (p = 0.005, 0.002, and <0.001). Conclusions: Chest imaging of pediatric patients with COVID-19 infection may be of value during the evaluation of severe cases of infected pediatric patients and in those with underlying comorbid conditions, especially during the early stage of infection. Moreover, the combined use of specific clinical and radiological COVID-19 scores are likely to be a successful measure of the extent of disease severity.

Chest Tightness with Cycling Turned Out To Be Pneumomediastinum.

BACKGROUND Pneumomediastinum, or mediastinal emphysema, means air present inside the mediastinum. It usually presents with symptoms of chest pain and shortness of breath. Examination can be significant for crepitus along the neck area. There are many risk factors associated with pneumomediastinum, including asthma and COVID-19. Most cases of pneumomediastinum improve with conservative management, and surgery (mediastinotomy) is reserved for complicated cases with tension pneumomediastinum. CASE REPORT This is the case of a 23-year-old man who presented with chest tightness after 3.5 h of cycling. The patient did have a prior history of clinically stable asthma, with no recent exacerbation, and denied any other associative factors. Imaging was significant for pneumomediastinum. The patient was admitted for observation in the hospital and treated with supportive care, without any surgical intervention. The patient had appropriate improvement in his symptoms in 24 h. Repeat imaging showed improvement in the pneumomediastinum, and the patient was discharged to outpatient follow-up. CONCLUSIONS Our case presents a unique link between cycling and pneumomediastinum. Prolonged cycling may emerge as a risk factor for this complication. People with a previous history of pneumomediastinum should be careful to review other risk factors prior to planning long-distance bicycling. Physicians need to keep this differential diagnosis in mind when encountering a patient with similar symptoms so that a timely diagnosis is made.

Severity prediction of pulmonary diseases using chest CT scans via cost-sensitive label multi-kernel distribution learning.

BACKGROUND AND OBJECTIVES: The progression of pulmonary diseases is a complex progress. Timely predicting whether the patients will progress to the severe stage or not in its early stage is critical to take appropriate hospital treatment. However, this task suffers from the "insufficient and incomplete" data issue since it is clinically impossible to have adequate training samples for one patient at each day. Besides, the training samples are extremely imbalanced since the patients who will progress to the severe stage is far less than those who will not progress to the non-severe stage. METHOD: We consider the severity prediction of pulmonary diseases as a time estimation problem based on CT scans. To handle the issue of "insufficient and incomplete" training samples, we introduced label distribution learning (LDL). Specifically, we generate a label distribution for each patient, making a CT image contribute to not only the learning of its chronological day, but also the learning of its neighboring days. In addition, a cost-sensitive mechanism is introduced to explore the imbalance data issue. To identify the importance of pulmonary segments in pulmonary disease severity prediction, multi-kernel learning in composite kernel space is further incorporated and particle swarm optimization (PSO) is used to find the optimal kernel weights. RESULTS: We compare the performance of the proposed CS-LD-MKSVR algorithm with several classical machine learning algorithms and deep learning (DL) algorithms. The proposed method has obtained the best classification results on the in-house data, fully indicating its effectiveness in pulmonary disease severity prediction. CONTRIBUTIONS: The severity prediction of pulmonary diseases is considered as a time estimation problem, and label distribution is introduced to describe the conversion time from non-severe stage to severe stage. The cost-sensitive mechanism is also introduced to handle the data imbalance issue to further improve the classification performance.

Diagnosing the acute abdomen during a global contrast shortage: a single centre experience.

BACKGROUND: The COVID-19 pandemic led to a global shortage of iodinated contrast media (ICM) in early 2022. ICM is used in more than half of the computed tomography of the abdomen and pelvis (CTAP) performed to diagnose an acute abdomen (AA). In response to the shortage, the RANZCR published contrast-conserving recommendations. This study aimed to compare AA diagnostic outcomes of non-contrast CTs performed before and during the shortage. METHODS: A single-centre retrospective observational cohort study of all adult patients presenting with an AA who underwent a CTAP was conducted during the contrast shortage period from May to July 2022. The pre-shortage control comparison group was from January to March 2022; key demographics, imaging modality indication and diagnostic outcomes were collected and analysed using SPSS v27. RESULTS: Nine hundred and sixty-two cases met the inclusion criteria, of which n = 502, 52.2% were in the shortage period group. There was a significant increase of 464% in the number of non-contrast CTAPs performed during the shortage period (P < 0.001). For the six AA pathologies, only n = 3, 1.8% of non-contrast CTAPs had equivocal findings requiring further imaging with a contrast CTAP. Of the total CTs performed, n = 464, 48.2% were negative. CONCLUSION: This study showed that when non-contrast CTs are selected appropriately, they appear to be non-inferior to contrast-enhanced CTAPs in diagnosing acute appendicitis, colitis, diverticulitis, hernia, collection, and obstruction. This study highlights the need for further research into utilizing non-contrast scans for assessing the AA to minimize contrast-associated complications.

A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative.

BACKGROUND: Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19). OBJECTIVES: To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity. METHODS: The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected. RESULTS: A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user. CONCLUSION: We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.