Thoracic Point-of-Care Ultrasound: A SARS-CoV-2 Data Repository for Future Artificial Intelligence and Machine Learning

Ali, Abdel-Moneim Mohamed; El-Alali, Emran; Weltz, Adam S.; Rehrig, Scott T.

doi:10.1177/15533506211018671

Cited by 2 publications

(2 citation statements)

References 10 publications

(11 reference statements)

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“…The COVIDx-US dataset consists of 150 videos (12943 frames) in total, categorized into four subsets: COVID-19, non-COVID-19, other lung diseases, and healthy patients. Another group worked on a 12-lung-field scanning protocol of thoracic POCUS (T-POCUS) images for COVID-19 patients [ 64 ]. The preliminary dataset consists of 16 subjects (mean age 67 years old), with 81% being male.…”

Section: Machine Learning In Covid-19 Lusmentioning

confidence: 99%

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

et al. 2022

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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.

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Section: Machine Learning In Covid-19 Lusmentioning

confidence: 99%

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

et al. 2022

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“…The use of automated techniques for LUS imaging has developed dramatically in recent years, with a rapid increase in operation and understanding of such tools during the COVID-19 pandemic [36][37][38][39][40]. Automatic tools such as computer-aided diagnosis systems and image analysis algorithms have made it possible to quickly and accurately analyze images.…”

Section: Introductionmentioning

confidence: 99%

Can Dialysis Patients Identify and Diagnose Pulmonary Congestion Using Self-Lung Ultrasound?

Schneider,

Maimon,

Hasidim

et al. 2023

JCM

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Background: With the recent developments in automated tools, smaller and cheaper machines for lung ultrasound (LUS) are leading us toward the potential to conduct POCUS tele-guidance for the early detection of pulmonary congestion. This study aims to evaluate the feasibility and accuracy of a self-lung ultrasound study conducted by hemodialysis (HD) patients to detect pulmonary congestion, with and without artificial intelligence (AI)-based automatic tools. Methods: This prospective pilot study was conducted between November 2020 and September 2021. Nineteen chronic HD patients were enrolled in the Soroka University Medical Center (SUMC) Dialysis Clinic. First, we examined the patient’s ability to obtain a self-lung US. Then, we used interrater reliability (IRR) to compare the self-detection results reported by the patients to the observation of POCUS experts and an ultrasound (US) machine with an AI-based automatic B-line counting tool. All the videos were reviewed by a specialist blinded to the performer. We examined their agreement degree using the weighted Cohen’s kappa (Kw) index. Results: A total of 19 patients were included in our analysis. We found moderate to substantial agreement between the POCUS expert review and the automatic counting both when the patient performed the LUS (Kw = 0.49 [95% CI: 0.05–0.93]) and when the researcher performed it (Kw = 0.67 [95% CI: 0.67–0.67]). Patients were able to place the probe in the correct position and present a lung image well even weeks from the teaching session, but did not show good abilities in correctly saving or counting B-lines compared to an expert or an automatic counting tool. Conclusions: Our results suggest that LUS self-monitoring for pulmonary congestion can be a reliable option if the patient’s count is combined with an AI application for the B-line count. This study provides insight into the possibility of utilizing home US devices to detect pulmonary congestion, enabling patients to have a more active role in their health care.

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Thoracic Point-of-Care Ultrasound: A SARS-CoV-2 Data Repository for Future Artificial Intelligence and Machine Learning

Cited by 2 publications

References 10 publications

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

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

Can Dialysis Patients Identify and Diagnose Pulmonary Congestion Using Self-Lung Ultrasound?

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