6 Lung Ultrasound versus Chest X-ray for the Diagnosis of COVID-19 Pneumonia

Gibbons, Ryan C.; Méndez, Karla Irene Martínez; Magee, Molly; Goett, H; Murrett, J.; Genninger, Jessica; Tyner, Nicholas; Zhao, Hong; Gentile, Nicole D.; Costantino, Thomas

doi:10.1016/j.annemergmed.2020.09.016

Cited by 4 publications

(6 citation statements)

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“…[9][10][11][12][13][14][15] Recent studies show that lung ultrasound also has better diagnostic accuracy than chest radiography in diagnosing COVID-19 pneumonia. 18,19 Chest radiography is a poor diagnostic test in COVID-19 as it may miss up to 40% of confirmed cases. 20 Meanwhile, diagnostic accuracy of lung ultrasound approaches that of CT for COVID-19 pneumonia.…”

Section: Introductionmentioning

confidence: 99%

Assessing COVID‐19 pneumonia—Clinical extension and risk with point‐of‐care ultrasound: A multicenter, prospective, observational study

Lieveld¹,

Kok

Azijli³

et al. 2021

JACEP Open

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contributed equally to this study.Registration: This study is registered with trialregister.nl, Trial NL8497.Funding and support: By JACEP Open policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). The authors have stated that no such relationships exist.Background: Assessing the extent of lung involvement is important for the triage and care of COVID-19 pneumonia. We sought to determine the utility of point-ofcare ultrasound (POCUS) for characterizing lung involvement and, thereby, clinical risk determination in COVID-19 pneumonia.Methods: This multicenter, prospective, observational study included patients with COVID-19 who received 12-zone lung ultrasound and chest computed tomography (CT) scanning in the emergency department (ED). We defined lung disease severity using the lung ultrasound score (LUS) and chest CT severity score (CTSS). We assessed the association between the LUS and poor outcome (ICU admission or 30-day all-cause mortality). We also assessed the association between the LUS and hospital length of stay. We examined the ability of the LUS to differentiate between disease severity groups. Lastly, we estimated the correlation between the LUS and CTSS and the interrater agreement for the LUS. We handled missing data by multiple imputation with chained equations and predictive mean matching.

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Section: Introductionmentioning

confidence: 99%

Assessing COVID‐19 pneumonia—Clinical extension and risk with point‐of‐care ultrasound: A multicenter, prospective, observational study

Lieveld¹,

Kok

Azijli³

et al. 2021

JACEP Open

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“…26,50,82,83 Additionally, studies using ML on multiple imaging modalities from the same cohort are certainly needed to shed light on comparative questions between modalities from the perspective of ML. The performance of AI-assisted radiologists in detecting COVID-19 might or might not confirm the current radiologic findings, for example that CXR is less sensitive than CT 84 and LUS (when compared with RT-PCR 76 or CT 85 ) or that B-lines are the most reliable pathological pattern across CT, CXR, and LUS. 86 From the AI perspective, LUS is presumably the modality with the highest improvement potential in MI analysis in the near future.…”

Section: Imaging Modality Rivalrymentioning

confidence: 78%

On the role of artificial intelligence in medical imaging of COVID-19

Born¹,

Beymer²,

Rajan³

et al. 2021

Patterns

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Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance. Our analysis evidences a significant disparity between clinical and AI communities, in the focus on both imaging modalities (AI experts neglected CT and ultrasound, favoring X-ray) and performed tasks (71.9% of AI papers centered on diagnosis). The vast majority of manuscripts were found to be deficient regarding potential use in clinical practice, but 2.7% (n = 12) publications were assigned a high maturity level and are summarized in greater detail. We provide an itemized discussion of the challenges in developing clinically relevant AI solutions with recommendations and remedies.THE BIGGER PICTURE During the COVID-19 pandemic, medical imaging (CT, X-ray, ultrasound) has played a key role in addressing the magnified need for speed, low cost, ubiquity, and precision in patient care. The contemporary digitization of medicine and rise of artificial intelligence (AI) induce a quantum leap in medical imaging: AI has proven equipollent to healthcare professionals across a diverse range of tasks, and hopes are high that AI can save time and cost and increase coverage by advancing rapid patient stratification and empowering clinicians. This review bridges medical imaging and AI in the context of COVID-19 and conducts the largest systematic review of the literature in the field. We identify several gaps and evidence significant disparities between clinicians and AI experts and foresee a need for improved, interdisciplinary collaboration to develop robust AI solutions that can be deployed in clinical practice.The key challenges on that roadmap are discussed alongside recommended solutions. ll

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“…Additionally, studies using ML on multiple imaging modalities from the same cohort are certainly needed to shed light on comparative questions between modalities from the perspective of ML. The performance of AI-assisted radiologists in detecting COVID-19 might or might not confirm the current radiologic findings, for example that CXR is less sensitive than CT 84 and LUS (when compared to RT-PCR 85 or CT 86 ) or that B-lines are the most reliable pathological pattern across CT, CXR and LUS 87 . From the AI perspective, LUS is presumably the modality with the highest improvement potential in medical image analysis in the near future.…”

Section: Discussionmentioning

confidence: 80%

On the Role of Artificial Intelligence in Medical Imaging of COVID-19

Born

Beymer

Rajan

et al. 2020

Preprint

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During the COVID-19 pandemic, lung imaging takes a key role in addressing the magnified need of speed, cost, ubiquity and precision in medical care. The rise of artificial intelligence induced a quantum leap in medical imaging: AI has now proven equipollent to healthcare professionals in several diseases and the potential to save time, cost and increase coverage. But AI-accelerated medical imaging must still fully demonstrate its ability in remediating diseases such as COVID-19. We identify key use cases of lung imaging for COVID-19, comparing CT, X-Ray and ultrasound imaging from clinical and AI perspectives. We perform a systematic, manual survey of 197 related publications that reveals a disparity in the focus of the AI and clinical communities, caused by data availability and the lack of collaboration, and in modality trends, driven by ubiquity. Last, challenges in AI-acceleration and ways to remediate them are discussed and future research goals are identified.

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6 Lung Ultrasound versus Chest X-ray for the Diagnosis of COVID-19 Pneumonia

Cited by 4 publications

References 0 publications

Assessing COVID‐19 pneumonia—Clinical extension and risk with point‐of‐care ultrasound: A multicenter, prospective, observational study

Assessing COVID‐19 pneumonia—Clinical extension and risk with point‐of‐care ultrasound: A multicenter, prospective, observational study

On the role of artificial intelligence in medical imaging of COVID-19

On the Role of Artificial Intelligence in Medical Imaging of COVID-19

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