Numerical computation as a tool to aid thermographic interpretation

Long-Term Skin Temperature Changes after Breast Cancer Radiotherapy

The aim of the study was to use thermal imaging to evaluate long-term chest temperature changes in patients who had previously been treated with radiotherapy. The examination with a thermal imaging camera involved 144 women—48 of them were patients after RT, 48 were females before breast cancer radiotherapy and the last group of participants were 48 healthy women. All patients (before and after radiotherapy) were divided into women after mastectomy and those after conservative surgery. In addition, the first group of women, those who had received radiotherapy, were divided into three other groups: up to 1 year after RT, over 1 year and up to 5 years after RT and over 5 years after RT. Due to this, it was possible to compare the results and analyse the differences between the temperature in the healthy and treated breasts. The comparison of obtained temperature results showed that the area treated by ionizing radiation is characterized by a higher temperature even a few years after the finished treatment. It is worth mentioning that despite the fact that the difference was visible on the thermograms, the patients had no observable skin lesion or change in color at the treatment site. For the results of the study provided for the group of healthy patients, there were no significant differences observed between the average temperatures in the breasts. The use of thermal imaging in the evaluation of skin temperature changes after radiotherapy showed that the average temperature in the treated breast area can change even a long time after treatment.

Twelve-Month Evaluation of Temperature Effects of Radiotherapy in Patients after Mastectomy

The aim of this study was to verify the changes in the temperature distribution within the breast at twelve months after the end of radiotherapy for breast cancer. The study included twenty-four women. The first test group consisted of twelve women who underwent breast mastectomy and qualified for radiotherapy according to standard medical treatment procedures. The second group included twelve healthy women. The tests were conducted before treatment with radiation therapy and two months, six months, nine months, and one year after the end of treatment. The mean temperature values changed depending on the time that had elapsed since the end of treatment. The highest temperature increase in all patients was observed six months after the end of radiotherapy. This research has confirmed that the assessment of temperature changes in the breast area after radiotherapy can evaluate the severity and lesions in the time course of the radiation reaction.

Breast cancer detection from thermal images using a Grunwald-Letnikov-aided Dragonfly algorithm-based deep feature selection method

Breast cancer is one of the deadliest diseases in women and its incidence is growing at an alarming rate. However, early detection of this disease can be life-saving. The rapid development of deep learning techniques has generated a great deal of interest in the medical imaging field. Researchers around the world are working on developing breast cancer detection methods using medical imaging. In the present work, we have proposed a two-stage model for breast cancer detection using thermographic images. Firstly, features are extracted from images using a deep learning model, called VGG16. To select the optimal subset of features, we use a meta-heuristic algorithm called the Dragonfly Algorithm (DA) in the second step. To improve the performance of the DA, a memory-based version of DA is proposed using the Grunwald-Letnikov (GL) method. The proposed two-stage framework has been evaluated on a publicly available standard dataset called DMR-IR. The proposed model efficiently filters out non-essential features and had 100% diagnostic accuracy on the standard dataset, with 82% fewer features compared to the VGG16 model.

The Use of Thermal Imaging in the Evaluation of Temperature Effects of Radiotherapy in Patients after Mastectomy-First Study

The aim of the study was to evaluate the temperature parameter of the breast area in patients undergoing radiotherapy at various intervals. The relationship between temperature changes on the patient’s skin and the time after the end of radiotherapy was studied. Measurements with a thermal imaging camera were performed in a group of twelve volunteers. Six of them were healthy women who did not have thermal asymmetry between the breasts, whereas six were diagnosed with breast cancer and underwent mastectomy due to the advanced stage of the disease. The patients were qualified for radiation therapy. Thermographic examinations were performed before treatment, two months later and then six months after the end of the treatment. Temperature differences between the healthy breasts and the treated areas were assessed. Additionally, the correlation between a patient’s skin temperature changes and the time after the end of radiotherapy was analyzed. The highest skin temperature increase (1.47 °C) was observed 6 months after the end of RT compared to the measurement before treatment. It seems that thermovision may bring a new tool for quantitative analyses of the temperature effects of radiotherapy.

Correlation between Isotherms and Isodoses in Breast Cancer Radiotherapy-First Study

The study is focused on correlation of isotherms derived from thermal images with an isodoses describing treatment plan for patients with breast cancer treated by radiotherapy. The irradiated area covered the part of the body after mastectomy. The study included patients diagnosed with breast cancer who were qualified for radiotherapy treatment. All patients were monitored during each treatment week during the entire radiotherapy process. The measurements were made under strictly defined conditions. In the treatment planning system (TPS), the specific plan was created for each patient. Spatial dose distribution in the patient's body was obtained and presented by the isodoses (lines connecting points with the same dose values). The following areas from the treatment planning system were plotted on the thermograms: target (tumor area) and isodose: 45 Gy, 40 Gy, 30 Gy, 20 Gy and 10 Gy. The obtained results indicated a high correlation between magnitude of the dose represented as the isodose and the temperature of the treated skin. Moreover, preliminary analysis showed a repeatable increase of the mean temperature in the irradiated area during the treatment.

Millikelvin-resolved ambient thermography

Temperature sensitivity of thermography is boosted by over 15 times to achieve millikelvin-resolution near ambient temperature.

An introduction to the Cyrcadia Breast Monitor: A wearable breast health monitoring device

BACKGROUND

The most common breast cancer detection modalities are generally limited by radiation exposure, discomfort, high costs, inter-observer variabilities in image interpretation, and low sensitivity in detecting cancer in dense breast tissue. Therefore, there is a clear need for an affordable and effective adjunct modality that can address these limitations. The Cyrcadia Breast Monitor (CBM) is a non-invasive, non-compressive, and non-radiogenic wearable device developed as an adjunct to current modalities to assist in the detection of breast tissue abnormalities in any type of breast tissue.

METHODS

The CBM records thermodynamic metabolic data from the breast skin surface over a period of time using two wearable biometric patches consisting of eight sensors each and a data recording device. The acquired multi-dimensional temperature time series data are analyzed to determine the presence of breast tissue abnormalities. The objective of this paper is to present the scientific background of CBM and also to describe the history around the design and development of the technology.

RESULTS

The results of using the CBM device in the initial clinical studies are also presented. Twenty four-hour long breast skin temperature circadian rhythm data was collected from 93 benign and 108 malignant female study subjects in the initial clinical studies. The predictive model developed using these datasets could differentiate benign and malignant lesions with 78% accuracy, 83.6% sensitivity and 71.5% specificity. A pilot study of 173 female study subjects is underway, in order to validate this predictive model in an independent test population.

CONCLUSIONS

The results from the initial studies indicate that the CBM may be valuable for breast health monitoring under physician supervision for confirmation of any abnormal changes, potentially prior to other methods, such as, biopsies. Studies are being conducted and planned to validate the technology and also to evaluate its ability as an adjunct breast health monitoring device for identifying abnormalities in difficult-to-diagnose dense breast tissue.

Thermal Imaging - An Emerging Modality for Breast Cancer Detection: A Comprehensive Review

Breast cancer is not preventable. To reduce the death rate and improve the survival chances of breast cancer patients, early and accurate detection is the only panacea. Delay in diagnosis of this disease causes 60% of deaths. Thermal imaging is a low-risk modality for early breast cancer decision making without injecting any form of energy into the human body. Thermography as a screening tool was first introduced and well accepted in 1956. However, a study in 1977 found that it lagged behind other screening tools and is subjective. Soon after, its use was discontinued. This review discusses various screening tools used to detect breast cancer with a focus on thermography along with their advantages and shortcomings. With the maturation of thermography equipment and technological advances, this technique is emerging and has become the refocus of many biomedical researchers across the globe in the past decade. This study dispenses an exhaustive review of the work done related to interpretation of breast thermal variations and confers the discipline, frameworks, and methodologies used by different authors to diagnose breast cancer. Different performance metrics like accuracy, specificity, and sensitivity have also been examined. This paper outlines the most pressing research gaps for future work to improvise the accuracy of results for diagnosis of breast abnormalities using image processing tools, mathematical modelling and artificial intelligence. However, supplementary research is needed to affirm the potential of this technology for predicting breast cancer risk effectively. Altogether, our findings inform that it is a promising research problem and a potential solution for early detection of breast cancer in younger women.

Determining the thermal characteristics of breast cancer based on high-resolution infrared imaging, 3D breast scans, and magnetic resonance imaging

For over the three decades, various researchers have aimed to construct a thermal (or bioheat) model of breast cancer, but these models have mostly lacked clinical data. The present study developed a computational thermal model of breast cancer based on high-resolution infrared (IR) images, real three-dimensional (3D) breast surface geometries, and internal tumor definition of a female subject histologically diagnosed with breast cancer. A state-of-the-art IR camera recorded IR images of the subject’s breasts, a 3D scanner recorded surface geometries, and standard diagnostic imaging procedures provided tumor sizes and spatial locations within the breast. The study estimated the thermal characteristics of the subject’s triple negative breast cancer by calibrating the model to the subject’s clinical data. Constrained by empirical blood perfusion rates, metabolic heat generation rates reached as high as 2.0E04 W/m³ for normal breast tissue and ranged between 1.0E05–1.2E06 W/m³ for cancerous breast tissue. Results were specific to the subject’s unique breast cancer molecular subtype, stage, and lesion size and may be applicable to similar aggressive cases. Prior modeling efforts are briefly surveyed, clinical data collected are presented, and finally thermal modeling results are presented and discussed.

Simultaneous localization of multiple tumors from thermogram of tissue phantom by using a novel optimization algorithm inspired by hunting dogs

The objective of this study is to couple the contact thermography method with a novel optimization algorithm to rapidly detect and localize the soft tissue tumor. To this end, experiments are carried out on tissue-mimicking phantoms containing resistance heaters to simulate the embedded tumors. An examiner robot is used to measure the temperature of the tissue surface. The time required for the examination of the tissue surface is reduced by developing a novel optimization algorithm called the Hunter Algorithm (HA). In the HA, population individuals are called the hunters, and the global maximum is referred to as the prey. The maximum temperature occurs at the location of the tumor. By the end of the hunting procedure, a flock of hunters converges to the maximum temperature and reaches the tumor while the examination time is significantly reduced. Performance of the HA is evaluated by applying the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm to 11 test functions as minimization problems. It is observed that for the Ackley's function, as an example, the HA finds the global minimum after the 10th iteration with an accuracy of 10^-4, while the PSO converges with the same accuracy after the 30th iteration and the accuracy of the GA remains about 0.002. In addition, the results show that the contact thermography in conjunction with the HA is of clinical importance in accurate detection of multiple tumors and small and deeply located tumors with insignificant thermal effects on the tissue surface.

THERMAL IMAGING AS AN ADJUNCT TOOL FOR IDENTIFYING FETAL GROWTH – A PILOT STUDY

Monitoring the fetal growth and diagnosing any possible abnormality plays a vital role in ensuring the healthy growth of a fetus. Certain health issues like Hyperthermia, Premature Rupture of Membranes (PROM) and Intrauterine Growth Restriction (IUGR) has to be diagnosed early. A pilot study comprising of 27 pregnant and 2 non-pregnant subjects was conducted to check the effectiveness of Thermal imaging in predicting the fetal growth. The heat dissipated by the fetus to the maternal abdominal wall is acquired as a surface thermal distribution. These images were processed qualitatively and quantitatively for better understanding. There was a consistent higher thermal pattern for pregnant women. A more pronounced temperature pattern is notable in the umbilical region that correlates with gestation age. However, as thermal pattern varies with age, gestation period and BMI, it is advisable to track the same person and compare the images for better assessment. This pilot study justifies the need for more elaborate study in building a database for classification and interpretation of thermogram to detect fetal abnormality with reduced human interpretation.

Preliminary results of a new approach for three-dimensional reconstruction of Dynamic AngioThermography (DATG) images based on the inversion of heat equation

Dynamic AngioThermography (DATG) is a contact-plate technique capable of producing a digital representation of breast vascularity. The inception and growth of a tumor are associated with neoangenesis, which may result in a demonstrable alteration in the regional blood flow, while in normal health conditions the vascularity remains unchanged throughout life. DATG, if included in the clinical evaluation for breast cancer, could potentially improve the accuracy of the diagnosis of this disease. Conventional DATG is limited, however, in that it is a projection (i.e. two-dimensional) imaging technique that does not provide any information on the depth and its effect on the pattern of the perfusion revealed by this technique. In fact, the blood pattern is detected by projecting temperature signals on the plate, thus acquiring a digital two-dimensional image. In this article we propose a new approach for extracting information on depth through the inversion of the Fourier heat equation. The idea is to extract the information along the third axis while acquiring and analyzing the temporal sequence during the process of image formation. The method implemented has been tested on a dedicated "electric phantom" and in one in vivo experiment. In spite of the limits of these preliminary tests, the experimental results have shown that this method makes it possible to obtain a 3D representation of the vascularity. Although it appears to be promising, further validation and characterization of our technique are required.

Parameter estimation of breast tumour using dynamic neural network from thermal pattern

This article presents a new approach for estimating the depth, size, and metabolic heat generation rate of a tumour. For this purpose, the surface temperature distribution of a breast thermal image and the dynamic neural network was used. The research consisted of two steps: forward and inverse. For the forward section, a finite element model was created. The Pennes bio-heat equation was solved to find surface and depth temperature distributions. Data from the analysis, then, were used to train the dynamic neural network model (DNN). Results from the DNN training/testing confirmed those of the finite element model. For the inverse section, the trained neural network was applied to estimate the depth temperature distribution (tumour position) from the surface temperature profile, extracted from the thermal image. Finally, tumour parameters were obtained from the depth temperature distribution. Experimental findings (20 patients) were promising in terms of the model's potential for retrieving tumour parameters.

Potentialities of steady-state and transient thermography in breast tumour depth detection: A numerical study

Breast thermography still has inherent limitations that prevent it from being fully accepted as a breast screening modality in medicine. The main challenges of breast thermography are to reduce false positive results and to increase the sensitivity of a thermogram. Further, it is still difficult to obtain information about tumour parameters such as metabolic heat, tumour depth and diameter from a thermogram. However, infrared technology and image processing have advanced significantly and recent clinical studies have shown increased sensitivity of thermography in cancer diagnosis. The aim of this paper is to study numerically the possibilities of extracting information about the tumour depth from steady state thermography and transient thermography after cold stress with no need to use any specific inversion technique. Both methods are based on the numerical solution of Pennes bioheat equation for a simple three-dimensional breast model. The effectiveness of two approaches used for depth detection from steady state thermography is assessed. The effect of breast density on the steady state thermal contrast has also been studied. The use of a cold stress test and the recording of transient contrasts during rewarming were found to be potentially suitable for tumour depth detection during the rewarming process. Sensitivity to parameters such as cold stress temperature and cooling time is investigated using the numerical model and simulation results reveal two prominent depth-related characteristic times which do not strongly depend on the temperature of the cold stress or on the cooling period.

Application of infrared thermography in computer aided diagnosis

The invention of thermography, in the 1950s, posed a formidable problem to the research community: What is the relationship between disease and heat radiation captured with Infrared (IR) cameras? The research community responded with a continuous effort to find this crucial relationship. This effort was aided by advances in processing techniques, improved sensitivity and spatial resolution of thermal sensors. However, despite this progress fundamental issues with this imaging modality still remain. The main problem is that the link between disease and heat radiation is complex and in many cases even non-linear. Furthermore, the change in heat radiation as well as the change in radiation pattern, which indicate disease, is minute. On a technical level, this poses high requirements on image capturing and processing. On a more abstract level, these problems lead to inter-observer variability and on an even more abstract level they lead to a lack of trust in this imaging modality. In this review, we adopt the position that these problems can only be solved through a strict application of scientific principles and objective performance assessment. Computing machinery is inherently objective; this helps us to apply scientific principles in a transparent way and to assess the performance results. As a consequence, we aim to promote thermography based Computer-Aided Diagnosis (CAD) systems. Another benefit of CAD systems comes from the fact that the diagnostic accuracy is linked to the capability of the computing machinery and, in general, computers become ever more potent. We predict that a pervasive application of computers and networking technology in medicine will help us to overcome the shortcomings of any single imaging modality and this will pave the way for integrated health care systems which maximize the quality of patient care.

Analysis of breast thermograms using Gabor wavelet anisotropy index

In this study, an attempt is made to distinguish the normal and abnormal tissues in breast thermal images using Gabor wavelet transform. Thermograms having normal, benign and malignant tissues are considered in this study and are obtained from public online database. Segmentation of breast tissues is performed by multiplying raw image and ground truth mask. Left and right breast regions are separated after removing the non-breast regions from the segmented image. Based on the pathological conditions, the separated breast regions are grouped as normal and abnormal tissues. Gabor features such as energy and amplitude in different scales and orientations are extracted. Anisotropy and orientation measures are calculated from the extracted features and analyzed. A distinctive variation is observed among different orientations of the extracted features. It is found that the anisotropy measure is capable of differentiating the structural changes due to varied metabolic conditions. Further, the Gabor features also showed relative variations among different pathological conditions. It appears that these features can be used efficiently to identify normal and abnormal tissues and hence, improve the relevance of breast thermography in early detection of breast cancer and content based image retrieval.

Total variation based edge enhancement for level set segmentation and asymmetry analysis in breast thermograms

In this work, an attempt has been made to perform asymmetry analysis in breast thermograms using non-linear total variation diffusion filter and reaction diffusion based level set method. Breast images used in this study are obtained from online database of the project PROENG. Initially the images are subjected to total variation (TV) diffusion filter to generate the edge map. Reaction diffusion based level set method is employed to segment the breast tissues using TV edge map as stopping boundary function. Asymmetry analysis is performed on the segmented breast tissues using wavelet based structural texture features. The results show that nonlinear total variation based reaction diffusion level set method could efficiently segment the breast tissues. This method yields high correlation between the segmented output and the ground truth than the conventional level set. Structural texture features extracted from the wavelet coefficients are found to be significant in demarcating normal and abnormal tissues. Hence, it appears that the asymmetry analysis on segmented breast tissues extracted using total variation edge map can be used efficiently to identify the pathological conditions of breast thermograms.

Evaluation of the diagnostic power of thermography in breast cancer using Bayesian network classifiers

Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool.

Medical applications of infrared thermography: A review

Abnormal body temperature is a natural indicator of illness. Infrared thermography (IRT) is a fast, passive, non-contact and non-invasive alternative to conventional clinical thermometers for monitoring body temperature. Besides, IRT can also map body surface temperature remotely. Last five decades witnessed a steady increase in the utility of thermal imaging cameras to obtain correlations between the thermal physiology and skin temperature. IRT has been successfully used in diagnosis of breast cancer, diabetes neuropathy and peripheral vascular disorders. It has also been used to detect problems associated with gynecology, kidney transplantation, dermatology, heart, neonatal physiology, fever screening and brain imaging. With the advent of modern infrared cameras, data acquisition and processing techniques, it is now possible to have real time high resolution thermographic images, which is likely to surge further research in this field. The present efforts are focused on automatic analysis of temperature distribution of regions of interest and their statistical analysis for detection of abnormalities. This critical review focuses on advances in the area of medical IRT. The basics of IRT, essential theoretical background, the procedures adopted for various measurements and applications of IRT in various medical fields are discussed in this review. Besides background information is provided for beginners for better understanding of the subject.

Comparison of standard mammography with digital mammography and digital infrared thermal imaging for breast cancer screening

Breast cancer is the most common malignancy in women. Screen-film mammography (SFM) has been considered the gold standard for breast cancer screening and detection. Despite its recognized value in detecting and characterizing breast disease, mammography has important limitations and its false-negative rate ranges from 4% to 34%. Given these limitations, development of imaging modalities that would enhance, complement, or replace mammography has been a priority. Digital mammography (FFDM) and digital infrared thermal imaging (DITI) are some of these alternative modalities.

A perspective on medical infrared imaging

Since the early days of thermography in the 1950s, image processing techniques, sensitivity of thermal sensors and spatial resolution have progressed greatly, holding out fresh promise for infrared (IR) imaging techniques. Applications in civil, industrial and healthcare fields are thus reaching a high level of technical performance. The relationship between body temperature and disease was documented since 400 bc. In many diseases there are variations in blood flow, and these in turn affect the skin temperature. IR imaging offers a useful and non-invasive approach to the diagnosis and treatment (as therapeutic aids) of many disorders, in particular in the areas of rheumatology, dermatology, orthopaedics and circulatory abnormalities. This paper reviews many usages (and hence the limitations) of thermography in biomedical fields.

Advanced integrated technique in breast cancer thermography

Thermography is a passive and non-contact imaging technique used extensively in the medical arena, but in relation to breast care, it has not been accepted as being on a par with mammography. This paper proposes the analysis of thermograms with the use of artificial neural networks (ANN) and bio-statistical methods, including regression and receiver operating characteristics (ROC). It is desired that through these approaches, highly accurate diagnosis using thermography techniques can be achieved. The suggested method is a multi-pronged approach comprising of linear regression, radial basis function network (RBFN) and ROC analysis. It is a novel, integrative and powerful technique that can be used to analyse large amounts of complicated measured data such as temperature values extracted from abnormal and healthy breast thermograms. The use of regression allows the correlation between the variables and the actual health status of the subject, which is decided by other traditional means such as the gold standard of mammography for breast cancer detection. This is important as it helps to select the appropriate variables to be used as inputs for building the neural network. RBFN is next trained to produce the desired outcome that is either positive or negative. When this is done, the RBFN possess the ability to predict the outcome when there are new input variables. The advantages of using RBFN include fast training of superior classification and decision-making abilities as compared to other networks such as backpropagation. Lastly, ROC is applied to evaluate the sensitivity, specificity and accuracy of the outcome for the RBFN test files. The proposed technique has an accuracy rate of 80.95%, with 100% sensitivity and 70.6% specificity in identifying breast cancer. The results are promising as compared to clinical examination by experienced radiologists, which has an accuracy rate of approximately 60-70%. To sum up, technological advances in the field of infrared thermography over the last 20 years warrant a re-evaluation of the use of high-resolution digital thermographic camera systems in the diagnosis and management of breast cancer. Thermography seeks to identify the presence of a tumour by the elevated temperature associated with increase blood flow and cellular activity. Of particular interest would be investigation in younger women and men, for whom mammography is either unsuitable or of limited effectiveness. The paper evaluated the high-definition digital infrared thermographic technology and knowledge base; and supports the development of future diagnostic and therapeutic services in breast cancer imaging. Through the use of integrative ANN and bio-statistical methods, advances are made in thermography application with regard to achieving a higher level of consistency. For breast cancer care, it has become possible to use thermography as a powerful adjunct and biomarker tool, together with mammography for diagnosis purposes.

Numerical modelling of biopotential field for detection of breast tumour

Breast cancer is a disease characterised by the uncontrolled growth of abnormal cells. These cancer cells can travel through the body by way of blood or lymph nodes. Previous studies have indicated that, changes in the electrical properties of abnormal breast are more significant compared to the breast normal tissues. In the present study, a simple 2D models of breast (close to realistic), with and without artificially inserted malignant cancer were simulated, based upon electrical activity within the breast. We developed an inhomogeneous female breast model, closer to the actual, by considering a breast as a hemisphere with various layers of unequal thickness in supine condition. In order to determine the potential distribution developed due to a dipole source, isotropic homogeneous conductivity was assigned to each of these compartments and the volume conductor problem was solved using finite element method. Significant changes in the potential distribution were recoded in the malignant and normal breast regions. The surface potential decreases about 0.5%, for the small malignant region of surface area 13 mm(2) (spherical diameter=2mm). And it (surface potential) decreases about 16.4% for large malignant surface area of 615 mm(2) (spherical diameter=14 mm). Hence, the results show that, the sizes of tumours result in the reduction of surface potential and follows a fourth order polynomial equation. Thus, biofield analysis yields promising results in the detection of the breast cancer of various sizes.

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.