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The association of lung ultrasound images with COVID‐19 infection in an emergency room cohort



Lung ultrasound could facilitate the triage of patients with suspected COVID‐19 infection admitted to the emergency room. We developed a predictive model for COVID‐19 diagnosis based on lung ultrasound and clinical features. We used ultrasound to image the lung bilaterally at two anterior sites, one and two hands below each clavicle, and a posterolateral site that was the posterior transverse continuation from the lower anterior site. We studied 100 patients, 31 of whom had a COVID‐19 positive reverse transcriptase polymerase chain reaction. A positive test was independently associated with: quick sequential organ failure assessment score ≥ 1; ≥ 3 B‐lines at the upper site; consolidation and thickened pleura at the lower site; and thickened pleura line at the posterolateral site. The model discrimination was an area (95%CI) under the receiver operating characteristic curve of 0.82 (0.75‐0.90). The characteristics (95%CI) of the model’s diagnostic threshold, applied to the population from which it was derived, were: sensitivity, 97% (83–100%); specificity, 62% (50–74%); positive predictive value, 54% (41–98%); and negative predictive value, 98% (88–99%). This model may facilitate triage of patients with suspected COVID‐19 infection admitted to the emergency room.
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The association of lung ultrasound images with COVID-19 infection in an emergency room cohort
S. Bar,1 A. Lecourtois,2 M. Diouf,3 E. Goldberg,4 C. Bourbon, 5 E. Arnaud,5 L. Domisse,5 H. Dupont6 and P.
1 Anaesthetist, 4 Resident, 6 Professor, Anaesthesiology and Critical Care Department, 2 Resident, 5 Doctor
of Medicine, Emergency Medicine Department, 3 Biostatistician, Amiens University Hospital, Amiens,
Keywords: lung ultrasound, COVID-19, triage.
Short title: Lung ultrasound to diagnose COVID-19
Correspondence to: S. Bar
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Lung ultrasound could facilitate the triage of patients with suspected COVID-19 infection admitted to the
emergency room. We developed a predictive model for COVID-19 diagnosis based on lung ultrasound and
clinical features. We used ultrasound to image the lung bilaterally at two anterior sites, one and two
hands below each clavicle, and a posterolateral site that was the posterior transverse continuation from
the lower anterior site. We studied 100 patients, 31 of whom had a COVID-19 positive reverse
transcriptase polymerase chain reaction. A positive test was independently associated with: quick
sequential organ failure assessment score 1; 3 B-lines at the upper site; consolidation and thickened
pleura at the lower site; and thickened pleura line at the posterolateral site. The model discrimination was
an area (95%CI) under the receiver operating characteristic curve of 0.82 (0.75-0.90). The characteristics
(95%CI) of the model’s diagnostic threshold, applied to the population from which it was derived, were:
sensitivity, 97% (83–100%); specificity, 62% (50–74%); positive predictive value, 54% (41–98%); and
negative predictive value, 98% (88–99%). This model may facilitate triage of patients with suspected
COVID-19 infection admitted to the emergency room.
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The current coronavirus disease 2019 (COVID-19) pandemic is caused by the severe acute respiratory
syndrome coronavirus 2 strain (SARS-CoV-2), with many patients admitted to the emergency room with
suspected COVID-19 infection [1]. The results of RNA reverse transcriptase polymerase chain reaction (RT-
CR) diagnostic oropharyngeal swabs may be unavailable for 48 h after collection [2]. This delay can lead to
unnecessary isolation of many patients that may exceed a hospital’s resources.
Lung ultrasound can contribute to the diagnosis of acute respiratory failure in the emergency
room, for instance the ‘bedside lung ultrasound in emergency’ (BLUE) protocol [3]. Lung ultrasonographic
characteristics of COVID-19 disease have been recently described [4]. Lung ultrasound could facilitate
rapid, simple and reliable triage of patients with suspected COVID-19 infection admitted to the emergency
room and may inform prognosis [5].
We aimed to develop a model for COVID-19 diagnosis in patients presenting to the emergency
room with possible infection, based on the association of lung ultrasound and clinical features with
positive viral swabs. Our secondary objectives were to study the associations between these and
admission to the intensive care unit, respiratory complications, and mortality.
The University Medical Centre review board approved this prospectively registered observational study,
which we conducted from March to April 2020 and that we report as standard [6]. Participants gave
informed consent. We studied adults admitted to the emergency room whose lungs were imaged with
ultrasound by the emergency physician for suspected COVID-19 infection as part of the BLUE protocol and
who had a SARS-CoV-2 RT-PCR test [3].
We did not study pregnant women or patients unable to give informed consent or patients with
suspected or proven chronic interstitial lung disease. We did not analyse patients whose ultrasound scans
were poor due to an acoustic barrier, for instance pneumothorax or subcutaneous emphysema.
We used a convex array transducer and ultrasound system (C5-2s™ and TE7, Mindray™;
Shenzhen, China) to identify abnormality consistent with possible COVID-19 disease: thickening of the
pleural line with irregularity; B-lines in a variety of patterns, including focal, multifocal, and confluent; and
consolidation in a variety of patterns (Fig. 1a) [4, 7, 8]. The ‘bedside lung ultrasound in emergency’ (BLUE)
protocol interrogates the lung bilaterally at upper and lower anterior sites and at a posterolateral site (Fig.
1b) [9]. We only counted the number of B-lines at the upper and lower sites as they are present in around
25% of healthy subjects elsewhere [8, 10]. The number of B-lines was counted in a short-axis scan
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between two ribs. Two experts who had performed at least 50 lung ultrasound scans interpreted stored
images, unaware of patients’ SARS-CoV-2 RT-PCR status (SB, PG) [11].
We recorded patients’ characteristics, including age, sex, BMI, medical history and medications.
We also recorded heart rate, mean arterial pressure and pulsed oxygen saturation. We calculated the
quick sequential organ failure assessment (qSOFA) score and the Glasgow coma scale, respiratory rate and
systolic arterial pressure [12]. We measured lymphocyte count, C-reactive protein and the ratio of arterial
oxygen partial pressure to inspired fraction oxygen (PaO2/FIO2). The primary outcome was the SARS-CoV-2
RT-PCR result, which we defined as negative if COVID-19 was not detected by two RT-PCRs [13]. The
secondary outcomes were admission to intensive care, respiratory complications (acute respiratory
distress syndrome (ARDS), pulmonary embolism and secondary bacterial infection), and mortality 14 days
after inclusion, recorded by one investigator (AL) who was not informed of lung ultrasound results or
COVID-19 status.
We calculated that we would need 100 patients to have a 80% power to demonstrate an
ultrasound diagnostic accuracy of 18% and 12% for sensitivity and specificity, assuming their true values
to be 50%, at an alpha threshold of 5%, if 30% of patients presenting to the emergency room with
suspected COVID19 had SARS-CoV-2 detected by up to two RT-PCR. We used the Agostino-Pearson test
for normality of data distribution. We used Student's t-test, Mann-Whitney test, chi-square test or Fisher
test, as appropriate. We used logistic regression to model the associations of lung ultrasound and clinical
features with admission to intensive care, respiratory complications and mortality. We constructed a
multivariate logistic model with ultrasound variables that associated with outcome (p < 0.05), constrained
by elastic net penalisation, with the L2 ridge parameter α set to 0.9 and the optimal L1 Lasso parameter λ
determined by 10-fold cross-validation [14, 15]. In this multivariate logistic model, we categorised the
number of B-lines 3 and < 3 in accordance with the international definition of interstitial syndrome [7].
We used the area under the receiver operating characteristic curve and the highest Youden index to
define model discrimination and diagnostic threshold, respectively. Interobserver agreement between the
two experts concerning qualitative ultrasound signs (signs present or absent) was evaluated using a Kappa
concordance coefficient and agreement on quantitative evaluations (number of B-lines detected at the
upper and lower sites) using an intraclass correlation coefficient. We used R for analyses (Core Team®,
2017, Vienna, Austria).
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We included 100 adults of whom 31 had a positive SARS-CoV-2 RT-PCR (Table 1 and Fig. 2). The qSOFA
score (≥ 1) and four ultrasound signs were independently associated with a positive test (Table 2). The
area (95%CI) under the receiver operating characteristic curve for the multivariate equation was 0.82
(0.75-0.90). The optimal model value for diagnosis was -1.35, recursively characterised (95%CI) in the
derivation population by: sensitivity, 97% (83-100%); specificity, 62% (50-74%); positive predictive value,
54% (41-98%); and negative predictive value, 98% (88-99%).
Nine patients (29%) with a positive SARS-CoV-2 RT-PCR developed ARDS, 6 (19%) developed a
secondary bacterial infection and none developed a pulmonary embolism. The odds ratio (95%CI) for
subsequent ARDS in patients with COVID-19 was independently associated with three variables: > 3 upper
site B-lines, 1.7 (1.3-2.3), p = 0.001; > 3 lower site B-lines, 1.0 (1.0-1.1), p = 0.03; and PaO2/FIO2 ratio, 1.00
(1.00-1.01), p = 0.006. The same variables were associated with admission to the ICU, OR (95%CI): 1.6
(1.2-2.1), p = 0.003; 1.0 (1.0-1.1), p = 0.016; and 1.00 (1.00-1.01], p = 0.02, respectively. No associations
were found between other respiratory complications and lung ultrasound variables.
Six patients (19 %) with a positive SARS-CoV-2 RT-PCR died during the study period. Mortality was
not associated with lung ultrasound variables. The interobserver agreement was good, with a Kappa
concordance coefficient of 0.89 (95% CI [0.67-1.00]). The intraclass correlation coefficient for the
agreement on quantitative evaluations was 0.92 (95% CI [0.81-0.97]).
We found that a combination of clinical features and lung ultrasound signs were independently associated
with positive SARS-CoV-2 RT-PCR. Subsequent development of adult respiratory distress syndrome and
ICU admission were also associated with lung ultrasound signs.
Chest computed tomography (CT) imaging has been strongly recommended because it is very
sensitive for detecting early disease [16]. The early stages of COVID-19 infection are characterised by
bilateral ground glass opacification, accompanied by interlobular thickening and consolidation,
predominantly in the peripheral and subpleural middle and lower lobes [17, 18]. However, the
transportation of potentially infectious and unstable patients for CT limits its use [4].
Lung ultrasound has several advantages compared with CT. It is non-irradiating and noninvasive. It
can be learned quickly and its use in the emergency room has generated great interest [7, 19–23]. The
decontamination of the equipment is straightforward [24]. The abnormalities observed on CT are
accompanied by ultrasound signs, which include B-lines that become more extensive with disease
progression, accompanied by pleural thickening and subpleural consolidation [25, 26].
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The signs associated with COVID-19 diagnosis in our model are consistent with other studies [4,
25]. The most common sign was thickening of the pleural line in the inferior and posterolateral sites,
which is indicative of pneumonia or ARDS [7]. Consolidation is common to bacterial pneumonitis and did
not independently associate with COVID-19 diagnosis [7]. Occasional B-lines may indicate chronic changes
and are common to a number of diseases, but at least three lines indicate interstitial syndrome and
greater numbers are associated with disease severity and higher mortality [4, 5, 7, 8, 10, 26–28]. The
qSOFA score is a simplified version of the SOFA score that aims to identify patients more likely to suffer
serious outcomes after infection [12]. Few patients with COVID-19 are haemodynamically compromised,
which may limit its utility [29, 30].
Our study had several limitations. The first was the interpretation of B-lines in the upper chest, as
a diffuse and heterogeneous distribution of B-lines with thickening of the pleura also occurs with chronic
interstitial lung disease, whom we excluded from the study [31]. Lung ultrasound can distinguish between
cardiogenic and non-cardiogenic pulmonary oedema, particularly through careful examination of the
pleura [32, 33]. Lung ultrasound signs are not particularly specific for infections, although the bilateral
distribution of changes in COVID-19 can help differentiate it from influenza and bacterial pneumonias [7,
34, 35]. The performance of our model will be limited in part by the sensitivity of the SARS-CoV-2 RT-PCR
test, which misdiagnoses one quarter of COVID-19 patients as negative, a rate that we tried to reduce by
performing two tests on each patient. It is possible that the performance of a model might be improved
by imaging with ultrasound more lung areas, but any improvement might not justify the additional time
[3, 5, 31, 36].
In conclusion, the association of BLUE protocol lung ultrasound signs and qSOFA with COVID-19
diagnosis could facilitate more effective triage of patients presenting to emergency departments with
suspected COVID-19 infection. This model should be tested on an independent cohort.
We retrospectively registered this study, 29 April 2020 (, NCT04368338). No competing
interests declared.
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Table 1 Characteristics of 100 patients presenting to the emergency room with possible COVID-19
infection. Values are mean (SD), numbers (proportion) or median (IQR [range]).
Positive (n = 31)
Negative (n = 69)
p value
Age; y
66.8 (16.3)
68.7 (16.4)
20 (65%)
39 (56%)
BMI; kg.m-2
30.0 (3.19)
26.4 (3.98)
Medical history
High blood pressure
21 (68%)
36 (52%)
Coronary heart disease
2 (6%)
13 (19%)
2 (6%)
21 (30%)
Peripheral arterial disease
2 (6%)
4 (6%)
7 (23%)
9 (13%)
3 (10%)
7 (10%)
10 (32%)
21 (30%)
ACE inhibitor
5 (16%)
11 (16%)
Angiotensin receptor blocker
8 (26%)
10 (15%)
1 (1%)
qSOFA score
1 (0-1 [0-2])
0 (0-1 [0-1])
Heart rate; min-1
97 (80-115 [70-127])
88 (80-105 [67-134])
Mean arterial pressure; mmHg
96.0 (12.9)
97.5 (17.4)
Oxygen saturation; %
95 (93-98 [85-100])
97 (93-99 [82-100])
Lymphocyte count; 109.l-1
1.5 (1.0-2.1 [0.6-3.6])
2.0 (1.8-2.2 [0.6-3.9])
C-reactive protein; mg.l-1
118 (71-151 [14-327])
42 (12-125 [0-29])
298 (119)
338 (105)
Chest ultrasound sites
Upper and lower anterior
B lines
6 (2-10 [0-30])
3 (1-7 [0-16])
Confluent B-lines
3 (10%)
Thickened pleural line
24 (77%)
26 (38%)
< 0.001
17 (54%)
11 (16%)
< 0.001
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Confluent B-lines
10 (32%)
8 (12%)
Thickened pleural line
24 (77%)
26 (38%)
< 0.001
18 (58%)
23 (33)
ACE, angiotensin-converting-enzyme; BLUE, bedside lung ultrasound in emergency; NSAID, nonsteroidal
anti-inflammatory drug; PaO2/FiO2, arterial oxygen partial pressure to fractional inspired oxygen; qSOFA,
quick sequential organ failure assessment; SARS-CoV RT-PCR, severe acute respiratory syndrome
coronavirus 2 reverse transcription polymerase chain reaction
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Table 2 Lung ultrasound characteristics and qSOFA score independently associated with COVID-19.
Odds ratio (95%CI)
1.05 (1.01-1.10)
1.52 (1.31-1.79)
1.73 (1.49-1.98)
2.39 (2.07-2.69)
1.97 (1.72-2.22)
qSOFA, quick sequential organ failure assessment
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Figure 1a Lower anterior chest subpleural consolidation associated with thickened pleura.
Figure 1b The ‘bedside lung ultrasound in emergency’ (BLUE) protocol interrogates three points in each
hemithorax. The two anterior sites are under one (upper) and two (lower) hands placed below each
clavicle. The posterolateral site is the posterior transverse continuation from the lower anterior site,
interrogated as posterior as possible in the supine patient.
Figure 2 Study flow chart detailing complications in case of positive RT-PCR and other aetiologies in case
of negative RT-PCR.
ARDS, acute respiratory distress syndrome; ER, emergency room; ICU, intensive care unit; RT-PCR, reverse
transcription polymerase chain reaction.
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... The B lines depict the interstitial involvement and represent the most common ultrasonographic pattern found in patients with COVID-19 [129,132,135]. Despite having a high sensitivity in detecting COVID-19 pneumonia in subpleural lung regions, the deep pulmonary parenchyma remains inaccessible to LUS due to air interposition leading to an underestimation of the disease extent [136]. LUS findings are not distinctive for viral cases of pneumonia, but just as it was previously discussed in the case of chest radiography and chest CT scans, the bilateral and predominantly basal distribution is a strong indicator for COVID-19 pneumonia rather than influenza or bacterial pneumonia [137]. ...
... LUS findings are not distinctive for viral cases of pneumonia, but just as it was previously discussed in the case of chest radiography and chest CT scans, the bilateral and predominantly basal distribution is a strong indicator for COVID-19 pneumonia rather than influenza or bacterial pneumonia [137]. Nonetheless, LUS has a low specificity, since it cannot distinguish from other pulmonary and cardiac conditions such as acute respiratory distress syndrome, heart failure and subpleural lung masses [125,136]. ...
Full-text available
Coronavirus disease 2019 (COVID-19), caused by a severe acute respiratory syndrome coronavirus 2 infection, has raised serious concerns worldwide over the past 3 years. The severity and clinical course of COVID-19 depends on many factors (e.g., associated comorbidities, age, etc) and may have various clinical and imaging findings, which raises management concerns. Gut microbiota composition is known to influence respiratory disease, and respiratory viral infection can also influence gut microbiota. Gut and lung microbiota and their relationship (gut-lung axis) can act as modulators of inflammation. Modulating the intestinal microbiota, by improving its composition and diversity through nutraceutical agents, can have a positive impact in the prophylaxis/treatment of COVID-19.
... At the same time, the use of LUS, due to its advantages, is rapidly increasing both in the ED and in the inpatient setting for early triage and follow-up of suspected and confirmed COVID-19 patients. [16][17][18][19][20][21] However, more than 2 years after the first identified case of SARS-CoV-2, 21 the diagnostic accuracy of each imaging technique and the relationship between LUS and chest HRCT is debated. The most recent Cochrane review on thoracic imaging in COVID-19 6 reports high sensitivity and low/moderate specificity for both LUS and HRCT. ...
Full-text available
Objective To analyze the diagnostic accuracy of lung ultrasonography (LUS) and high-resolution computed tomography (HRCT), to detect COVID-19. Materials and Methods This study recruited all patients admitted to the emergency medicine unit, due to a suspected COVID-19 infection, during the first wave of the COVID-19 pandemic. These patients also who underwent a standardized LUS examination and a chest HRCT. The signs detected by both LUS and HRCT were reported, as well as the sensitivity, specificity, positive predictive value, and negative predictive value for LUS and HRCT. Results This cohort included 159 patients, 101 (63%) were diagnosed with COVID-19. COVID-19 patients showed more often confluent subpleural consolidations and parenchymal consolidations in lower lung regions of LUS. They also had “ground glass” opacities and “crazy paving” on HRCT, while pleural effusion and pulmonary consolidations were more common in non-COVID-19 patients. LUS had a sensitivity of 0.97 (95% CI 0.92–0.99) and a specificity of 0.24 (95% CI 0.07–0.5) for COVID-19 lung infections. HRCT abnormalities resulted in a 0.98 sensitivity (95% CI 0.92–0.99) and 0.1 specificity (95% CI 0.04–0.23) for COVID-19 lung infections. Conclusion In this cohort, LUS proved to be a noninvasive, diagnostic tool with high sensitivity for lung abnormalities that were likewise detected by HRCT. Furthermore, LUS, despite its lower specificity, has a high sensitivity for COVID-19, which could prove to be as effective as HRCT in excluding a COVID-19 lung infection.
... Caution must be held with POCUS, however, given its low specificity. [40][41][42] ...
With a disproportionately high burden of global morbidity and mortality caused by chronic respiratory diseases (CRDs) in low and middle-income countries (LMICs), access to radiological services is of critical importance for screening, diagnosis, and treatment guidance.
... These retrospective studies were conducted in emergency departments and included patients with severe and critical COVID-19 infection. [19][20][21] Some studies included mild patients who were evaluated in the ED and sometimes hospitalised. [22][23][24] Although these patients had mild COVID-19, their disease was more severe as they needed a medical assessment unlike the patients included in the present study who came for SARS-CoV-2 screening. ...
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Objectives Early identification of SARS-CoV-2 infection is important to guide quarantine and reduce transmission. This study evaluates the diagnostic performance of lung ultrasound (LUS), an affordable, consumable-free point-of-care tool, for COVID-19 screening. Design, setting and participants This prospective observational cohort included adults presenting with cough and/or dyspnoea at a SARS-CoV-2 screening centre of Lausanne University Hospital between 31 March and 8 May 2020. Interventions Investigators recorded standardised LUS images and videos in 10 lung zones per patient. Two blinded independent experts reviewed LUS recording and classified abnormal findings according to prespecified criteria to investigate their predictive value to diagnose SARS-CoV-2 infection according to PCR on nasopharyngeal swabs (COVID-19 positive vs COVID-19 negative). Primary and secondary outcome measures We finally combined LUS and clinical findings to derive a multivariate logistic regression diagnostic score. Results Of 134 included patients, 23% (n=30/134) were COVID-19 positive and 77% (n=103/134) were COVID-19 negative; 85%, (n=114/134) cases were previously healthy healthcare workers presenting within 2–5 days of symptom onset (IQR). Abnormal LUS findings were significantly more frequent in COVID-19 positive compared with COVID-19 negative (45% vs 26%, p=0.045) and mostly consisted of focal pathologic B lines. Combining clinical findings in a multivariate logistic regression score had an area under the receiver operating curve of 80.3% to detect COVID-19, and slightly improved to 84.5% with the addition of LUS features. Conclusions COVID-19-positive patients are significantly more likely to have lung pathology by LUS. However, LUS has an insufficient sensitivity and is not an appropriate screening tool in outpatients. LUS only adds little value to clinical features alone.
... Another limitation was the inability to calculate score or to fit model for all articles from our previous systematic review. Indeed, more than 20% information like known exposure, Visual Analog Scale pain, Sequential Organ Failure Assessment or ethnicity required in [48][49][50][51][52][53] was not recorded in the database. Moreover, the modelling approach used in [54] could not be reproduced. ...
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Background Since the beginning of the pandemic, hospitals have been constantly overcrowded, with several observed waves of infected cases and hospitalisations. To avoid as much as possible this situation, efficient tools to facilitate the diagnosis of COVID-19 are needed. Objective To evaluate and compare prediction models to diagnose COVID-19 identified in a systematic review published recently using performance indicators such as discrimination and calibration measures. Methods A total of 1618 adult patients present at two Emergency Department triage centers and for whom qRT-PCR tests had been performed were included in this study. Six previously published models were reconstructed and assessed using diagnostic tests as sensitivity (Se) and negative predictive value (NPV), discrimination (Area Under the Roc Curve (AUROC)) and calibration measures. Agreement was also measured between them using Kappa’s coefficient and IntraClass Correlation Coefficient (ICC). A sensitivity analysis has been conducted by waves of patients. Results Among the 6 selected models, those based only on symptoms and/or risk exposure were found to be less efficient than those based on biological parameters and/or radiological examination with smallest AUROC values (< 0.80). However, all models showed good calibration and values above > 0.75 for Se and NPV but poor agreement (Kappa and ICC < 0.5) between them. The results of the first wave were similar to those of the second wave. Conclusion Although quite acceptable and similar results were found between all models, the importance of radiological examination was also emphasized, making it difficult to find an appropriate triage system to classify patients at risk for COVID-19.
INTRODUCTION. Lung ultrasound has been widely used to diagnose bacterial pneumonia, pulmonary congestion in heart failure, pneumo- and hydrothorax, and other pathological conditions. With the onset of the COVID-19 pandemic, the need for various methods of lung imaging has dramatically increased. AIM. To estimate the value of lung ultrasound in the diagnosis and severity assessment of COVID-19. MATERIAL AND METHODS. Patients with a positive PCR test result for SARS-Cov-2 were included and divided into a moderate, severe, or critical severe group. There were 30 patients involved. All the patients underwent clinical assessment, complete blood count, biochemical blood profile, pulse oxymetry, bedside lung ultrasound, and chest CT scan. To evaluate LUS findings thorax into 16 lung regions and each of these areas was quantitatively scored and summed up in total LUS score. A total severity score for chest CT was compared with the total LUS score. RESULT AND DISCUSSION. All patients had positive LUS findings, as well as positive CT findings. Most common lung ultrasound findings were the following: subpleural consolidations (100.0%, 30/30), large consolidations (73.3%, 22/30) and an irregular pleural line (43.3%, 13/30). A higher total lung ultrasound score was seen in patients with a more severe course of illness and a larger number of lung areas affected; they were older and had a significantly higher incidence of comorbidities and lower SpO2. Spearman correlation coefficient revealed a statistically significant correlation between CT chest scan and lung ultrasound – rs=.52, p= 0.003011. CONCLUSION. During the COVID-19 pandemic, lung ultrasound is a great tool for assessing lung tissue in patients with varying severity of the disease. The advantages of the method are accessibility, ease of implementation and the absence of radiation exposure.
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Tuberculosis is a major health threat in many regions of the world. Opportunistic infections in immunocompromised HIV/AIDS patients and multi-drugresistant bacterial strains have exacerbated the problem, while diagnosing tuberculosis remains challenging. Medical images have made a high impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. This chapter presents a novel X-ray of lungs segmentation method using the U-net model. First, we construct the U-net which combine the lungs and mask. Then, we convert to problem of positive and negative TB lungs into the segmentation of lungs, and extract the lungs by subtracting the chest from the radiography. In experiment, the proposed model achieves 97.62% on the public dataset of collection by Shenzhen Hospital, China and Montgomery County X-ray Set.
The SARS-CoV-2 (severe acute respiratory syndrome coronavirus) pandemic, also known as COVID-19 (coronavirus 2019), impacted humanity worldwide and significantly impacted the healthcare community. COVID-19 infection and transmission have resulted in several international issues, including health hazards. Sore throat, trouble breathing, cough, fever, weariness, and other clinical signs have been described. In SARS-CoV-2 patients, the most common infections are in the lungs and the gastric intestine. Lung infections may be caused by viral or bacterial infections, physical trauma, or inhalation of harmful particles. This research presents deep learning-based approaches for COVID-19 infection detection based on radiological images, prevention and therapy based on benchmark publicly available datasets. Finally, the analysis and findings explore evidence-based methodologies and modalities, leading to a conclusion and possible future healthcare planning.KeywordsLung infectionCOVID-19Deep learning techniquesRadiologyRadiographyTreatmentsWHOComputed tomographyUltrasound
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Melanoma is one of the riskiest diseases that extensively influence the quality of life and can be dangerous or even fatal. Skin lesion classification methods faced challenges in varying scenarios. The available hand-crafted features could not generate better results when the skin lesion images contain low contrast, under and over-segmented images. The hand-crafted features for skin lesions did not discriminate well between the two significantly different densities. The pigmented network feature vector and deep feature vector have been fused using a parallel fusion method to increase classification accuracy. This optimized fused feature vector has been fed to machine learning classifiers that accurately classify the dermoscopic images into two categories as benign and malignant melanoma. The statistical performance measures were used to assess the proposed fused feature vector on three skin lesion datasets (ISBI 2016, ISIC 2017, and PH2). The proposed fused feature vector accurately classified the skin lesion with the highest accuracy of 99.8% for the ISBI 2016.
超音波装置の小型化とベッドサイドへの普及により,超音波検査は救急現場で積極的に利用されるようになった。医療従事者がベッドサイドで観察範囲を絞り,臨床決断と侵襲的手技の質向上のために実施する超音波検査はpoint–of–care ultrasonography(POCUS)と呼ばれる。その概念は世界中で広く共有されるようになったが,本邦ではfocused assessment with sonography for trauma(FAST)と超音波ガイド下中心静脈穿刺を除き,POCUSに関する正式な指針はこれまで存在しなかった。日本救急医学会Point–of–Care超音波推進委員会では,POCUSを用いた救急診療の質向上について議論を繰り返し,日本救急医学会からの認証を得て救急point–of–care超音波診療指針としてまとめた。この指針では,背景,救急科専門医の到達目標,その論文的根拠,領域横断的な活用について述べる。到達目標の主要項目には,超音波の基礎,上気道,胸部,心臓,腹部,深部静脈,ガイド下手技,症候別評価が含まれる。また将来主要項目になる可能性があるものは付加項目として広く言及した。この指針は救急科専門医にとっての超音波検査の概要と方向性を示すものであり,救急超音波教育のために利用できる。この指針をきっかけに,本邦の救急診療の現場で超音波検査が効果的に利用されることを願う。 Owing to the miniaturization of diagnostic ultrasound scanners and their spread of their bedside use, ultrasonography has been actively utilized in emergency situations. Ultrasonography performed by medical personnel with focused approaches at the bedside for clinical decision–making and improving the quality of invasive procedures is now called point–of–care ultrasonography (POCUS). The concept of POCUS has spread worldwide; however, in Japan, formal clinical guidance concerning POCUS is lacking, except for the application of focused assessment with sonography for trauma (FAST) and ultrasound–guided central venous cannulation. The Committee for the Promotion of Point–of–Care Ultrasonography in the Japanese Association for Acute Medicine (JAAM) has often discussed improving the quality of acute care using POCUS, and the “Clinical Guidance for Emergency and Point–of–Care Ultrasonography” was finally established with the endorsement of JAAM. The background, targets for acute care physicians, rationale based on published articles, and integrated application were mentioned in this guidance. The core points include the fundamental principles of ultrasound, upper airway, chest, cardiac, abdominal, and deep venous ultrasound, ultrasound–guided procedures, and the usage of ultrasound based on symptoms. Additional points, which are currently being considered as potential core points in the future, have also been widely mentioned. This guidance describes the overview and future direction of ultrasonography for acute care physicians and can be utilized for emergency ultrasound education. We hope this guidance will contribute to the effective use of ultrasonography in acute care settings in Japan.
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Lung ultrasound examination has been demonstrated to be an accurate imaging method to detect pulmonary and pleural conditions. During pregnancy, there is a need for a rapid assessment of the maternal lung in patients suspected to have COVID-19. We report our experience on lung ultrasound examination in the diagnosis of Sars-Cov-2 pneumonia in a pregnant woman. Typical ultrasound features of this pulmonary pathology, including diffuse hyperechoic vertical artifacts with thickened pleural line and “white lung” with patchy distribution, were observed. We suggest point of care lung ultrasound examination as a diagnostic imaging tool in pregnant women with suspected COVID-19.
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The pandemic of COVID-19 is seriously challenging the medical organization in many parts of the world. This novel corona virus SARS-CoV-2 has a specific tropism for the low respiratory airways, but causes severe pneumonia in a low percentage of patients. However, the rapid spread of the infection during this pandemic is causing the need to hospitalize a high number of patients. Pneumonia in COVID-19 has peculiar features and can be studied by lung ultrasound in the early approach to suspected patients. The sonographic signs are non-specific when considered alone, but observation of some aspects of vertical artifacts can enhance the diagnostic power of the ultrasound examination. Also, the combination of sonographic signs in patterns and their correlation with blood exams in different phenotypes of the disease may allow for a reliable characterization and be of help in triaging and admitting patients.
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Almost the entire world, not only China, is currently experiencing the outbreak of a novel coronavirus that causes respiratory disease, severe pneumonia, and even death. The outbreak began in Wuhan, China, in December of 2019 and is currently still ongoing. This novel coronavirus is highly contagious and has resulted in a continuously increasing number of infections and deaths that have already surpassed the SARS-CoV outbreak that occurred in China between 2002 and 2003. It is now officially a pandemic, announced by WHO on the 11th of March. Currently, the 2019 novel coronavirus (SARS-CoV-2) can be identified by virus isolation or viral nucleic acid detection; however, false negatives associated with the nucleic acid detection provide a clinical challenge and thus make the imaging examination crucial. Imaging exams have been a main clinical diagnostic criteria for the 2019 novel coronavirus disease (COVID-19) in China. Imaging features of multiple patchy areas of ground glass opacity and consolidation predominately in the periphery of the lungs are characteristic manifestations on chest CT and extremely helpful in the early detection and diagnosis of this disease, which aids prompt diagnosis and the eventual control of this emerging global health emergency. Key Points • In December 2019, China, an outbreak of pneumonia caused by a novel, highly contagious coronavirus raised grave concerns and posed a huge threat to global public health. • Among the infected patients, characteristic findings on CT imaging include multiple, patchy, ground-glass opacity, crazy-paving pattern, and consolidation shadows, mainly distributed in the peripheral and subpleural areas of both lungs, which are very helpful for the frontline clinicians. • Imaging examination has become the indispensable means not only in the early detection and diagnosis but also in monitoring the clinical course, evaluating the disease severity, and may be presented as an important warning signal preceding the negative RT-PCR test results.
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Aims Patients with COVID‐19 may present with respiratory syndromes indistinguishable from common viruses. This poses a challenge for early detection during triage at the emergency department (ED). Over a 3‐month period, our ED aimed to minimise nosocomial transmission by using broader suspect case criteria for better detection and using appropriate personal protective equipment (PPE) for healthcare workers (HCWs) Methods All ED admissions with respiratory syndromes over a 3‐month period were tested for COVID‐19. The sensitivity and specificity of screening criteria in detecting COVID‐19 was assessed. A risk‐stratified approach was adopted for PPE usage in the ED, based on high‐risk “fever areas” and lower‐risk zones. When a case of COVID‐19 was confirmed, surveillance was conducted for potentially exposed patients and HCWs. Results A total of 1,841 cases presenting with respiratory syndromes required admission over the study period. Amongst these, 70 cases of COVID‐19 were subsequently confirmed. The majority (84.2%, 59/70) were picked up at ED triage as they fulfilled suspect case criteria. Of these, 34 met the official screening criteria; another 25 were picked up by the broader internal screening criteria. Over the 12‐week period, the cumulative sensitivity of internal screening criteria was 84.3% (95% confidence interval, CI=73.6%‐91.9%), whereas the sensitivity of the official screening criteria was 48.6% (95%CI=36.4%‐ 60.8%). Given the broadened internal criteria, the pre‐existing ED “fever area” was insufficient and had to be expanded. However, there were no cases of nosocomial transmission from intra‐ED exposure, despite extensive surveillance. Conclusion Frontline physicians need to be given leeway to decide on the disposition of cases based on clinical suspicion during an ongoing outbreak of COVID‐19. If a broader criterion is used at ED triage, ED facilities and isolation facilities need to be readied to accommodate a surge of suspect cases. Usage of appropriate PPE is essential in minimising nosocomial transmission.
Background: The COVID-19 outbreak is presenting the health system with new challenges, and there is a great need for knowledge about symptoms, clinical findings and course of illness in patients admitted to Norwegian hospitals with COVID-19. Material and method: In this observational qualitative study, all patients admitted to a Norwegian local hospital (Bærum Hospital) with proven COVID-19 infection were included consecutively from the start of the outbreak. We present here patient characteristics, symptoms, clinical findings, experience of using clinical scoring systems and course of illness based on data in medical records. Results: In the period 9-31 March 2020, 42 patients, of whom 28 (67 %) were men, were admitted to hospital with COVID-19 infection. The median age was 72.5 years (range 30-95). Fever (79 %), reduced general condition (79 %), dyspnoea (69 %) and cough (67 %) were the most common symptoms. A total of nine patients (21 %) had a critical course of illness with treatment in the Intensive Care Department and/or death during their stay in hospital. Patients with a critical course had a higher average score on National Early Warning Score 2 (NEWS2) on admission (7.6 vs 3.3). Only one of the most severely ill patients scored ≥ 2 on the quick Sepsis-related Organ Failure Assessment (qSOFA) on admission. Interpretation: Most patients admitted to our hospital with COVID-19 had a fever and respiratory tract symptoms. A high percentage of patients had a critical course of illness. A NEWS2 score of ≥ 5 on admission may be a useful aid in identifying patients at risk of a critical course of illness, while CRB-65 and qSOFA score ≥ 2 proved to be of little usefulness for this purpose in our material.
Objectives To investigate the different CT characteristics which may distinguish influenza from 2019 coronavirus disease (COVID-19).MethodsA total of 13 confirmed patients with COVID-19 were enrolled from January 16, 2020, to February 25, 2020. Furthermore, 92 CT scans of confirmed patients with influenza pneumonia, including 76 with influenza A and 16 with influenza B, scanned between January 1, 2019, to February 25, 2020, were retrospectively reviewed. Pulmonary lesion distributions, number, attenuation, lobe predomination, margin, contour, ground-glass opacity involvement pattern, bronchial wall thickening, air bronchogram, tree-in-bud sign, interlobular septal thickening, intralobular septal thickening, and pleural effusion were evaluated in COVID-19 and influenza pneumonia cohorts.ResultsPeripheral and non-specific distributions in COVID-19 showed a markedly higher frequency compared with the influenza group (p < 0.05). Most lesions in COVID-19 showed balanced lobe localization, while in influenza pneumonia they were predominantly located in the inferior lobe (p < 0.05). COVID-19 presented a clear lesion margin and a shrinking contour compared with influenza pneumonia (p < 0.05). COVID-19 had a patchy or combination of GGO and consolidation opacities, while a cluster-like pattern and bronchial wall thickening were more frequently seen in influenza pneumonia (p < 0.05). The lesion number and attenuation, air bronchogram, tree-in-bud sign, interlobular septal thickening, and intralobular septal thickening were not significantly different between the two groups (all p > 0.05).Conclusions Though viral pneumonias generally show similar imaging features, there are some characteristic CT findings which may help differentiating COVID-19 from influenza pneumonia.Key Points • CT can play an early warning role in the diagnosis of COVID-19 in the case of no epidemic exposure. • CT could be used for the differential diagnosis of influenza and COVID-19 with satisfactory accuracy. • COVID-19 had a patchy or combination of GGO and consolidation opacities with peripheral distribution and balanced lobe predomination.
This document from the European Society of Radiology (ESR) and the European Society of Thoracic Imaging (ESTI) aims to present the main imaging features, and the role of CT scan in the early diagnosis of COVID-19, describing, in particular, the typical findings which make it possible to identify the disease and distinguish it from bacterial causes of infection, and to define which category of patients may benefit from CT imaging. The precautions that must be taken when performing scans to protect radiologists and technologists from infection will be described. The organisational measures that can be taken within radiology departments in order to cope with the influx of patients, while continuing to manage other emergency and time-sensitive activity (e.g. oncology, other infectious diseases etc.), will be discussed. KEY POINTS: • Bilateral ground glass opacities are typical CT manifestations of COVID-19. • Crazy paving and organising pneumonia pattern are seen at a later stage. • Extensive consolidation is associated with a poor prognosis.