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

2021 ◽  
pp. 155335062110186
Author(s):  
Abdel-Moneim Mohamed Ali ◽  
Emran El-Alali ◽  
Adam S. Weltz ◽  
Scott T. Rehrig
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Point Of Care ◽  
Prognostic Indicators ◽  
Data Repository ◽  
Point Of Care Ultrasound ◽  
Ultrasound Images ◽  
Clinical Protocol ◽  
Improved Outcomes ◽  
Current Experience

Current experience suggests that artificial intelligence (AI) and machine learning (ML) may be useful in the management of hospitalized patients, including those with COVID-19. In light of the challenges faced with diagnostic and prognostic indicators in SARS-CoV-2 infection, our center has developed an international clinical protocol to collect standardized thoracic point of care ultrasound data in these patients for later AI/ML modeling. We surmise that in the future AI/ML may assist in the management of SARS-CoV-2 patients potentially leading to improved outcomes, and to that end, a corpus of curated ultrasound images and linked patient clinical metadata is an invaluable research resource.

scholarly journals Artificial Intelligence in POCUS: The Vanguard of Technology in COVID-19 Pandemic

2020 ◽  
Vol 4 (02) ◽  
pp. 116-120
Author(s):  
Srinath Damodaran ◽  
Arjun Alva ◽  
Srinath Kumar ◽  
Muralidhar Kanchi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real Time ◽  
Point Of Care ◽  
Learning Technologies ◽  
Point Of Care Ultrasound ◽  
Intelligent Software ◽  
Physiologic Responses ◽  
Insight Into

AbstractThe creation of intelligent software or system, machine learning, and deep learning technologies are the integral components of artificial intelligence. Point-of-care ultrasound involves the bedside use of ultrasound to answer specific diagnostic questions and to assess real-time physiologic responses to treatment. This article provides insight into the pearls and pitfalls of artificial intelligence in point-of-care ultrasound for the coronavirus disease 2019 pandemic.

512 Bedside Transthoracic echography volume Assessment in the BICU

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S103-S103
Author(s):  
Michael G Chambers ◽  
Britton Garrett ◽  
Leopoldo C Cancio
Keyword(s):  
Point Of Care ◽  
Injured Patient ◽  
Bedside Ultrasound ◽  
Point Of Care Ultrasound ◽  
Ultrasound Images ◽  
Advanced Practice ◽  
Multidisciplinary Rounds ◽  
Volume Assessment ◽  
Us Findings ◽  
Image Repository

Abstract Introduction Point-of-Care Ultrasound (POCUS) has been shown to be a useful adjunct in assessment of various shock states and utilized to guide resuscitative and post-resuscitation de-escalation efforts. POCUS use for guiding resuscitation in burn injured patient has not be described. Objectives characterize the use of bedside ultrasound examinations performed by advance practice providers and treating physicians in a regional burn intensive care unit Methods Daily beside ultrasound examinations were performed utilizing a bedside ultrasound device by an advanced practice provider prior to rounds POCUS examinations consist of: Ultrasound images were archived to a centralized image repository and reviewed daily during multi-disciplinary rounds. Ultrasonographic volume assessment compared to clinical volume assessment made during daily multidisciplinary rounds. Results 100 examinations were performed of those 32 were within the initial 72 hour window: Conclusions Our results demonstrate that bedside ultrasound aides in guidance of both resuscitative and post-resuscitative efforts. We identified a cohort of patients who appeared hypervolemic clinically but US findings supported hypovolemia, we refer to as pseudohypervolemia US volume assessment provides information that changes management. We believe point of care ultrasound is a viable tool in preventing over-resuscitation as well as to guide post-resuscitative diuresis.

scholarly journals Clinical measurements obtained from point-of-care ultrasound images to assess acquisition skills

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Brian P. Lucas ◽  
Antonietta D’Addio ◽  
Clay Block ◽  
Harold L. Manning ◽  
Brian Remillard ◽  
...  
Keyword(s):  
Point Of Care ◽  
Point Of Care Ultrasound ◽  
Ultrasound Images ◽  
Clinical Measurements

scholarly journals Rural point-of-care ultrasound of the kidney and bladder: quality and effect on patient management

2018 ◽  
Vol 10 (4) ◽  
pp. 324 ◽  
Author(s):  
Garry Nixon ◽  
Katharina Blattner ◽  
Jill Muirhead ◽  
Ngaire Kerse
Keyword(s):  
Patient Care ◽  
Urinary Retention ◽  
Point Of Care ◽  
Renal Stones ◽  
Point Of Care Ultrasound ◽  
Ultrasound Images ◽  
Positive Contribution ◽  
Diagnostic Certainty ◽  
Negative Effect ◽  
Clinical Records

ABSTRACT INTRODUCTION Point-of-care ultrasound (POCUS) of the kidney and bladder are among the most commonly performed POCUS scans in rural New Zealand (NZ). AIM To determine the quality, safety and effect on patient care of POCUS of the kidney and bladder in rural NZ. METHODS Overall, 28 doctors in six NZ rural hospitals completed a questionnaire both before and after undertaking a POCUS scan over a 9-month period. The clinical records and saved ultrasound images were reviewed by a specialist panel. RESULTS The 28 participating doctors undertook 138 kidney and 60 bladder scans during the study. POCUS of the bladder as a test for urinary retention had a sensitivity of 100% (95% CI 88–100) and specificity of 100% (95% CI 93–100). POCUS of the kidney as a test for hydronephrosis had a sensitivity 90% (95% CI 74–96) and specificity of 96% (95% CI 89–98). The accuracy of other findings such as renal stones and bladder clot was lower. POCUS of the bladder appeared to have made a positive contribution to patient care in 92% of cases without evidence of harm. POCUS of the kidney benefited 93% of cases, although in three cases (2%), it may have had a negative effect on patient care. DISCUSSION POCUS as a test for urinary retention and hydronephrosis in the hands of rural doctors was technically straightforward, improved diagnostic certainty, increased discharges and overall had a positive effect on patient care.

Lung Ultrasound as a New Technique for Diagnosis of Neonatal Respiratory Diseases

2018 ◽  
Vol 37 (4) ◽  
pp. 224-232 ◽  
Author(s):  
Yasser N. Elsayed
Keyword(s):  
Lung Disease ◽  
Lung Ultrasound ◽  
Point Of Care ◽  
Point Of Care Ultrasound ◽  
Ultrasound Images ◽  
X Ray ◽  
The Past ◽  
Neonatal Lung ◽  
Chest X Ray ◽  
A New Technique

Point-of-care ultrasound in the NICU is becoming more commonplace and is now used for a number of indications. Over the past ten years, the use of ultrasound as an alternative to a chest x-ray for the diagnosis of neonatal lung disease has been explored, and protocols were developed to refine the interpretation of ultrasound images in neonatal lung disease. The purpose of this column is to briefly explain the physics of ultrasound and describe the application of ultrasound to neonatal lung assessment.

scholarly journals P83 Online Multidisciplinary Review of Point of Care Ultrasound Images During the COVID-19 Pandemic

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
B A Shuker ◽  
J Perry ◽  
Benjamin Shuker
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Multidisciplinary Team ◽  
Point Of Care ◽  
Anticoagulation Therapy ◽  
Vena Cava ◽  
Point Of Care Ultrasound ◽  
Ultrasound Images ◽  
Thoracic Imaging ◽  
Online Platform

Abstract   Point-of-care-ultrasound (POCUS) is a valuable diagnostic tool in intensive care. Evaluation of POCUS images acquired in our intensive care unit (ICU) prior to the COVID-19 pandemic had typically been performed solely at the point of care. Where further evaluation was required, cross-sectional thoracic imaging or departmental echocardiography would be requested. Clinicians also had access to ICU ultrasound machines for review of images, or to repeat studies for clarification of findings. However, the nature of the pandemic limited access to ICU to minimise contact with COVID-19. Objectives We aimed to develop an online solution for review of POCUS images by the multidisciplinary team (MDT). Methods Microsoft Teams was utilised to create a dedicated channel for the MDT to review POCUS images. Images were exported from ultrasound machines used inside our ICU to portable USB drives in standard formats (DICOM or WMV). The portable USB drives were decontaminated prior to transfer outside of the ICU. Anonymised images were uploaded with relevant clinical details to the Teams platform for MDT review. Results The online platform provided rapid access to images for review by the MDT. POCUS images from ICU patients with and without COVID-19 were reviewed. MDT review frequently led to a change in patient management. Significant examples included identification of a missed inferior vena cava thrombus leading to initiation of anticoagulation therapy, and rapid expert input for a case of cardiac tamponade. Conclusion The use of an online platform allowed our intensive care unit to establish a reliable method for images acquired from point-of-care-ultrasound to be remotely reviewed by an expert multidisciplinary team, consequently improving patient care.

scholarly journals Autonomous Robotic Point-of-Care Ultrasound Imaging for Monitoring of COVID-19–Induced Pulmonary Diseases

2021 ◽  
Vol 8 ◽  
Author(s):  
Lidia Al-Zogbi ◽  
Vivek Singh ◽  
Brian Teixeira ◽  
Avani Ahuja ◽  
Pooyan Sahbaee Bagherzadeh ◽  
...  
Keyword(s):  
Force Feedback ◽  
Close Contact ◽  
Point Of Care ◽  
Ct Scans ◽  
Surface Model ◽  
Point Of Care Ultrasound ◽  
Ultrasound Images ◽  
Ultrasound Probe ◽  
Optimal Position ◽  
Health Crisis

The COVID-19 pandemic has emerged as a serious global health crisis, with the predominant morbidity and mortality linked to pulmonary involvement. Point-of-Care ultrasound (POCUS) scanning, becoming one of the primary determinative methods for its diagnosis and staging, requires, however, close contact of healthcare workers with patients, therefore increasing the risk of infection. This work thus proposes an autonomous robotic solution that enables POCUS scanning of COVID-19 patients’ lungs for diagnosis and staging. An algorithm was developed for approximating the optimal position of an ultrasound probe on a patient from prior CT scans to reach predefined lung infiltrates. In the absence of prior CT scans, a deep learning method was developed for predicting 3D landmark positions of a human ribcage given a torso surface model. The landmarks, combined with the surface model, are subsequently used for estimating optimal ultrasound probe position on the patient for imaging infiltrates. These algorithms, combined with a force–displacement profile collection methodology, enabled the system to successfully image all points of interest in a simulated experimental setup with an average accuracy of 20.6 ± 14.7 mm using prior CT scans, and 19.8 ± 16.9 mm using only ribcage landmark estimation. A study on a full torso ultrasound phantom showed that autonomously acquired ultrasound images were 100% interpretable when using force feedback with prior CT and 88% with landmark estimation, compared to 75 and 58% without force feedback, respectively. This demonstrates the preliminary feasibility of the system, and its potential for offering a solution to help mitigate the spread of COVID-19 in vulnerable environments.

Role of Emerging Technologies in COVID 19: Analyses, Predictions, and Future Countermeasures (Preprint)

2020 ◽  
Author(s):  
Dr. Rekha G
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Language Processing ◽  
Emerging Technologies ◽  
Point Of Care ◽  
Cost Effective ◽  
Current Status ◽  
Future Research ◽  
Smart Healthcare

UNSTRUCTURED In the resent decade, emerging technologies like Artificial Intelligence, Blockchain Technology, Cloud Computing , Internet of Things (IoT), etc., have changed people life a lot (in terms of living). Artificial Intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which is currently happening around the globe.We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. For this, a summary of COVID-19 related data sources that are available for research purposes (for future researchers) is also presented.For that, all the tools, resources and datasets needed to facilitate AI research are also been reviewed. Also discussed about Machine Learning use cases for Drug Formulations, Treatment of Patients Suffering with COVID-19, how Artificial Intelligence and internet of things can be useful to develop Cost- effective and Rapid Point-of-Care Diagnostics. For example, uses of Internet of Medical Things for Smart Healthcare (primary focus on detecting COVID-19 symptoms, and alerts for other users) have been discussed in this work. In summary, this work providesuseful information about (potential of) AI methods, machine learning, internet of things, used in many applications like Medicare, COVID-19 outbreak and summarizes several critical roles of Artificial Intelligence (including machine learning and internet of things) research in this unprecedented battle.We also discuss several future Research directions, global impact of corona on internet of things and many applications. It is envisaged that this work will provide AI, and ML researchers and the wider community an overview of the current status of AI and ML applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

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