COVID-19 Pneumonia: The Great Ultrasonography Mimicker

Lacedonia, Donato; Quarato, Carla Maria Irene; Mirijello, Antonio; Trovato, Guglielmo M.; Colle, Anna Del; Rea, Gaetano; Scioscia, Giulia; Barbaro, Maria Pia Foschino; Sperandeo, Marco

doi:10.3389/fmed.2021.709402

Cited by 7 publications

(10 citation statements)

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“…GGO with or without consolidation in a peripheral and basilar predominant distribution was found to be the most common finding in our SARS-CoV-2 positive cohort, which is in agreement with previous reports [26,27]. The differential diagnosis of COVID-19 and other viral/bacterial pneumonia is challenging based on HRCT findings, owing to overlapping computed tomography features, which is in consonance with previous studies [28,29].…”

Section: Discussionsupporting

confidence: 93%

Co-infection of SARS-CoV-2 with other respiratory pathogens in patients with liver disease

Samal

Agarwal

Soni

et al. 2022

Access Microbiology

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Respiratory illness caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) was first documented in Wuhan, China, in December 2019, followed by its rapid spread across the globe. Accumulating evidence has demonstrated viral/bacterial co-infection in the respiratory tract could modulate disease severity and its outcome in COVID-19 infection. In this retrospective study, 300 chronic liver disease patients with radiologically confirmed lower respiratory tract infection were enrolled from September 2020 to December 2021. In all of them, along with SARS-CoV-2, other respiratory viral/bacterial pathogens were studied. In total, 23.7 % (n=71) patients were positive for SARS-CoV-2. Among the positive patients, 23.9 % (n=17) had co-infection with other respiratory pathogens, bacterial co-infections being dominant. The SARS-CoV-2 negative cohort had 39.7 % positivity (n=91) for other respiratory pathogens, the most common being those of the rhinovirus/enterovirus family. Ground glass opacity (GGO) with consolidation was found to be the most common radiological finding among SARS-CoV-2 positive co-infected patients, as compared to only GGO among SARS-CoV-2 mono-infected patients. Accurate diagnosis of co-infections, especially during pandemics including COVID-19, can ameliorate the treatment and management of suspected cases.

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Section: Discussionsupporting

confidence: 93%

Co-infection of SARS-CoV-2 with other respiratory pathogens in patients with liver disease

Samal

Agarwal

Soni

et al. 2022

Access Microbiology

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“…Namely, it should not be considered an alternative to CT scan for assessment of COVID-19 pneumonia in pregnant women, especially considering the higher risk of operator exposure to contagion associated with this type of examination ( 31 ). Moreover, Lung US findings in COVID-19 patients are even less specific than those detectable on CT scans are ( 32 ).…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence (AI) and Lung Ultrasound in Infectious Pulmonary Disease

Trovato¹,

Russo²

2021

Front. Med.

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Deep Learning algorithms are ready for use and can aid in the interpretation of radiological findings monitoring change during follow-up. However, the translational approach to ultrasound is difficult and currently subject to major limitations. Great help can derive from computer-assisted report procedures, which, in appropriate contexts of expert machine learning, can structure a future context of diagnostic orientation.Lung ultrasound is a diagnostic method that relies heavily on the operator's abilities, in which the human expertise component must be accompanied by the most suitable quality of the equipment and of its appropriate setting. However, this strictly clinical and qualitative interpretation when reading lung images is an element of weakness since we have health professionals with different levels of knowledge and expertise in performing the procedure. There are also particular difficulties linked to the conditions of the patients and to the methods of assistance and caution due to the need to prevent contagion in the fight against coronavirus 2019 disease (COVID-19).Patients, particularly in limited resource subsets at home, in ambulances, and in emergency room facilities, are very difficult subjects even for expert operators, and this is also because chest ultrasounds are full-contact procedures.Quantitative analysis makes radiology reporting much more comprehensive. Actually, several research groups have begun looking to artificial intelligence (AI) as a tool for reading and analyzing X-rays and computed tomography (CT) scans and helping to diagnose and monitor COVID-19 by several deep-learning approaches. This is carried out with the hope of overcoming the intrinsic limits of the diagnostic procedure and the context of intervention.Unfortunately, with US procedures, the proposal to use inappropriate methods, based on the automatic reading of the artifacts, in particular of the b-lines, has been addressed: this is what has been unexpectedly, and for no good reason, suggested by many. In this regard, it must be strongly reiterated that quantifying erratic and unreliable artifacts, measuring them by machine counting methods, is only a mystifying and misleading approach without advantages and is de facto dangerous in patient management.Currently, it is unlikely that the algorithms proposed may directly replace the medical doctors, and, namely, the radiologists' judgments as well as personal responsibility during ultrasound diagnosis. This is due to the limited or even absent specificity of these approaches for categorizing definite findings and to the medico-legal implications of such diagnoses.

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“…Due to the necessary hygienic measures, the use of handheld ultrasound devices in COVID has become established at some centers [27,28]. Although in the context of a high clinical pretest probability for COVID, this is a spot diagnosis, these ultrasound phenomena are nevertheless not specific but can also be observed in other interstitial pneumonias [29].…”

Section: Special Featuresmentioning

confidence: 99%

Ultrasound of the lung

et al. 2023

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The CME review is intended to explain and discuss the clinical value of lung ultrasound but also to enable a pragmatically oriented approach by analyzing the clinical aspect. This includes knowledge of the pre-test probability, the acuteness of the disease, the current clinical situation, detection and/or characterization, initial diagnosis or follow up assessment and the peculiarities of exclusion diagnosis. Diseases of the pleura and lungs are described using these criteria with their direct and indirect sonographic signs and the specific clinical significance of ultrasound findings. The importance and criteria of conventional B-mode, color Doppler ultrasound with or without spectral analysis of the Doppler signal and contrast-enhanced ultrasound are discussed as well.

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COVID-19 Pneumonia: The Great Ultrasonography Mimicker

Cited by 7 publications

References 50 publications

Co-infection of SARS-CoV-2 with other respiratory pathogens in patients with liver disease

Co-infection of SARS-CoV-2 with other respiratory pathogens in patients with liver disease

Artificial Intelligence (AI) and Lung Ultrasound in Infectious Pulmonary Disease

Ultrasound of the lung

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