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1.
BMC Med Inform Decis Mak ; 22(1): 284, 2022 11 02.
Article in English | MEDLINE | ID: covidwho-2098335

ABSTRACT

BACKGROUND: The sensitivity of RT-PCR in diagnosing COVID-19 is only 60-70%, and chest CT plays an indispensable role in the auxiliary diagnosis of COVID-19 pneumonia, but the results of CT imaging are highly dependent on professional radiologists. AIMS: This study aimed to develop a deep learning model to assist radiologists in detecting COVID-19 pneumonia. METHODS: The total study population was 437. The training dataset contained 26,477, 2468, and 8104 CT images of normal, CAP, and COVID-19, respectively. The validation dataset contained 14,076, 1028, and 3376 CT images of normal, CAP, and COVID-19 patients, respectively. The test set included 51 normal cases, 28 CAP patients, and 51 COVID-19 patients. We designed and trained a deep learning model to recognize normal, CAP, and COVID-19 patients based on U-Net and ResNet-50. Moreover, the diagnoses of the deep learning model were compared with different levels of radiologists. RESULTS: In the test set, the sensitivity of the deep learning model in diagnosing normal cases, CAP, and COVID-19 patients was 98.03%, 89.28%, and 92.15%, respectively. The diagnostic accuracy of the deep learning model was 93.84%. In the validation set, the accuracy was 92.86%, which was better than that of two novice doctors (86.73% and 87.75%) and almost equal to that of two experts (94.90% and 93.88%). The AI model performed significantly better than all four radiologists in terms of time consumption (35 min vs. 75 min, 93 min, 79 min, and 82 min). CONCLUSION: The AI model we obtained had strong decision-making ability, which could potentially assist doctors in detecting COVID-19 pneumonia.


Subject(s)
COVID-19 , Deep Learning , Humans , COVID-19/diagnostic imaging , SARS-CoV-2 , Tomography, X-Ray Computed/methods , Research Design
2.
Ter Arkh ; 94(4): 485-490, 2022 May 26.
Article in Russian | MEDLINE | ID: covidwho-2091497

ABSTRACT

AIM: To develop a protocol for ultrasound diagnostics of COVID-19 pneumonia and to assess the diagnostic capabilities of the method in comparison with computer tomography (CT). MATERIALS AND METHODS: The study included 59 patients with a new coronavirus infection. In order to identify changes in the lung tissue characteristic of a new coronavirus infection, we used a special protocol for ultrasound of the lungs, which was developed by us in such a way that the data obtained were compared by segment with the results of CT of the lungs. RESULTS: When comparing the results of lung ultrasound with the data of CT diagnostics, according to the new protocol, the percentage of lung tissue damage during ultrasound of the lungs averaged 70.8% in the group [62.5; 87.5], and according to the results of CT 70.0% [60.0; 72.5] (p=0.427). Thus, the ultrasound of the lung lesions was almost completely consistent with the changes revealed by CT. In order to assess the diagnostic value of lung ultrasound in identifying severe lung tissue lesions corresponding to CT 34, ROC analysis was performed, which showed the high diagnostic value of lung ultrasound in identifying severe lung tissue lesions. CONCLUSION: A new protocol was developed for assessing the severity of lung tissue damage according to ultrasound data, which showed a high diagnostic value in detecting COVID-19 pneumonia in comparison with CT. The results obtained give reason to recommend this protocol of ultrasound of the lungs as a highly sensitive method in diagnosing the severity of COVID-19 pneumonia. Its application is very important for dynamic examination of patients, especially in conditions of low availability of CT.


Subject(s)
COVID-19 , Humans , COVID-19/diagnostic imaging , SARS-CoV-2 , Lung/diagnostic imaging , Tomography, X-Ray Computed/methods , Computers , Retrospective Studies
3.
Medicina (Kaunas) ; 58(11)2022 Oct 26.
Article in English | MEDLINE | ID: covidwho-2090278

ABSTRACT

For COVID-19 pneumonia, many manifestations such as fever, dyspnea, dry cough, anosmia and tiredness have been described, but differences have been observed from person to person according to age, pulmonary function, damage and severity. In clinical practice, it has been found that patients with severe forms of infection with COVID-19 develop serious complications, including pneumomediastinum. Although two years have passed since the beginning of the pandemic with the SARS-CoV-2 virus and progress has been made in understanding the pathophysiological mechanisms underlying the COVID-19 infection, there are also unknown factors that contribute to the evolution of the disease and can lead to the emergence some complications. In this case report, we present a patient with COVID-19 infection who developed a massive spontaneous pneumomediastinum and subcutaneous emphysema during hospitalization, with no pre-existing lung pathology and no history of smoking. The patient did not get mechanical ventilation or chest trauma, but the possible cause could be severe alveolar inflammation. The CT results highlighted pneumonia in context with SARS-CoV-2 infection affecting about 50% of the pulmonary area. During hospitalization, lung lesions evolved 80% pulmonary damage associated with pneumomediastinum and subcutaneous emphysema. After three months, the patient completely recovered and the pneumomediastinum fully recovered with the complete disappearance of the lesions. Pneumomediastinum is a severe and rare complication in COVID-19 pneumonia, especially in male patients, without risk factors, and an early diagnosis can increase the chances of survival.


Subject(s)
COVID-19 , Mediastinal Emphysema , Subcutaneous Emphysema , Humans , Male , Mediastinal Emphysema/diagnosis , Mediastinal Emphysema/etiology , COVID-19/complications , SARS-CoV-2 , Tomography, X-Ray Computed/methods , Subcutaneous Emphysema/etiology , Subcutaneous Emphysema/complications
4.
Korean J Radiol ; 21(10): 1150-1160, 2020 10.
Article in English | MEDLINE | ID: covidwho-2089785

ABSTRACT

OBJECTIVE: To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance. MATERIALS AND METHODS: In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated. RESULTS: Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients (4.8%) were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes; p < 0.001). CONCLUSION: Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.


Subject(s)
Betacoronavirus , Coronavirus Infections/diagnostic imaging , Deep Learning , Pneumonia, Viral/diagnostic imaging , Radiography, Thoracic , Adult , Aged , COVID-19 , Female , Humans , Male , Middle Aged , Pandemics , Radiography, Thoracic/methods , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods
5.
Technol Health Care ; 30(6): 1299-1314, 2022.
Article in English | MEDLINE | ID: covidwho-2089739

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a deadly viral infection spreading rapidly around the world since its outbreak in 2019. In the worst case a patient's organ may fail leading to death. Therefore, early diagnosis is crucial to provide patients with adequate and effective treatment. OBJECTIVE: This paper aims to build machine learning prediction models to automatically diagnose COVID-19 severity with clinical and computed tomography (CT) radiomics features. METHOD: P-V-Net was used to segment the lung parenchyma and then radiomics was used to extract CT radiomics features from the segmented lung parenchyma regions. Over-sampling, under-sampling, and a combination of over- and under-sampling methods were used to solve the data imbalance problem. RandomForest was used to screen out the optimal number of features. Eight different machine learning classification algorithms were used to analyze the data. RESULTS: The experimental results showed that the COVID-19 mild-severe prediction model trained with clinical and CT radiomics features had the best prediction results. The accuracy of the GBDT classifier was 0.931, the ROUAUC 0.942, and the AUCPRC 0.694, which indicated it was better than other classifiers. CONCLUSION: This study can help clinicians identify patients at risk of severe COVID-19 deterioration early on and provide some treatment for these patients as soon as possible. It can also assist physicians in prognostic efficacy assessment and decision making.


Subject(s)
COVID-19 , Humans , COVID-19/diagnostic imaging , Tomography, X-Ray Computed/methods , Machine Learning , Lung/diagnostic imaging , Algorithms , Retrospective Studies
6.
Crit Care ; 26(1): 328, 2022 10 25.
Article in English | MEDLINE | ID: covidwho-2089224

ABSTRACT

BACKGROUND: Steroids have been shown to reduce inflammation, hypoxic pulmonary vasoconstriction (HPV) and lung edema. Based on evidence from clinical trials, steroids are widely used in severe COVID-19. However, the effects of steroids on pulmonary gas volume and blood volume in this group of patients are unexplored. OBJECTIVE: Profiting by dual-energy computed tomography (DECT), we investigated the relationship between the use of steroids in COVID-19 and distribution of blood volume as an index of impaired HPV. We also investigated whether the use of steroids influences lung weight, as index of lung edema, and how it affects gas distribution. METHODS: Severe COVID-19 patients included in a single-center prospective observational study at the intensive care unit at Uppsala University Hospital who had undergone DECT were enrolled in the current study. Patients' cohort was divided into two groups depending on the administration of steroids. From each patient's DECT, 20 gas volume maps and the corresponding 20 blood volume maps, evenly distributed along the cranial-caudal axis, were analyzed. As a proxy for HPV, pulmonary blood volume distribution was analyzed in both the whole lung and the hypoinflated areas. Total lung weight, index of lung edema, was estimated. RESULTS: Sixty patients were analyzed, whereof 43 received steroids. Patients not exposed to steroids showed a more extensive non-perfused area (19% vs 13%, p < 0.01) and less homogeneous pulmonary blood volume of hypoinflated areas (kurtosis: 1.91 vs 2.69, p < 0.01), suggesting a preserved HPV compared to patients treated with steroids. Moreover, patients exposed to steroids showed a significantly lower lung weight (953 gr vs 1140 gr, p = 0.01). A reduction in alveolar-arterial difference of oxygen followed the treatment with steroids (322 ± 106 mmHg at admission vs 267 ± 99 mmHg at DECT, p = 0.04). CONCLUSIONS: The use of steroids might cause impaired HPV and might reduce lung edema in severe COVID-19. This is consistent with previous findings in other diseases. Moreover, a reduced lung weight, as index of decreased lung edema, and a more homogeneous distribution of gas within the lung were shown in patients treated with steroids. TRIAL REGISTRATION: Clinical Trials ID: NCT04316884, Registered March 13, 2020.


Subject(s)
COVID-19 , Papillomavirus Infections , Humans , COVID-19/drug therapy , Tomography, X-Ray Computed/methods , Lung , Hypoxia , Oxygen , Steroids , Edema
7.
EBioMedicine ; 85: 104315, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2086128

ABSTRACT

BACKGROUND: Hepatic steatosis (HS) identified on CT may provide an integrated cardiometabolic and COVID-19 risk assessment. This study presents a deep-learning-based hepatic fat assessment (DeHFt) pipeline for (a) more standardised measurements and (b) investigating the association between HS (liver-to-spleen attenuation ratio <1 in CT) and COVID-19 infections severity, wherein severity is defined as requiring invasive mechanical ventilation, extracorporeal membrane oxygenation, death. METHODS: DeHFt comprises two steps. First, a deep-learning-based segmentation model (3D residual-UNet) is trained (N.ß=.ß80) to segment the liver and spleen. Second, CT attenuation is estimated using slice-based and volumetric-based methods. DeHFt-based mean liver and liver-to-spleen attenuation are compared with an expert's ROI-based measurements. We further obtained the liver-to-spleen attenuation ratio in a large multi-site cohort of patients with COVID-19 infections (D1, N.ß=.ß805; D2, N.ß=.ß1917; D3, N.ß=.ß169) using the DeHFt pipeline and investigated the association between HS and COVID-19 infections severity. FINDINGS: The DeHFt pipeline achieved a dice coefficient of 0.95, 95% CI [0.93...0.96] on the independent validation cohort (N.ß=.ß49). The automated slice-based and volumetric-based liver and liver-to-spleen attenuation estimations strongly correlated with expert's measurement. In the COVID-19 cohorts, severe infections had a higher proportion of patients with HS than non-severe infections (pooled OR.ß=.ß1.50, 95% CI [1.20...1.88], P.ß<.ß.001). INTERPRETATION: The DeHFt pipeline enabled accurate segmentation of liver and spleen on non-contrast CTs and automated estimation of liver and liver-to-spleen attenuation ratio. In three cohorts of patients with COVID-19 infections (N.ß=.ß2891), HS was associated with disease severity. Pending validation, DeHFt provides an automated CT-based metabolic risk assessment. FUNDING: For a full list of funding bodies, please see the Acknowledgements.


Subject(s)
COVID-19 , Deep Learning , Fatty Liver , Humans , Retrospective Studies , Tomography, X-Ray Computed/methods , Fatty Liver/diagnostic imaging , Severity of Illness Index
8.
Respir Investig ; 60(6): 762-771, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2076679

ABSTRACT

BACKGROUND: The purpose of this study was to assess the diagnostic accuracy of lung ultrasound (LUS) in determining the severity of coronavirus disease 2019 (COVID-19) pneumonia compared with thoracic computed tomography (CT) and establish the correlations between LUS score, inflammatory markers, and percutaneous oxygen saturation (SpO2). METHODS: This prospective observational study, conducted at Târgu-Mureș Pulmonology Clinic included 78 patients with confirmed severe acute respiratory syndrome coronavirus-2 infection via nasopharyngeal real-time-polymerase chain reaction (RT-PCR) (30 were excluded). Enrolled patients underwent CT, LUS, and blood tests on admission. Lung involvement was evaluated in 16 thoracic areas, using AB1 B2 C (letters represent LUS pattern) scores ranging 0-48. RESULTS: LUS revealed bilateral B-lines (97.8%), pleural irregularities with thickening/discontinuity (75%), and subpleural consolidations (70.8%). Uncommon sonographic patterns were alveolar consolidations with bronchogram (33%) and pleural effusion (2%). LUS score cutoff values of ≤14 and > 22 predicted mild COVID-19 (sensitivity [Se] = 84.6%; area under the curve [AUC] = 0.72; P = 0.002) and severe COVID-19 (Se = 50%, specificity (Sp) = 91.2%, AUC = 0.69; P = 0.02), respectively, and values > 29 predicted the patients' transfer to the intensive care unit (Se = 80%, Sp = 97.7%). LUS score positively correlated with CT score (r = 0.41; P = 0.003) and increased with the decrease of SpO2 (r = -0.49; P = 0.003), with lymphocytes decline (r = -0.52; P = 0.0001). Patients with consolidation patterns had higher ferritin and C-reactive protein than those with B-line patterns (P = 0.01; P = 0.03). CONCLUSIONS: LUS is a useful, non-invasive and effective tool for diagnosis, monitoring evolution, and prognostic stratification of COVID-19 patients.


Subject(s)
COVID-19 , Humans , COVID-19/diagnostic imaging , SARS-CoV-2 , Lung/diagnostic imaging , Ultrasonography/methods , Tomography, X-Ray Computed/methods
9.
Kathmandu Univ Med J (KUMJ) ; 19(75): 381-386, 2021.
Article in English | MEDLINE | ID: covidwho-2073760

ABSTRACT

Background Coronavirus disease (COVID-19) is the recent global health emergency making it crucial for rapid diagnosis and intervention. Computed tomography (CT) is important for screening, diagnosis and evaluating severity and disease progress. Objective To assess the CT changes in COVID patients and study its relationship with various factors. Method A retrospective study was conducted at Norvic International hospital from August 2020 to November 2020 among RT-PCR positive symptomatic COVID cases who had positive CT changes. CT imaging data were analyzed by radiology expertise. Statistical analysis was carried out with the help of SPSS 16. Result Out of 120 patients, 75% were males and mean age was 54.70±15.56 years. The mean CT severity score was 18.35±6.87. Pure ground glass opacities was seen in 74(61.7%), reticulations 89(74.2%) and crazy-paving pattern 28(23.3%). CT scans with bilateral 118(98.3%) and peripheral involvement 109(90.8%) in all five lobes. CT- severity score was positively correlated with oxygen and mechanical ventilation requirement (P-value < 0.05 and 0.011 respectively). Conclusion CT findings including pure ground glass opacities, reticulations, bilateral and peripheral involvement involving all five lobes were more frequent. Our data suggest that CT-severity score significantly correlates with oxygen and mechanical ventilation requirements.


Subject(s)
COVID-19 , Adult , Aged , COVID-19/diagnostic imaging , Female , Hospitals , Humans , Lung , Male , Middle Aged , Nepal , Oxygen , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods
10.
Tomography ; 8(5): 2435-2449, 2022 Sep 28.
Article in English | MEDLINE | ID: covidwho-2066488

ABSTRACT

BACKGROUND: The aim of this study was to evaluate CT (computed tomography) imaging differences for the Delta and the Omicron variant in COVID-19 infection. METHODS: The study population was derived from a retrospective study cohort investigating chest CT imaging patterns in vaccinated and nonvaccinated COVID-19 patients. CT imaging patterns of COVID-19 infection were evaluated by qualitative and semiquantitative scoring systems, as well as imaging pattern analysis. RESULTS: A total of 60 patients (70.00% male, 62.53 ± 17.3 years, Delta: 43 patients, Omicron: 17 patients) were included. Qualitative scoring systems showed a significant correlation with virus variants; "typical appearance" and "very high" degrees of suspicion were detected more often in patients with Delta (RSNA: p = 0.003; CO-RADS: p = 0.002; COV-RADS: p = 0.001). Semiquantitative assessment of lung changes revealed a significant association with virus variants in univariate (Delta: 6.3 ± 3.5; Omicron: 3.12 ± 3.2; p = 0.002) and multivariate analysis. The vacuolar sign was significantly associated with the Delta variant (OR: 14.74, 95% CI: [2.32; 2094.7], p = 0.017). CONCLUSION: The Delta variant had significantly more extensive lung involvement and showed changes classified as "typical" more often than the Omicron variant, while the Omicron variant was more likely associated with CT findings such as "absence of pulmonary changes". A significant correlation between the Delta variant and the vacuolar sign was observed.


Subject(s)
COVID-19 , Humans , Male , Female , Pilot Projects , COVID-19/diagnostic imaging , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods
12.
J Laryngol Otol ; 136(12): 1304-1308, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2062086

ABSTRACT

OBJECTIVE: Three-dimensional computed tomography reconstruction of the face has recently been presented as a newer diagnostic tool in coronavirus disease 2019 associated mucormycosis. This study was conducted to compare three-dimensional computed tomography reconstruction with conventional two-dimensional computed tomography in coronavirus disease 2019 associated mucormycosis. METHODS: A total of 123 mucormycosis patients underwent three-dimensional computed tomography reconstruction after a comprehensive clinical investigation. The involvement of the facial skeleton was noted. RESULTS: The anterior maxillary wall was most commonly involved (9.8 per cent). Involvement of the lateral maxillary wall was noted in 6.5 per cent of patients. Sixty-seven patients (54.5 per cent) underwent endoscopic surgery, 22 (17.9 per cent) underwent open surgical procedures, and 12 (9.8 per cent) had combined endoscopic and open surgical procedures. In 21 patients (17.1 per cent), open surgery was performed in the first instance based on additional three-dimensional computed tomography findings, and revision surgical procedures were avoided. CONCLUSION: Three-dimensional computed tomography of the face was found to be superior in determining the extent of disease. It reduces delays in diagnosis, facilitates surgical planning and minimises the need for multiple surgical procedures.


Subject(s)
COVID-19 , Mucormycosis , Humans , Mucormycosis/diagnostic imaging , COVID-19/diagnostic imaging , Tomography, X-Ray Computed/methods , Maxilla , Endoscopy
13.
Tunis Med ; 100(5): 374-383, 2022.
Article in English | MEDLINE | ID: covidwho-2058487

ABSTRACT

BACKGROUND: The analysis of the clinical and radiological characteristics of COVID-19 patients around the world observed a rich semiology, different from one country to another, and within the same country. AIM: To analyze the clinical, computed tomography (CT) features, and the outcome of patients suspected of COVID-19 hospitalized in a COVID-19 unit of Oran university hospital (Algeria). METHODS: We collected retrospectively the files of patients suspected of COVID-19 admitted in a COVID-19 unit during July 2020. Data were collected on standardized questionnaire with prior coding of parameters. Patients were admitted according to a triage based on their clinical situation and the chest CT aspects suggestive of COVID-19. Two physicians reviewed the high-resolution CT (HR-CT) images independently, and discrepancies were resolved by consensus with the input of two others experimented physicians. RESULTS: 112 patients (64% males, median age: 68 (18-88) years) were included. The main symptoms were dyspnea (51.7%), cough (34%), fatigue (14%). Almost the half (49.1%) of patients had hypoxemia. The HR-CT findings were typical of COVID-19 in 96% of patients. Although 61% of patients had favorable prognosis, mortality rate was 30%. Mutlivariate analysis of risk factors for death showed that patients aged > 60 years had a 4-fold risk of death (95% confidence interval: [1.27-12.58], p=0.018). CONCLUSION: Dyspnea, cough and fatigue were predominant symptoms, moderate and severe COVID-19 characterized our patients. Age > 60 years was a major risk factor for the deaths of our patients.


Subject(s)
COVID-19 , Aged , Algeria/epidemiology , COVID-19/diagnostic imaging , COVID-19/epidemiology , Cough , Dyspnea , Fatigue , Female , Hospitals, University , Humans , Male , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods
14.
Medicine (Baltimore) ; 101(39): e30744, 2022 Sep 30.
Article in English | MEDLINE | ID: covidwho-2051700

ABSTRACT

OBJECTIVE: The aim of this study was to compare the radiographic features of patients with progressive and nonprogressive coronavirus disease 2019 (COVID-19) pneumonia. METHODS: PubMed, Embase, and Cochrane Library databases were searched from January 1, 2020, to February 28, 2022, by using the keywords: "COVID-19", "novel Coronavirus", "2019-novel coronavirus", "CT", "radiology" and "imaging". We summarized the computed tomography manifestations of progressive and nonprogressive COVID-19 pneumonia. The meta-analysis was performed using the Stata statistical software version 16.0. RESULTS: A total of 10 studies with 1092 patients were included in this analysis. The findings of this meta-analysis indicated that the dominating computed tomography characteristics of progressive patients were a crazy-paving pattern (odds ratio [OR] = 2.10) and patchy shadowing (OR = 1.64). The dominating lesions distribution of progressive patients were bilateral (OR = 11.62), central mixed subpleural (OR = 1.37), and central (OR = 1.36). The other dominating lesions of progressive patients were pleura thickening (OR = 2.13), lymphadenopathy (OR = 1.74), vascular enlargement (OR = 1.39), air bronchogram (OR = 1.29), and pleural effusion (OR = 1.29). Two patterns of lesions showed significant links with the progression of disease: nodule (P = .001) and crazy-paving pattern (P = .023). Four lesions distribution showed significant links with the progression of disease: bilateral (P = .004), right upper lobe (P = .003), right middle lobe (P = .001), and left upper lobe (P = .018). CONCLUSION: Nodules, crazy-paving pattern, and/or new lesions in bilateral, upper and middle lobe of right lung, and lower lobe of left lung may indicate disease deterioration. Clinicians should formulate or modify treatment strategies in time according to these specific conditions.


Subject(s)
COVID-19 , Pneumonia , COVID-19/diagnostic imaging , Humans , Lung/diagnostic imaging , Lung/pathology , Pneumonia/pathology , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods
15.
Sci Rep ; 12(1): 16411, 2022 Sep 30.
Article in English | MEDLINE | ID: covidwho-2050534

ABSTRACT

The complex process of manual biomarker extraction from body composition analysis (BCA) has far restricted the analysis of SARS-CoV-2 outcomes to small patient cohorts and a limited number of tissue types. We investigate the association of two BCA-based biomarkers with the development of severe SARS-CoV-2 infections for 918 patients (354 female, 564 male) regarding disease severity and mortality (186 deceased). Multiple tissues, such as muscle, bone, or adipose tissue are used and acquired with a deep-learning-based, fully-automated BCA from computed tomography images of the chest. The BCA features and markers were univariately analyzed with a Shapiro-Wilk and two-sided Mann-Whitney-U test. In a multivariate approach, obtained markers were adjusted by a defined set of laboratory parameters promoted by other studies. Subsequently, the relationship between the markers and two endpoints, namely severity and mortality, was investigated with regard to statistical significance. The univariate approach showed that the muscle volume was significant for female (pseverity ≤ 0.001, pmortality ≤ 0.0001) and male patients (pseverity = 0.018, pmortality ≤ 0.0001) regarding the severity and mortality endpoints. For male patients, the intra- and intermuscular adipose tissue (IMAT) (p ≤ 0.0001), epicardial adipose tissue (EAT) (p ≤ 0.001) and pericardial adipose tissue (PAT) (p ≤ 0.0001) were significant regarding the severity outcome. With the mortality outcome, muscle (p ≤ 0.0001), IMAT (p ≤ 0.001), EAT (p = 0.011) and PAT (p = 0.003) remained significant. For female patients, bone (p ≤ 0.001), IMAT (p = 0.032) and PAT (p = 0.047) were significant in univariate analyses regarding the severity and bone (p = 0.005) regarding the mortality. Furthermore, the defined sarcopenia marker (p ≤ 0.0001, for female and male) was significant for both endpoints. The cardiac marker was significant for severity (pfemale = 0.014, pmale ≤ 0.0001) and for mortality (pfemale ≤ 0.0001, pmale ≤ 0.0001) endpoint for both genders. The multivariate logistic regression showed that the sarcopenia marker was significant (pseverity = 0.006, pmortality = 0.002) for both endpoints (ORseverity = 0.42, 95% CIseverity: 0.23-0.78, ORmortality = 0.34, 95% CImortality: 0.17-0.67). The cardiac marker showed significance (p = 0.018) only for the severity endpoint (OR = 1.42, 95% CI 1.06-1.90). The association between BCA-based sarcopenia and cardiac biomarkers and disease severity and mortality suggests that these biomarkers can contribute to the risk stratification of SARS-CoV-2 patients. Patients with a higher cardiac marker and a lower sarcopenia marker are at risk for a severe course or death. Whether those biomarkers hold similar importance for other pneumonia-related diseases requires further investigation.


Subject(s)
COVID-19 , Sarcopenia , Adipose Tissue/diagnostic imaging , Biomarkers , Body Composition , Female , Humans , Male , Retrospective Studies , SARS-CoV-2 , Sarcopenia/diagnostic imaging , Tomography, X-Ray Computed/methods
16.
Pediatr Radiol ; 52(10): 2017-2028, 2022 09.
Article in English | MEDLINE | ID: covidwho-2048213

ABSTRACT

In this review, we summarize early pulmonary complications related to cancer therapy in children and highlight characteristic findings on imaging that should be familiar to a radiologist reviewing imaging from pediatric cancer patients.


Subject(s)
Neoplasms , Tomography, X-Ray Computed , Child , Humans , Neoplasms/diagnostic imaging , Neoplasms/therapy , Tomography, X-Ray Computed/methods
17.
Tomography ; 8(5): 2403-2410, 2022 Sep 23.
Article in English | MEDLINE | ID: covidwho-2043963

ABSTRACT

On 27 February 2021, the Food and Drug Administration(FDA) authorized the administration of the adenovirus-based Ad26.COV2-S vaccine (J&J-Janssen) for the prevention of COVID-19, a viral pandemic that, to date, has killed more than 5.5 million people. Performed during the early phase of the COVID-19 4th wave, this retrospective observational study aims to report the computerized tomography (CT) findings and intensive care unit admission rates of Ad26.COV2-S-vaccinated vs. unvaccinated COVID-19 patients. From the 1st to the 23rd of December 2021, all confirmed COVID-19 patients that had been subjected to chest non-contrast CT scan analysis were enrolled in the study. These were divided into Ad26.COV2.S-vaccinated (group 1) and unvaccinated patients (group 2). The RSNA severity score was calculated for each patient and correlated to CT findings and type of admission to a healthcare setting after CT-i.e., home care, ordinary hospitalization, sub-intensive care, and intensive care. Descriptive and inference statistical analyses were performed by comparing the data from the two groups. Data from a total of 71 patients were collected: 10 patients in group 1 (4M, 6F, mean age 63.5 years, SD ± 4.2) and 61 patients in group 2 (32M, 29F, mean age 64.7 years, SD ± 3.7). Statistical analysis showed lower values of RSNA severity in group 1 compared to group 2 (mean value 14.1 vs. 15.7, p = 0.009, respectively). Furthermore, vaccinated patients were less frequently admitted to both sub-intensive and high-intensive care units than group 2, with an odds ratio of 0.45 [95%CI (0.01; 3.92)]. Ad26.COV2.S vaccination protects from severe COVID-19 based on CT severity scores. As a result, Ad26.COV2.S-vaccinated COVID-19 patients are more frequently admitted to home in comparison with unvaccinated patients.


Subject(s)
COVID-19 , Humans , Middle Aged , COVID-19/prevention & control , Ad26COVS1 , Tomography, X-Ray Computed/methods , Vaccination , Critical Care
18.
Sensors (Basel) ; 22(19)2022 Sep 26.
Article in English | MEDLINE | ID: covidwho-2043924

ABSTRACT

Since the beginning of the COVID-19 pandemic, many works have been published proposing solutions to the problems that arose in this scenario. In this vein, one of the topics that attracted the most attention is the development of computer-based strategies to detect COVID-19 from thoracic medical imaging, such as chest X-ray (CXR) and computerized tomography scan (CT scan). By searching for works already published on this theme, we can easily find thousands of them. This is partly explained by the fact that the most severe worldwide pandemic emerged amid the technological advances recently achieved, and also considering the technical facilities to deal with the large amount of data produced in this context. Even though several of these works describe important advances, we cannot overlook the fact that others only use well-known methods and techniques without a more relevant and critical contribution. Hence, differentiating the works with the most relevant contributions is not a trivial task. The number of citations obtained by a paper is probably the most straightforward and intuitive way to verify its impact on the research community. Aiming to help researchers in this scenario, we present a review of the top-100 most cited papers in this field of investigation according to the Google Scholar search engine. We evaluate the distribution of the top-100 papers taking into account some important aspects, such as the type of medical imaging explored, learning settings, segmentation strategy, explainable artificial intelligence (XAI), and finally, the dataset and code availability.


Subject(s)
COVID-19 , Artificial Intelligence , COVID-19/diagnostic imaging , Humans , Pandemics , SARS-CoV-2 , Tomography, X-Ray Computed/methods , X-Rays
19.
Acta Biomed ; 93(S1): e2022270, 2022 Sep 22.
Article in English | MEDLINE | ID: covidwho-2040601

ABSTRACT

A 62-year-old man with COVID-19 had PS for fever, coughing, and breathlessness. Two days after therapy, the patient's clinical condition worsened. X-ray and CT showed pneumomediastinum, emphysema and pneumothorax. The patient was intubated and subjected to conservative therapy. The patient was discharged after about 20 days. Radiological imaging plays a key role in the proper diagnosis and treatment of COVID-19 patients with related complications.


Subject(s)
COVID-19 , Mediastinal Emphysema , Pneumothorax , COVID-19/complications , Humans , Male , Mediastinal Emphysema/diagnostic imaging , Mediastinal Emphysema/etiology , Mediastinal Emphysema/therapy , Middle Aged , Pneumothorax/etiology , Pneumothorax/therapy , Tomography, X-Ray Computed/methods
20.
Med Biol Eng Comput ; 60(11): 3203-3215, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2035256

ABSTRACT

Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and treatment. Identifying lesions in medical images is costly and time-consuming and requires highly specialized knowledge. For this reason, supervised and semi-supervised learning techniques have been developed. Nevertheless, the lack of annotated data, which is common in medical imaging, is an issue; in this context, interesting approaches can use unsupervised learning to accurately distinguish between healthy tissues and lesions, training the network without using the annotations. In this work, an unsupervised learning technique is proposed to automatically segment coronavirus disease 2019 (COVID-19) lesions on 2D axial CT lung slices. The proposed approach uses the technique of image translation to generate healthy lung images based on the infected lung image without the need for lesion annotations. Attention masks are used to improve the quality of the segmentation further. Experiments showed the capability of the proposed approaches to segment the lesions, and it outperforms a range of unsupervised lesion detection approaches. The average reported results for the test dataset based on the metrics: Dice Score, Sensitivity, Specificity, Structure Measure, Enhanced-Alignment Measure, and Mean Absolute Error are 0.695, 0.694, 0.961, 0.791, 0.875, and 0.082 respectively. The achieved results are promising compared with the state-of-the-art and could constitute a valuable tool for future developments.


Subject(s)
COVID-19 , COVID-19/diagnostic imaging , Humans , Image Processing, Computer-Assisted/methods , Lung/diagnostic imaging , Radionuclide Imaging , Thorax , Tomography, X-Ray Computed/methods
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