Enhanced Point‐of‐Care Ultrasound Applications by Integrating Automated Feature‐Learning Systems Using Deep Learning

Shokoohi, Hamid; LeSaux, Maxine; Roohani, Yusuf; Liteplo, Andrew S.; Huang, Calvin; Blaivas, Michael

doi:10.1002/jum.14860

Cited by 54 publications

(47 citation statements)

References 46 publications

(54 reference statements)

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“…Interviewees also reported the local internet services were currently too slow, unreliable, or expensive to use effectively for remote learning. However, it is important to note that given the pace of advances in both remote learning technology and artificial intelligence applications [21], the former allowing for accelerated training and the later offering automated image interpretation, a swift decline in training as a barrier to POCUS implementation is expected.…”

Section: Discussionmentioning

confidence: 99%

Stakeholder Perceptions of Point-of-Care Ultrasound Implementation in Resource-Limited Settings

et al. 2019

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Background: Nearly half of the world lacks access to diagnostic imaging. Point of care ultrasound (POCUS) is a versatile and relatively affordable imaging modality that offers promise as a means of bridging the radiology gap and improving care in low resource settings. Methods: We performed semi-structured interviews of key stakeholders at two diverse hospitals where POCUS implementation programs had recently been conducted: one in a rural private hospital in Haiti and the other in a public referral hospital in Malawi. Questions regarding the clinical utility of POCUS, as well as barriers and facilitators of its implementation, were asked of study participants. Using the Framework Method, analysis of interview transcripts was guided by the WHO ASSURED criteria for point of care diagnostics. Results: Fifteen stakeholders with diverse roles in POCUS implementation were interviewed. Interviewees from both sites considered POCUS a valuable diagnostic tool that improved clinical decisions. They perceived barriers to adequate training as one of the most important remaining barriers to POCUS implementation. Conclusions: In spite of the increasing affordability and portability of ultrasounds devices, there are still important barriers to the implementation of POCUS in resource-limited settings.

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Section: Discussionmentioning

confidence: 99%

Stakeholder Perceptions of Point-of-Care Ultrasound Implementation in Resource-Limited Settings

et al. 2019

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“…To quantify B-lines, we employed a machine learning system embedded in the Venue device, which included "auto-gain" and "auto B-lines" functions [10]. The "auto B-lines" function automatically distinguishes between real B-lines and all other artefacts [11]. The Lung Review screen provided the overall LUS score, by adding the scores from 12 segments.…”

Section: Lung Ultrasoundmentioning

confidence: 99%

Lung ultrasound for early diagnosis and severity assessment of pneumonia in patients with coronavirus disease 2019

Cho

Song

Lee

et al. 2020

Korean J Intern Med

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Background/Aims: Current evidence supports lung ultrasound as a point-ofcare alternative diagnostic tool for various respiratory diseases. We sought to determine the utility of lung ultrasound for early detection of pneumonia and for assessment of respiratory failure among patients with coronavirus disease 2019 (COVID-19). Methods: Six patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction were enrolled. All had undergone chest X-ray and chest computed tomography (CT) on the day of admission and underwent multiple point-of-care lung ultrasound scans over the course of their hospitalization. Results: Lung ultrasound detected early abnormal findings of representative B-lines in a patient with a normal chest X-ray, corresponding to ground-glass opacities on the chest CT scan. The ultrasound findings improved as her clinical condition improved and her viral load decreased. In another minimally symptomatic patient without significant chest X-ray findings, the ultrasound showed B-lines, an early sign of pneumonia before abnormalities were detected on the chest CT scan. In two critically ill patients, ultrasound was performed to assess for evaluation of disease severity. In both patients, the clinicians conducted emergency rapid sequence intubation based on the ultrasound findings without awaiting the laboratory results and radiological reports. In two children, ultrasound was used to assess the improvement in their pneumonia, thus avoiding further imaging tests such as chest CT. Conclusions: Lung ultrasound is feasible and useful as a rapid, sensitive, and affordable point-of-care screening tool to detect pneumonia and assess the severity of respiratory failure in patients hospitalized with COVID-19.

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“…An AI-based unique attempt has been made for POCUS using deep learning models with sets of thousands of images derived from a large database of US images, which include both normal and pathologic findings for the targeted conditions. With this background, deep learning models have been introduced as novel technologies to improve the accuracy and efficacy of POCUS imaging, by automated image interpretation and by matching various algorithms for specific patient conditions [91]. The results of these trials suggest the potential utility of deep learning radiomics of elastography as an alternative to the current SWE system in the noninvasive assessment of hepatic fibrosis.…”

Section: Radiomics In the Field Of Point Of Care Us (Pocus)mentioning

confidence: 99%

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

et al. 2021

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Hepatocellular carcinoma (HCC) is a common cancer worldwide. Recent international guidelines request an identification of the stage and patient background/condition for an appropriate decision for the management direction. Radiomics is a technology based on the quantitative extraction of image characteristics from radiological imaging modalities. Artificial intelligence (AI) algorithms are the principal axis of the radiomics procedure and may provide various results from large data sets beyond conventional techniques. This review article focused on the application of the radiomics-related diagnosis of HCC using radiological imaging (computed tomography, magnetic resonance imaging, and ultrasound (B-mode, contrast-enhanced ultrasound, and elastography)), and discussed the current role, limitation and future of ultrasound. Although the evidence has shown the positive effect of AI-based ultrasound in the prediction of tumor characteristics and malignant potential, posttreatment response and prognosis, there are still a number of issues in the practical management of patients with HCC. It is highly expected that the wide range of applications of AI for ultrasound will support the further improvement of the diagnostic ability of HCC and provide a great benefit to the patients.

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Enhanced Point‐of‐Care Ultrasound Applications by Integrating Automated Feature‐Learning Systems Using Deep Learning

Cited by 54 publications

References 46 publications

Stakeholder Perceptions of Point-of-Care Ultrasound Implementation in Resource-Limited Settings

Stakeholder Perceptions of Point-of-Care Ultrasound Implementation in Resource-Limited Settings

Lung ultrasound for early diagnosis and severity assessment of pneumonia in patients with coronavirus disease 2019

AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound

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