Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools. Ultrasound, in contrast to CT or X-Ray, has many practical advantages and can serve as a globally-applicable first-line examination technique. We provide the largest publicly available lung ultrasound (US) dataset for COVID-19 consisting of 106 videos from three classes (COVID-19, bacterial pneumonia, and healthy controls); curated and approved by medical experts. On this dataset, we perform an in-depth study of the value of deep learning methods for differential diagnosis of COVID-19. We propose a frame-based convolutional neural network that correctly classifies COVID-19 US videos with a sensitivity of 0.98+-0.04 and a specificity of 0.91+-08 (frame-based sensitivity 0.93+-0.05, specificity 0.87+-0.07). We further employ class activation maps for the spatio-temporal localization of pulmonary biomarkers, which we subsequently validate for human-in-the-loop scenarios in a blindfolded study with medical experts. Aiming for scalability and robustness, we perform ablation studies comparing mobile-friendly, frame- and video-based architectures and show reliability of the best model by aleatoric and epistemic uncertainty estimates. We hope to pave the road for a community effort toward an accessible, efficient and interpretable screening method and we have started to work on a clinical validation of the proposed method. Data and code are publicly available.

Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis

Diagnostic testing plays a critical role in addressing the coronavirus disease 2019 (COVID-19) pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Rapid and accurate diagnostic tests are imperative for identifying and managing infected individuals, contact tracing, epidemiologic characterization, and public health decision making. Laboratory testing may be performed based on symptomatic presentation or for screening of asymptomatic people. Confirmation of SARS-CoV-2 infection is typically by nucleic acid amplification tests (NAAT), which requires specialized equipment and training and may be particularly challenging in resource-limited settings. NAAT may give false-negative results due to timing of sample collection relative to infection, improper sampling of respiratory specimens, inadequate preservation of samples, and technical limitations; false-positives may occur due to technical errors, particularly contamination during the manual real-time polymerase chain reaction (RT-PCR) process. Thus, clinical presentation, contact history and contemporary phyloepidemiology must be considered when interpreting results. Several sample-to-answer platforms, including high-throughput systems and Point of Care (PoC) assays, have been developed to increase testing capacity and decrease technical errors. Alternatives to RT-PCR assay, such as other RNA detection methods and antigen tests may be appropriate for certain situations, such as resource-limited settings. While sequencing is important to monitor on-going evolution of the SARS-CoV-2 genome, antibody assays are useful for epidemiologic purposes. The ever-expanding assortment of tests, with varying clinical utility, performance requirements, and limitations, merits comparative evaluation. We herein provide a comprehensive review of currently available COVID-19 diagnostics, exploring their pros and cons as well as appropriate indications. Strategies to further optimize safety, speed, and ease of SARS-CoV-2 testing without compromising accuracy are suggested. Access to scalable diagnostic tools and continued technologic advances, including machine learning and smartphone integration, will facilitate control of the current pandemic as well as preparedness for the next one.

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Review of Current COVID-19 Diagnostics and Opportunities for Further Development

COVID-19 has caused great devastation in the past year. Multi-organ point-of-care ultrasound (PoCUS) including lung ultrasound (LUS) and focused cardiac ultrasound (FoCUS) as a clinical adjunct has played a significant role in triaging, diagnosis and medical management of COVID-19 patients. The expert panel from 27 countries and 6 continents with considerable experience of direct application of PoCUS on COVID-19 patients presents evidence-based consensus using GRADE methodology for the quality of evidence and an expedited, modified-Delphi process for the strength of expert consensus. The use of ultrasound is suggested in many clinical situations related to respiratory, cardiovascular and thromboembolic aspects of COVID-19, comparing well with other imaging modalities. The limitations due to insufficient data are highlighted as opportunities for future research.

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Multi-organ point-of-care ultrasound for COVID-19 (PoCUS4COVID): international expert consensus.

Care during the COVID-19 pandemic hinges upon the existence of fast, safe, and highly sensitive diagnostic tools. Considering significant practical advantages of lung ultrasound (LUS) over other imaging techniques, but difficulties for doctors in pattern recognition, we aim to leverage machine learning toward guiding diagnosis from LUS. We release the largest publicly available LUS dataset for COVID-19 consisting of 202 videos from four classes (COVID-19, bacterial pneumonia, non-COVID-19 viral pneumonia and healthy controls). On this dataset, we perform an in-depth study of the value of deep learning methods for the differential diagnosis of lung pathologies. We propose a frame-based model that correctly distinguishes COVID-19 LUS videos from healthy and bacterial pneumonia data with a sensitivity of 0.90±0.08 and a specificity of 0.96±0.04. To investigate the utility of the proposed method, we employ interpretability methods for the spatio-temporal localization of pulmonary biomarkers, which are deemed useful for human-in-the-loop scenarios in a blinded study with medical experts. Aiming for robustness, we perform uncertainty estimation and demonstrate the model to recognize low-confidence situations which also improves performance. Lastly, we validated our model on an independent test dataset and report promising performance (sensitivity 0.806, specificity 0.962). The provided dataset facilitates the validation of related methodology in the community and the proposed framework might aid the development of a fast, accessible screening method for pulmonary diseases. Dataset and all code are publicly available at: https://github.com/BorgwardtLab/covid19_ultrasound.

Accelerating detection of lung pathologies with explainable ultrasound image analysis

Medical imaging methods are assuming a greater role in the workup of patients with COVID-19, mainly in relation to the primary manifestation of pulmonary disease and the tissue distribution of the angiotensin-converting-enzyme 2 (ACE 2) receptor. However, the field is so new that no consensus view has emerged guiding clinical decisions to employ imaging procedures such as radiography, computer tomography (CT), positron emission tomography (PET), and magnetic resonance imaging, and in what measure the risk of exposure of staff to possible infection could be justified by the knowledge gained. The insensitivity of current RT-PCR methods for positive diagnosis is part of the rationale for resorting to imaging procedures. While CT is more sensitive than genetic testing in hospitalized patients, positive findings of ground glass opacities depend on the disease stage. There is sparse reporting on PET/CT with [18F]-FDG in COVID-19, but available results are congruent with the earlier literature on viral pneumonias. There is a high incidence of cerebral findings in COVID-19, and likewise evidence of gastrointestinal involvement. Artificial intelligence, notably machine learning is emerging as an effective method for diagnostic image analysis, with performance in the discriminative diagnosis of diagnosis of COVID-19 pneumonia comparable to that of human practitioners.

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A comprehensive review of imaging findings in COVID-19 - status in early 2021.

Objectives To perform a systematic review of the accuracy of carotid ultrasound measures in determining volume responsiveness in adults. Methods We conducted a systematic review of Ovid MEDLINE and Scopus from conception until January 1, 2019. Two independent reviewers used an iterative process to identify relevant articles and abstract information from them. The quality and risk of bias were assessed with the Quality Assessment of Diagnostic Accuracy Studies version 2 tool. Results We identified 17 relevant articles with 956 patients. The 2 most frequently cited carotid measures of fluid responsiveness were corrected flow time and peak velocity or change in peak velocity with respiration (ΔCDPV). Accordingly, the diagnostic characteristics of corrected flow time in these studies varied widely, with sensitivities from 60% to 73%, specificities from 82% to 92%, and areas under the receiver operating characteristic curves from 0.75 to 0.88. Optimal cutoff values for ΔCDPV ranged from 9.1% to 14%, with areas under the receiver operating characteristic curves from 0.81 to 0.91, sensitivities from 73% to 86%, and specificities from 78% to 86%. Other measures, such as carotid blood flow and carotid diameter, had limited data to support their use. Heterogeneity of the studies prohibited a meta-analysis. Most studies had a moderate risk of bias and high applicability. Conclusions Preliminary research suggests that carotid ultrasound measures may be useful adjunct measures of fluid status; however, they should not be interpreted as absolute and should be placed in a clinical context. The most well-defined and supported measure currently is ΔCDPV, with cutoffs from 9% to 14%. Corrected flow time shows promise, because of heterogeneity of how this value is measured, an optimal cutoff has not been established.

Carotid Ultrasound to Predict Fluid Responsiveness: A Systematic Review.

Background The novel coronavirus SARS-CoV-2 has caused a pandemic, overwhelming health care systems around the world. Hospitals around the world, including the United States, have been struggling to adapt to the influx of patients with COVID-19, the illness caused by SARS-CoV2, given limited resources and high demand for medical care. Objectives This article seeks to provide emergency physicians with a guide to sonographic findings in COVID-19 and an algorithm by which point-of-care lung ultrasound may assist emergency physicians caring for these patients during the SARS-CoV-2 pandemic. Discussion The studies currently being published have established a typical set of ultrasound findings in COVID-19. Point-of-care lung ultrasound is rapid and accessible in most emergency departments in the United States, and even in many resource-poor settings. Conclusion Point-of-care ultrasound provides numerous benefits to emergency providers caring for patients with COVID-19, including decreasing resource utilization, assisting in diagnosis, guiding management of the critically ill patient, and aiding in rapid triage of patients under investigations for COVID-19.

Point-of-Care Ultrasound in the Evaluation of COVID-19.

OBJECTIVE Develop and validate a prognostic model for clinical deterioration or death within days of pulmonary embolism (PE) diagnosis using point-of-care criteria. METHODS We used prospective registry data from six emergency departments. The primary composite outcome was death or deterioration (respiratory failure, cardiac arrest, new dysrhythmia, sustained hypotension, and rescue reperfusion intervention) within 5 days. Candidate predictors included laboratory and imaging right ventricle (RV) assessments. The prognostic model was developed from 935 PE patients. Univariable analysis of 138 candidate variables was followed by penalized and standard logistic regression on 26 retained variables, and then tested with a validation database (N = 801). RESULTS Logistic regression yielded a nine-variable model, then simplified to a nine-point tool (PE-SCORE): one point each for abnormal RV by echocardiography, abnormal RV by computed tomography, systolic blood pressure < 100 mmHg, dysrhythmia, suspected/confirmed systemic infection, syncope, medico-social admission reason, abnormal heart rate, and two points for creatinine greater than 2.0 mg/dL. In the development database, 22.4% had the primary outcome. Prognostic accuracy of logistic regression model versus PE-SCORE model: 0.83 (0.80, 0.86) vs. 0.78 (0.75, 0.82) using area under the curve (AUC) and 0.61 (0.57, 0.64) vs. 0.50 (0.39, 0.60) using precision-recall curve (AUCpr). In the validation database, 26.6% had the primary outcome. PE-SCORE had AUC 0.77 (0.73, 0.81) and AUCpr 0.63 (0.43, 0.81). As points increased, outcome proportions increased: a score of zero had 2% outcome, whereas scores of six and above had ≥ 69.6% outcomes. In the validation dataset, PE-SCORE zero had 8% outcome [no deaths], whereas all patients with PE-SCORE of six and above had the primary outcome. CONCLUSIONS PE-SCORE model identifies PE patients at low- and high-risk for deterioration and may help guide decisions about early outpatient management versus need for hospital-based monitoring.

Development and validation of a prognostic tool: Pulmonary embolism short-term clinical outcomes risk estimation (PE-SCORE)

Young Woman With Chest and Back Pain.

https://www.sciencedirect.com/science/article/pii/S2405469019301268/pdfft?md5=d0c2344f58417a4ba411606267ba50c6&pid=1-s2.0-S2405469019301268-main.pdf

Dasia Esener

Papers

Carotid Ultrasound to Predict Fluid Responsiveness: A Systematic Review.

Point-of-Care Ultrasound in the Evaluation of COVID-19.

Development and validation of a prognostic tool: Pulmonary embolism short-term clinical outcomes risk estimation (PE-SCORE)

Young Woman With Chest and Back Pain.

A man with unilateral vision loss: Ocular plasmacytoma by bedside ultrasound