Current role of imaging in COVID-19 infection with recent recommendations of point of care ultrasound in the contagion: a narrative review

Accuracy of point-of-care ultrasound examination of the lung in primary care performed by general practitioners: a cross-sectional study

BACKGROUND

Lung ultrasonography (LUS) is a point-of-care imaging modality with growing potential in primary care.

OBJECTIVES

While its use is well established in hospital settings, data on its accuracy when performed by general practitioners (GPs) remain limited. This study aimed to assess the diagnostic accuracy of LUS conducted by GPs following structured training.

METHODS

We recruited 17 GPs from various regions of the Czech Republic. They completed a two-day educational course focused on LUS. Patients with current dyspnoea (NYHA II-IV) or a history of dyspnoea within the last four weeks were included and underwent LUS to assess the presence of pleural effusion and interstitial syndrome. An independent expert sonographer, blinded to clinical data, evaluated recorded LUS video loops as the reference standard. LUS findings were categorized into A profile (presence of A lines and intact lung sliding, indicating normal aeration), B profile (three or more B lines per intercostal space in at least two intercostal spaces per hemithorax, suggesting interstitial syndrome), pulmonary consolidation and pleural effusion.

RESULTS

A total of 128 patients were enrolled in the study. A total of 768 thoracic segments were examined. A profile was identified in 642 (83.6%) segments, B profile in 108 (14.1%), pulmonary consolidation in 8 (1.0%), and pleural effusion in 12 (1.6%). For the identification of A profile, the sensitivity was 97.51% (95% CI 95.98-98.57), and the specificity was 88.10% (95% CI 81,13-93,18); for B profile, the sensitivity was 87.04% (95% CI 79,21-92,73), and the specificity was 97.73% (95% CI96,28-98,72); for pulmonary consolidation, the sensitivity was 100.0% (95% CI 63,06-100,00), and the specificity was 100.0% (95% CI 99,52-100,0); for pleural effusion, the sensitivity was 83.33% (95% CI 51,59-97,91), and the specificity was 99.87% (95% CI 99,27-100,00).

CONCLUSION

Our findings provide important preliminary data, demonstrating that GPs can perform LUS accurately after a structured training program. THE TRIAL REGISTRATION IDENTIFIER: is NCT04905719.

The Role of POCUS to Face COVID-19: A Narrative Review

COVID-19 has been a challenging outbreak to face, with millions of deaths among the globe. Acute respiratory failure due to interstitial pneumonia was the leading cause of death other than prothrombotic activation and complications. Lung ultrasound (LUS) and point-of-care ultrasound (POCUS) are widely used not only to triage, to identify, and to monitor lungs involvement but also to assess hemodynamic status and thrombotic and hemorrhagic complications, mainly in critically ill patients. POCUS has gained growing consideration due to its bedside utilization, reliability, and reproducibility even in emergency settings especially in unstable patients. In this narrative review, we aim to describe LUS and POCUS utilization in COVID-19 infection based on the literature found on this topic. We reported the LUS patterns of COVID-19 pulmonary infection, the diagnostic accuracy with respect to CT lung scan, its prognostic value, the variety of scores and protocols proposed, and the utilization of POCUS to investigate the extra-lung complications.

Rural family physician use of point-of-care ultrasonography: experiences of primary care providers in British Columbia, Canada

BACKGROUND

In British Columbia (BC), rural and remote areas lack proximal access to radiographic services. Poor access to radiographic services in rural settings presents a challenge to timely diagnosis and screening across many disease states and healthy pregnancies. As a solution to the lack of access to radiographic services in rural settings, the Rural Coordination Centre of BC (RCCbc) supported rural Family Physicians (FPs) wishing to use PoCUS through the Intelligent Network for PoCUS (IN PoCUS) program. This study evaluates FPs' experience and use of PoCUS in their clinical practice.

METHODS

This qualitative study conducted in-depth virtual interviews with 21 FPs across rural BC. The interview asked participants' motivation to participate in the RCCbc program, the type of training they received, their current use of PoCUS, their experience with the technology, and their experience interacting with specialists in regional centres. Thematic analysis of findings was undertaken.

RESULTS

This study used Rogers' framework on the five elements of diffusion of innovation to understand the factors that impede and enable the adoption of PoCUS in rural practice. Rural FPs in this study differentiated PoCUS from formal imaging done by specialists. The adoption of PoCUS was viewed as an extension of physical exams and was compatible with their values of providing generalist care. This study found that the use of PoCUS provided additional information that led to better clinical decision-making for triage and allowed FPs to determine the urgency for patient referral and transport to tertiary hospitals. FPs also reported an increase in job satisfaction with PoCUS use. Some barriers to using PoCUS included the time needed to be acquainted with the technology and learning how to integrate it into their clinical flow in a seamless manner.

CONCLUSION

This study has demonstrated the importance of PoCUS in improving patient care and facilitating timely diagnosis and treatment. As the use of PoCUS among FPs is relatively new in Canada, larger infrastructure support such as improving billing structures, long-term subsidies, educational opportunities, and a quality improvement framework is needed to support the use of PoCUS among rural FPs.

Findings of Abdominal Imaging in Patients with COVID-19 - Part 1: Hollow Organs

Since COVID-19 has spread worldwide, the role of imaging for early detection of the disease has become more prominent. Abdominal symptoms in COVID-19 are common in addition to respiratory manifestations. This review collected the available data about abdominal computed tomography (CT) and ultrasonography indications in hollow abdominal organs in patients with COVID-19 and their findings. Since abdominal imaging is less frequently used in COVID-19, there is limited information about the gastrointestinal findings. The most common indications for abdominal CT in patients with COVID-19 were abdominal pain and sepsis. Bowel wall thickening and fluid-filled colon were the most common findings in abdominal imaging. Acute mesenteric ischemia (AMI) was one of the COVID-19 presentations secondary to coagulation dysfunction. AMI manifests with sudden abdominal pain associated with high morbidity and mortality in admitted patients; therefore, CT angiography should be considered for early diagnosis of AMI. Ultrasonography is a practical modality because of its availability, safety, rapidity, and ability to be used at the bedside. Clinicians and radiologists should be alert to indications and findings of abdominal imaging modalities in COVID-19 to diagnose the disease and its potentially serious complications promptly.

The diagnostic performance of lung ultrasound for detecting COVID-19 in emergency departments: A systematic review and meta-analysis

PURPOSE

To perform a systematic review and meta-analysis of published literature investigating the use of lung ultrasound (LUS) on COVID-19 patients, in emergency point of care settings, and to determine its diagnostic value compared with lung computed tomography (LCT) diagnostic performance. Whilst using the real-time polymerase chain reaction test as the 'gold standard'.

METHODS

Literature searches were performed on MEDLINE, Embase, Web of Science, and PubMed databases for eligible studies. The LUS and LCT pooled diagnostic performance were measured using DerSimonian-Laird random effect method.

RESULT

Out of a total of 158 studies, 16 met the eligibility criteria and were included in this review. The pooled sensitivity, specificity, positive and negative likelihood ratios were 86.9%, 62.4%, 2.4 and 0.19, respectively for LUS and 93.5%, 72.6%, 3.3 and 0.05, respectively for LCT.

CONCLUSION

The lung ultrasound (LUS) demonstrated acceptable sensitivity but poor specificity when used independently to diagnose COVID-19 pneumonia patients in emergency departments, while the lung computed tomography showed higher performance. Thus, LUS can be used to supplement existing diagnostic tools and possibly for the triage of patients.

Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review

Artificial intelligence (AI) and computer vision (CV) methods become reliable to extract features from radiological images, aiding COVID-19 diagnosis ahead of the pathogenic tests and saving critical time for disease management and control. Thus, this review article focuses on cascading numerous deep learning-based COVID-19 computerized tomography (CT) imaging diagnosis research, providing a baseline for future research. Compared to previous review articles on the topic, this study pigeon-holes the collected literature very differently (i.e., its multi-level arrangement). For this purpose, 71 relevant studies were found using a variety of trustworthy databases and search engines, including Google Scholar, IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus. We classify the selected literature in multi-level machine learning groups, such as supervised and weakly supervised learning. Our review article reveals that weak supervision has been adopted extensively for COVID-19 CT diagnosis compared to supervised learning. Weakly supervised (conventional transfer learning) techniques can be utilized effectively for real-time clinical practices by reusing the sophisticated features rather than over-parameterizing the standard models. Few-shot and self-supervised learning are the recent trends to address data scarcity and model efficacy. The deep learning (artificial intelligence) based models are mainly utilized for disease management and control. Therefore, it is more appropriate for readers to comprehend the related perceptive of deep learning approaches for the in-progress COVID-19 CT diagnosis research.

An Imaging Overview of COVID-19 ARDS in ICU Patients and Its Complications: A Pictorial Review

A significant proportion of patients with COVID-19 pneumonia could develop acute respiratory distress syndrome (ARDS), thus requiring mechanical ventilation, and resulting in a high rate of intensive care unit (ICU) admission. Several complications can arise during an ICU stay, from both COVID-19 infection and the respiratory supporting system, including barotraumas (pneumothorax and pneumomediastinum), superimposed pneumonia, coagulation disorders (pulmonary embolism, venous thromboembolism, hemorrhages and acute ischemic stroke), abdominal involvement (acute mesenteric ischemia, pancreatitis and acute kidney injury) and sarcopenia. Imaging plays a pivotal role in the detection and monitoring of ICU complications and is expanding even to prognosis prediction. The present pictorial review describes the clinicopathological and radiological findings of COVID-19 ARDS in ICU patients and discusses the imaging features of complications related to invasive ventilation support, as well as those of COVID-19 itself in this particularly fragile population. Radiologists need to be familiar with COVID-19's possible extra-pulmonary complications and, through reliable and constant monitoring, guide therapeutic decisions. Moreover, as more research is pursued and the pathophysiology of COVID-19 is increasingly understood, the role of imaging must evolve accordingly, expanding from the diagnosis and subsequent management of patients to prognosis prediction.

Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic

Ultrasound imaging of the lung has played an important role in managing patients with COVID-19-associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques.

The role of chest imaging in the diagnosis, management, and monitoring of coronavirus disease 2019 (COVID-19)

Coronavirus disease 2019 (COVID-19) pandemic has posed a major public health crisis all over the world. The role of chest imaging, especially computed tomography (CT), has evolved during the pandemic paralleling the accumulation of scientific evidence. In the early stage of the pandemic, the performance of chest imaging for COVID-19 has widely been debated especially in the context of comparison to real-time reverse transcription polymerase chain reaction. Current evidence is against the use of chest imaging for routine screening of COVID-19 contrary to the initial expectations. It still has an integral role to play, however, in its work up and staging, especially when assessing complications or disease progression. Chest CT is gold standard imaging modality for COVID-19 pneumonia; in some situations, chest X-ray or ultrasound may be an effective alternative. The most important role of radiologists in this context is to be able to identify those patients at greatest risk of imminent clinical decompensation by learning to stratify cases of COVID-19 on the basis of radiologic imaging in the most efficient and timely fashion possible. The present availability of multiple and more refined CT grading systems and classification is now making this task easier and thereby contributing to the recent improvements achieved in COVID-19 treatment and outcomes. In this article, evidence of chest imaging regarding diagnosis, management and monitoring of COVID-19 will be chronologically reviewed.

Long-term chest CT follow-up in COVID-19 Survivors: 102-361 days after onset

Background

The aim of this study was to evaluate long-term longitudinal changes in chest computed tomography (CT) findings in coronavirus disease 2019 (COVID-19) survivors and their correlations with dyspnea after discharge.

Methods

A total of 337 COVID-19 survivors who underwent CT scan during hospitalization and between 102 and 361 days after onset were retrospectively included. Subjective CT findings, lesion volume, therapeutic measures and laboratory parameters were collected. The severity of the survivors' dyspnea was determined by follow-up questionnaire. The evolution of the CT findings from the peak period to discharge and throughout follow-up and the abilities of CT findings and clinical parameters to predict survival with and without dyspnea were analyzed.

Results

Ninety-one COVID-19 survivors still had dyspnea at follow-up. The age, comorbidity score, duration of hospital stays, receipt of hormone administration, receipt of immunoglobulin injections, intensive care unit (ICU) admission, receipt of mechanical ventilation, laboratory parameters, clinical classifications and parameters associated with lesion volume of the survivors with dyspnea were significantly different from those of survivors without dyspnea. Among the clinical parameters and CT parameters used to identify dyspnea, parameters associated with lesion volume showed the largest area under the curve (AUC) values, with lesion volume at discharge showing the largest AUC (0.820). Lesion volume decreased gradually from the peak period to discharge and through follow-up, with a notable decrease observed after discharge. Absorption of lesions continued 6 months after discharge.

Conclusions

Among the clinical parameters and subjective CT findings, CT findings associated with lesion volume were the best predictors of post-discharge dyspnea in COVID-19 survivors.

A Comparison of Lung Ultrasound and Computed Tomography in the Diagnosis of Patients with COVID-19: A Systematic Review and Meta-Analysis

Background Lung ultrasound (LUS) and computed tomography (CT) can both be used for diagnosis of interstitial pneumonia caused by coronavirus disease 2019 (COVID-19), but the agreement between LUS and CT is unknown. Purpose to compare the agreement of LUS and CT in the diagnosis of interstitial pneumonia caused by COVID-19. Materials and Methods We searched PubMed, Cochrane library, Embase, Chinese Biomedicine Literature, and WHO COVID-19 databases to identify studies that compared LUS with CT in the diagnosis of interstitial pneumonia caused by COVID-19. We calculated the pooled overall, positive and negative percent agreements, diagnostic odds ratio (DOR) and the area under the standard receiver operating curve (SROC) for LUS in the diagnosis of COVID-19 compared with CT. Results We identified 1896 records, of which nine studies involving 531 patients were finally included. The pooled overall, positive and negative percentage agreements of LUS for the diagnosis of interstitial pneumonia caused by COVID-19 compared with CT were 81% (95% confidence interval [CI] 43–99%), 96% (95% CI, 80–99%, I² = 92.15%) and 80% (95%CI, 60–92%, I² = 92.85%), respectively. DOR was 37.41 (95% CI, 9.43–148.49, I² = 63.9%), and the area under the SROC curve was 0.94 (95% CI, 0.92–0.96). The quality of evidence for both specificity and sensitivity was low because of heterogeneity and risk of bias. Conclusion The level of diagnostic agreement between LUS and CT in the diagnosis of interstitial pneumonia caused by COVID-19 is high. LUS can be therefore considered as an equally accurate alternative for CT in situations where molecular tests are not available.

Imaging in the COVID-19 era: Lessons learned during a pandemic

The first year of the coronavirus disease 2019 (COVID-19) pandemic has been a year of unprecedented changes, scientific breakthroughs, and controversies. The radiology community has not been spared from the challenges imposed on global healthcare systems. Radiology has played a crucial part in tackling this pandemic, either by demonstrating the manifestations of the virus and guiding patient management, or by safely handling the patients and mitigating transmission within the hospital. Major modifications involving all aspects of daily radiology practice have occurred as a result of the pandemic, including workflow alterations, volume reductions, and strict infection control strategies. Despite the ongoing challenges, considerable knowledge has been gained that will guide future innovations. The aim of this review is to provide the latest evidence on the role of imaging in the diagnosis of the multifaceted manifestations of COVID-19, and to discuss the implications of the pandemic on radiology departments globally, including infection control strategies and delays in cancer screening. Lastly, the promising contribution of artificial intelligence in the COVID-19 pandemic is explored.

Discrepancies in the clinical and radiological profiles of COVID-19: A case-based discussion and review of literature

The current gold standard for the diagnosis of coronavirus disease-19 (COVID-19) is a positive reverse transcriptase polymerase chain reaction (RT-PCR) test, on the background of clinical suspicion. However, RT-PCR has its limitations; this includes issues of low sensitivity, sampling errors and appropriate timing of specimen collection. As pulmonary involvement is the most common manifestation of severe COVID-19, early and appropriate lung imaging is important to aid diagnosis. However, gross discrepancies can occur between the clinical and imaging findings in patients with COVID-19, which can mislead clinicians in their decision making. Although chest X-ray (CXR) has a low sensitivity for the diagnosis of COVID-19 associated lung disease, especially in the earlier stages, a positive CXR increases the pre-test probability of COVID-19. CXR scoring systems have shown to be useful, such as the COVID-19 opacification rating score which helps to predict the need of tracheal intubation. Furthermore, artificial intelligence-based algorithms have also shown promise in differentiating COVID-19 pneumonia on CXR from other lung diseases. Although costlier than CXR, unenhanced computed tomographic (CT) chest scans have a higher sensitivity, but lesser specificity compared to RT-PCR for the diagnosis of COVID-19 pneumonia. A semi-quantitative CT scoring system has been shown to predict short-term mortality. The routine use of CT pulmonary angiography as a first-line imaging modality in patients with suspected COVID-19 is not justifiable due to the risk of contrast nephropathy. Scoring systems similar to those pioneered in CXR and CT can be used to effectively plan and manage hospital resources such as ventilators. Lung ultrasound is useful in the assessment of critically ill COVID-19 patients in the hands of an experienced operator. Moreover, it is a convenient tool to monitor disease progression, as it is cheap, non-invasive, easily accessible and easy to sterilise. Newer lung imaging modalities such as magnetic resonance imaging (MRI) for safe imaging among children, adolescents and pregnant women are rapidly evolving. Imaging modalities are also essential for evaluating the extra-pulmonary manifestations of COVID-19: these include cranial imaging with CT or MRI; cardiac imaging with ultrasonography (US), CT and MRI; and abdominal imaging with US or CT. This review critically analyses the utility of each imaging modality to empower clinicians to use them appropriately in the management of patients with COVID-19 infection.

COVID-19 and venous thromboembolism: Known and unknown for imaging decisions

As we continue to fight against the current coronavirus disease-2019 (COVID-19) pandemic, healthcare professionals across the globe are trying to answer questions surrounding how to best help patients with the up-to-date available science while awaiting the development of new therapies and mass vaccination. Since early in the pandemic, studies indicated a heightened risk of venous thromboembolism (VTE) in COVID-19 infected patients. There have been differing expert opinions about how to assess pretest probability of VTE in this patient population. This has been partly due to the high prevalence of respiratory failure in this patient population and the use of D-dimer as a prognostic test which is also frequently elevated in patients with COVID-19 in absence of VTE. Some experts have argued for an approach similar to usual care with testing if clinical suspicion is high enough. Some have argued for more routine screening at different points of care. Others have even suggested empiric therapeutic anti-coagulation in moderate to severely ill COVID-19 patients. In the following article, we review and summarize the most current literature in hopes of assisting clinicians in decision making and guidance for when to be concerned for VTE in COVID-19 patients. We also discuss research gaps and share pathways currently being used within our institution.