Is thermal scanner losing its bite in mass screening of fever due to SARS?
Open accessAbstract
Severe acute respiratory syndrome (SARS) is a highly infectious disease caused by a coronavirus. Screening to detect a potential SARS infected person plays an important role in preventing the spread of SARS. The use of infrared thermal imaging cameras has been proposed as a noninvasive, speedy, cost effective and fairly accurate means for mass blind screening of potential SARS infected persons. Infrared thermography provides a digital image showing temperature patterns. This has been previously utilized in the detection of inflammation and nerve dysfunctions. It is believed that IR cameras can potentially be used to detect subjects with fever, the cardinal symptom of SARS, and avian influenza. The accuracy of the infrared system can, however, be affected by human, environmental, and equipment variables. It is also limited by the fact that the thermal imager measures the skin temperature and not the core body temperature. As known, the body determines a temperature as its so-called "set point" at any one time during the body temperature regulation. Fever happens if the hypothalamus detects pyrogens and then raises the set point. The time course of a typical fever can be divided into three stages. When the fever initiates, the body attempts to raise its temperature but vasoconstriction occurs to prevent heat loss through the skin. With this reason, some individuals at this stage of fever (at the rising slope and immediately after fever begins or falling slope after the fever breaks) will not be detected by the scanner if it is not designed to detect subject at the plateau of the fever (with her/his high core temperature) in particular. This paper aims to study the effectiveness of infrared systems for its application in mass blind screening to detect subjects with elevated body temperature. For this application, it is critical for thermal imagers to be able to identify febrile from normal subjects accurately. Minimizing the number of false positive and false negative cases, improves the efficiency of the screening stations. False negative results should be avoided at all costs, as letting a SARS infected person through the screening process may result in potentially catastrophic results. Various statistical methods such as linear regression, Receiver Operating Characteristics analysis, and neural networks based classification were used to analyze the temperature data collected from various sites on the face on both the frontal and side profiles. Two important conclusions were drawn from the analysis: the best region on the face to obtain temperature readings and the optimal preset threshold temperature for the thermal imager. To conclude, the current research application will remain an interest and useful for reference by both local and overseas manufacturers of thermal scanners, users, and various government and private establishments. As elevation of body temperature is a common presenting symptom for many illnesses including infectious diseases, thermal imagers are useful tools for mass screening of body temperature not only for SARS but also during other public health crisis where widespread transmission of infection is a concern.
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A single temperature measurement can indicate whether a patient has fever or hypothermia.
“A single temperature measurement can tell us whether the patient has a fever or hypothermia [150,151,152,153,154].”
From The Current State of Optical Sensors in Medical Wearables (2022) · cites this paper as [154]
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- Authors
- Ng, E. Y. ‐.
- Venue
- Medical Physics
- Publisher
- Wiley
- Published
- 2004-12-16
- Volume
- 32
- Issue
- 1
- Pages
- 93-97
- ISSN
- 0094-2405 (ISSN-L)
- 2473-4209
- PMC
- PMC7168465
- OpenAlex
- W1986974549
- Cited by
- 86
- References
- 7
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 5 · 2022: 4 · 2021: 5 · 2020: 11 · 2019: 2 · 2018: 4 · 2017: 7 · 2016: 2 · 2015: 3 · 2014: 3 · 2013: 7 · 2012: 6
- FWCI
- 1.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Medical Physics (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Infrared Thermography in Medicine
- Physical Sciences › Engineering › Mechanics of Materials › Thermography and Photoacoustic Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Measurement and Detection Methods
- Keywords
- SARS, infrared thermography, fever screening, mass screening, body temperature measurement, skin temperature, core body temperature, false positive and false negative detection, avian influenza, neural network classification, receiver operating characteristic, linear regression, thermoregulation, SARS-CoV
- MeSH terms
- Body Temperature, Body Temperature Regulation, Disease Outbreaks, False Negative Reactions, False Positive Reactions, Fever, Humans, Inflammation, Infrared Rays, Mass Screening, Regression Analysis, ROC Curve, Sensitivity and Specificity, Skin Temperature, Temperature, Neural Networks, Computer, Severe Acute Respiratory Syndrome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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