Clinical Application of Multiple Vital Signs-Based Infection Screening System in a Mongolian Hospital: Optimization of Facial Temperature Measurement by Thermography at Various Ambient Temperature Conditions Using Linear Regression Analysis

Dagdanpurev, Sumiyakhand; Sun, Guanghao; Choimaa, Lodoiravsal; Abe, Shigeto; Matsui, Takemi

doi:10.1109/embc.2018.8513513

Cited by 8 publications

(5 citation statements)

References 8 publications

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“…They obtained a classification of the disease with a sensitivity of 97.1% and a specificity of 81.3%. Dagdanpurev et al [ 60 ] proposed a model for infection screening at various ambient temperatures. Combining a linear regression analysis, to account for environmental thermal condition, with a k-nearest neighbor algorithm, a sensitivity of 91% and NPV of 92% in fever detection were obtained.…”

Section: Machine Learning Applications For Respiratory Diseases Asmentioning

confidence: 99%

An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies

Perpetuini

Filippini

Cardone

et al. 2021

IJERPH

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Infrared thermal imaging (IRI) is a contact-less technology able to monitor human skin temperature for biomedical applications and in real-life contexts. Its capacity to detect fever was exploited for mass screening during past epidemic emergencies as well as for the current COVID-19 pandemic. However, the only assessment of fever may not be selective for the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Hence, novel approaches for IRI data analysis have been investigated. The present review aims to describe how IRI have been employed during the last epidemics, highlighting the potentialities and the limitations of this technology to contain the contagions. Specifically, the methods employed for automatic face recognition and fever assessment and IRI’s performances in mass screening at airports and hospitals are reviewed. Moreover, an overview of novel machine learning methods for IRI data analysis, aimed to identify respiratory diseases, is provided. In addition, IRI-based smart technologies developed to support the healthcare during the COVID-19 pandemic are described. Finally, relevant guidelines to fully exploit IRI for COVID-19 identification are defined, to improve the effectiveness of IRI in the detection of the SARS-CoV-2 infection.

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Section: Machine Learning Applications For Respiratory Diseases Asmentioning

confidence: 99%

An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies

Perpetuini

Filippini

Cardone

et al. 2021

IJERPH

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“…As of now, traditional approaches 136 are the most common (n=17). Given the general trend towards deep learning in computer vision, it comes to no surprise that this tool has found use in camera-based vital sign estimation (n=7) 94 , 137 138 139 . Deep learning has proven useful for image classification and segmentation in several fields, including medical image analysis 140 .…”

Section: Survey Taxonomymentioning

confidence: 99%

A Broader Look: Camera-Based Vital Sign Estimation across the Spectrum

Antink

Lyra²,

Paul

et al. 2019

Yearb Med Inform

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Objectives: Camera-based vital sign estimation allows the contactless assessment of important physiological parameters. Seminal contributions were made in the 1930s, 1980s, and 2000s, and the speed of development seems ever increasing. In this suivey, we aim to overview the most recent works in this area, describe their common features as well as shortcomings, and highlight interesting “outliers”. Methods: We performed a comprehensive literature research and quantitative analysis of papers published between 2016 and 2018. Quantitative information about the number of subjects, studies with healthy volunteers vs. pathological conditions, public datasets, laboratory vs. real-world works, types of camera, usage of machine learning, and spectral properties of data was extracted. Moreover, a qualitative analysis of illumination used and recent advantages in terms of algorithmic developments was also performed. Results: Since 2016, 116 papers were published on camera-based vital sign estimation and 59% of papers presented results on 20 or fewer subjects. While the average number of participants increased from 15.7 in 2016 to 22.9 in 2018, the vast majority of papers (n=100) were on healthy subjects. Four public datasets were used in 10 publications. We found 27 papers whose application scenario could be considered a real-world use case, such as monitoring during exercise or driving. These include 16 papers that dealt with non-healthy subjects. The majority of papers (n=61) presented results based on visual, red-green-blue (RGB) information, followed by RGB combined with other parts of the electromagnetic spectrum (n=18), and thermography only (n=12), while other works (n=25) used other mono- or polychromatic non-RGB data. Surprisingly, a minority of publications (n=39) made use of consumer-grade equipment. Lighting conditions were primarily uncontrolled or ambient. While some works focused on specialized aspects such as the removal of vital sign information from video streams to protect privacy or the influence of video compression, most algorithmic developments were related to three areas: region of interest selection, tracking, or extraction of a one-dimensional signal. Seven papers used deep learning techniques, 17 papers used other machine learning approaches, and 92 made no explicit use of machine learning. Conclusion: Although some general trends and frequent shortcomings are obvious, the spectrum of publications related to camera-based vital sign estimation is broad. While many creative solutions and unique approaches exist, the lack of standardization hinders comparability of these techniques and of their performance. We believe that sharing algorithms and/ or datasets will alleviate this and would allow the application of newer techniques such as deep learning.

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“…T is measured from the neck skin by an infrared thermometer (MLX90614, Melexis NV). Infrared technology uses infrared emissions from the human skin, which enables simple, rapid, and noncontact temperature measurement [17]. Physical signals are processed from these biosensors and calculated as numerical values by an Arduino Nano microprocessor with software that was developed based on LabVIEW graphical user interface software (National Instruments, Texas, USA).…”

Section: Pediatric Pneumonia Screening System Using Hr Rr and Tmentioning

confidence: 99%

Random tree algorithm-based pediatric pneumonia screening using heart rate, respiration rate and temperature

Dagdanpurev¹,

Tsogzolmaa²,

Sun³

et al. 2019

JBRCI

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Despite a reduction in pneumonia-related mortality, pneumonia remains a leading cause of death among children aged 0-5 years. Most of these deaths occur in developing countries. However, more than half of pneumonia-related deaths are preventable with improved facilities and health strategies. Early rapid diagnosis is important to decrease pneumonia mortality. We developed a portable, cost-effective, rapid pneumonia screening system using a random tree algorithm to support early detection of pneumonia in children. We enrolled 105 participants: 57 patients aged 1-13 years (33 boys, 24 girls) diagnosed with pneumonia by chest radiograph and 48 normal volunteers aged 2-14 years (25 boys, 22 girls). We conducted a clinical trial in the Bayangol District Geriatric and Pediatric Hospital, Ulaanbaatar, Mongolia from January 12-19, 2019. Our screening system measured heart rate, respiration rate and skin temperature within 10 seconds and used a random tree algorithm to distinguish patients with pneumonia and normal volunteers. The system uses a photosensor, Doppler radar, and infrared thermophile to determine vital signs and an Arduino Nano microprocessor to perform computations. Paired t-tests were used to compare vital signs between patients with pneumonia and normal volunteers. The random tree algorithm achieved sensitivity of 96.5%, specificity of 81.3%, positive predictive value of 85.9%, and negative predictive value of 95.1%. The paired t-tests showed strong statistically significant differences in all three vital signs between patients with pneumonia and normal volunteers. Our random tree algorithm-based screening system offers an effective, rapid, and convenient tool for early detection of pneumonia in children. Its cost-effectiveness enables application in low-income countries. The system measures multiple vital signs simultaneously within 10 seconds, which may be useful for initial physical examinations in pediatric hospitals.

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Clinical Application of Multiple Vital Signs-Based Infection Screening System in a Mongolian Hospital: Optimization of Facial Temperature Measurement by Thermography at Various Ambient Temperature Conditions Using Linear Regression Analysis

Cited by 8 publications

References 8 publications

An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies

An Overview of Thermal Infrared Imaging-Based Screenings during Pandemic Emergencies

A Broader Look: Camera-Based Vital Sign Estimation across the Spectrum

Random tree algorithm-based pediatric pneumonia screening using heart rate, respiration rate and temperature

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