Abdominal imaging in ICU patients with viral pneumonia: Are findings in COVID-19 patients really different from those observed with non-SARS-CoV-2 viral pneumonia?

Reizine, Edouard; Mulé, Sebastien; Prost, Nicolas de; Deux, Jean-François; Kobeiter, Hicham; Luciani, Alain

doi:10.1016/j.redii.2022.01.001

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…In the past two decades, biosensors for disease diagnosis and healthcare applications have grown rapidly. These biosensors and ML methods are not commercially accessible for application in pandemic illnesses such as Covid'19, other pandemic diseases [17,18,19,20]. Biosensor-based diagnosis and prevention of ventilator-associated pneumonia have not been successfully adopted in hospitals.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED: A wireless body area network enabled machine learning-assisted biosensor framework for detecting and analyzing ventilator and hospital-acquired pneumonia using the internet of things

Baskar

2022

Preprint

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Ventilator-associated pneumonia is the leading cause of death in intensive care units (ICU). Hospital-acquired pneumonia is twice as common in non-ventilated patients as it is in ventilated patients, according to a recent study. Exhaled breath samples can be used to distinguish between ill and healthy individuals by identifying volatile organic compounds (VOCs) in the breath. A reliable and non-invasive method of diagnosing VOC-induced pneumonia is essential for prompt treatment and decreasing fatality rates. Researchers have used wearable biosensors and machine learning (ML) algorithms to diagnose pneumonia to analyze VOCs in exhaled air. Wireless body area networks (WBANs) offer the potential for wearable sensors and internet-enabled ICU monitoring when it comes to health monitoring. These results imply that a wearable biosensor-based IoT-based machine learning system may identify ventilator- and hospital-acquired pneumonia from exhaled VOC components. The developed device was deployed on an NVIDIA Jetson Nano graphical processing unit, allowing for the seamless and immediate transmission of volatile organic compound (VOC) data and other patient biological attributes such as temperature and blood oxygen saturation (SpO2) to the Amazon Web Services IoT Core. VOC and ML-aided techniques have proven that artificial intelligence can reliably discriminate pneumonia samples from control samples, and the SVM model outperforms the K Nearest Neighbors model in terms of accuracy (91.38%), sensitivity (92.7%), precision (92.8%), and receiver operating characteristic (ROC) (91.01 percent).

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Section: Related Workmentioning

confidence: 99%

RETRACTED: A wireless body area network enabled machine learning-assisted biosensor framework for detecting and analyzing ventilator and hospital-acquired pneumonia using the internet of things

Baskar

2022

Preprint

View full text Add to dashboard Cite

show abstract

Abdominal imaging in ICU patients with viral pneumonia: Are findings in COVID-19 patients really different from those observed with non-SARS-CoV-2 viral pneumonia?

Cited by 1 publication

References 26 publications

RETRACTED: A wireless body area network enabled machine learning-assisted biosensor framework for detecting and analyzing ventilator and hospital-acquired pneumonia using the internet of things

RETRACTED: A wireless body area network enabled machine learning-assisted biosensor framework for detecting and analyzing ventilator and hospital-acquired pneumonia using the internet of things

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