Analysis of IR thermal imager for mass blind fever screening.
BACKGROUND
Obtaining meaningful temperature for the human body requires identifying a body site that will provide reliable data across a large population. It is important to understand that skin temperature does not solely depend on body-core temperature and may be affected by other physiological and environmental factors. Currently, there is lack of empirical data in correlating facial surface temperature with body core temperature. Present IR systems in use at airports/immigration checkpoints have not been scientifically validated particularly in regards to the false-negative rate. As a result, they may create a false sense of security by underestimating the number of febrile (and possibly infected) individuals. This article evaluates the effectiveness of thermal scanner when it is being used for mass blind screening of potential fever subjects such as SARS or bird flu patients.


METHODS
Bio-statistics with regression analysis and ROC is applied to analyse the data collected (502) from the SARS hospital in Singapore and conclusive results are drawn from them. The results are vital in determining two very important pieces of information: the best and yet practical region on the face to take readings and optimal pre-set threshold temperature for the thermal imager.


RESULTS
(1) The thermal scanner can be used as a first line tool for the mass blind screening of hyperthermia, (2) the readings from the scanner suggest good correlation with the ear temperature readings, (3) an imager temperature threshold should be determined by the environmental factors, outdoor condition in particular, the physiological site offset and the performance characteristics of thermal imager to warrant the most accurate and reliable screening operation.


CONCLUSIONS
The analysis suggested that the thermal imager used holds much promise for mass blind screening when the readings from a specific region have a good correlation with the ear temperature. From the regression analysis, the best reading is taken from the maximum temperature in the eye region, followed by the maximum temperature in the forehead region. With ROC analysis, a randomly selected individual from the fever group has a test value larger than that for a randomly selected individual from the normal group in 97.2% of the time. The test can distinguish between the normal and febrile groups and an optimum threshold temperature for the thermal imager can be found. The pre-set threshold cut-off temperature for the current thermal imager was found to be 36.3 degrees C with reference to the associated environmental condition. Any temperature readings that exceed this reading will trigger off the alarm and a thermometer will be used to verify the whether the person is having fever.
Ng
Eddie Y K
EY
School of Mechanical and Production Engineering, Nanyang Tech University, Singapore 639798, Singapore. mykng@ntu.edu.sg
Kaw
G J L
GJ
Chang
W M
WM
D000328:Adult; D001831:Body Temperature; D003951:Diagnostic Errors; D005145:Face; D005334:Fever / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D007251:Influenza, Human / Q000175:diagnosis / Q000503:physiopathology; D007259:Infrared Rays; D008403:Mass Screening / Q000295:instrumentation / Q000379:methods / Q000706:statistics & numerical data; D012372:ROC Curve; D012044:Regression Analysis; D045169:Severe Acute Respiratory Syndrome / Q000175:diagnosis / Q000503:physiopathology; D013817:Thermography / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data