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Lower back pain is one of the most prevalent health issues, affecting more than 80% of adults worldwide. Thermotherapy including heat wrap and dry sauna has long been utilized for pain relief and relaxation. Far-infrared graphene-based thermography is a heat therapy method where the graphene emits far-infrared rays that can penetrate human skin. However, its effects remain largely unstudied compared to conventional thermotherapy. This study investigates the impact of far-infrared graphene-based thermotherapy on healthy individuals and individuals associated with nonspecific low back pain. Over four sessions, 24 subjects undergo 30 min treatments, with measurements including body heat profiles, blood oxygen levels, joint angles, pain scales, and Oswestry scores. Results indicate increased body heat and blood oxygen levels post-treatment, alongside significant reductions in pain scores. However, changes in joint angles were not statistically significant, suggesting no immediate impact on locomotion. In conclusion, far-infrared graphene-based thermotherapy shows promise for pain relief and improved blood oxygenation, however, it has not been proven to improve locomotion.

To examine the effectiveness of the herbal medicine prescription, Shu-Jing-Huo-Xue-Tang (SJHXT), for pain relief, we performed a study using rats with adjuvant arthritis (AA). After injecting the adjuvant, AA rats were maintained for 6 months as a chronic pain model. Starting at 6 months, SJHXT was administered for 12 weeks. We measured the tail skin temperature and locomotor activity of rats using thermography and a metabolism measuring system, respectively, before and after 12 weeks of SJHXT administration. Normal rats were used as controls. Before SJHXT administration, the tail surface temperature and locomotor activity were significantly lower in the AA rats than in the control rats. The tail skin temperature and locomotor activity of SJHXT-treated AA rats were significantly higher than those of the control rats. These findings suggest that the pain relief effects of SJHXT may be primarily due to increased blood circulation.

Honeycomb composites are now fairly widely used in civilian and military aircraft structures. Common defects found in these materials are delaminations by impact damage and their presence will lead to structural weaknesses which could lead failure of the airframe structures. It is important to develop effective non-destructive testing procedures to identify these defects and increase the safety of aircraft travel. This paper describes the detection technique of impact damage defect using thermography and ESPI. The results obtained with the two techniques are compared with ultrasonic C-scan testing. The investigation shows that both imaging NDT methods are able to identify the presence of artificial defect and impact damage. The adoption of the thermography allowed significant advantages in inspection condition, and gives smaller error in quantitative estimation of defects.

This paper presents major medical applications of microwave radiation in therapy and diagnostics of disorders of thermoregulation, especially hyperthermia and thermography. Microwave thermography is a thermal imaging system produced by self-emission, using emissivity differences to extend our vision beyond the shortwave red. Human tissues are partially transparent to microwaves, thus it is possible to detect the microwave of subcutaneous tissues in thermography, and to allow microwave energy penetration through subcutaneous tissues for deep-tissue heating in hyperthermia. The physics of microwave thermography together with the microwave properties and emission of body tissues are introduced. It is followed by reviews of the literature pertinent to microwave hyperthermia in therapy and treatment. Recent development in this field is briefly discussed.

Although mammography is still the benchmark technique for breast cancer detection, many advantages of thermography make it a suitable adjunct tool for early detection. This paper describes the development of a computer-aided system for use together with thermography to assist in the detection and visualization/analysis of breast tumors. The system consists of a detection module for predicting the presence of tumors from thermograms, and a visualization module for generating the 3-D volumetric geometry of the suspected tumor inside the breast based on the 2-D thermogram. Detection is achieved through an artificial neural network taking the thermogram image as input, while the visualization is obtained by generating the 3-D model of the breast that produces a matching thermal image as the thermogram under a 3-D finite element analysis. A study with 200 subjects indicate that the detection sensitivity was good but the specificity was poor, but the reverse performance result was true for another back-propagation neural network which used physiological data instead of thermograms as input. This suggests that overall prediction capability can be improved by appropriate combination of the two results.

The treatment of early development of breast tumor has a higher success rate. This paper presents a framework for the early discovery of breast cancer. The objective is to assist the general practitioners and specialists in the detection of breast tumor. The proposed detection process consists of a preliminary screening process and a prediction process. The preliminary screening process using thermography aims to complement the detailed screening operation using mammography. The prediction process using artificial intelligence techniques aims to use past records of other similar cases to enhance the forecast of breast cancer development. The paper discusses the issues and techniques for the implementation of the proposed framework. These include the preliminary screening process, the retrieval of the relevant cases, and the prediction of the risk of developing breast cancer based on the thermographs, environmental/social data, physiological information, genetic factors, and medical records. This work constitutes initial effort to lessen the burden of medical professionals and increase the chances of successful treatment for patients in the fight against breast cancer.

Computational simulation of the thermal transport phenomena in the human body has recently aroused a great deal of interest among researchers, because it can be applied in different areas such as medicine, rehabilitation, space suits, and others. In this study, we developed a coupling model to analyze the temperature distribution of the human middle finger. Firstly, a one-dimensional thermo-fluid model of blood circulation in the human upper limb is constructed. Secondly, a two-dimensional thermal model of the human finger, which consists of skin, tendon, bone, main arteries, and veins is developed. The two models are further coupled weakly through data transfer. The blood pressure, blood flow rate, and blood temperature at different vessel sites and the tissue temperature are thus obtained. The effect of viscosity on the finger skin temperature was also investigated. Simultaneously, the thermograms of the human hand were also obtained using thermograpy under the resting condition and after jogging, to observe the variation in the blood circulation. The temperature at different points was extracted from the thermograms. It is observed that there is a periodic variation in skin temperature near the blood vessels after jogging. It is expected that this coupling model will be applicable to hyperthermia, drug delivery, and sports training.

Background: Breast cancer is a common and dreadful disease in women. One in five cancers in Singaporean women is due to breast cancer. Breast health is every woman's right and responsibility. In average, every $100 spent on breast mammogram screening, an additional $33 was spent on evaluating possible false-positive results. Thermography, with its non-radiation, non-contact and low-cost basis has been demonstrated to be a valuable and safe early risk marker of breast pathology, and an excellent case management tool available today in the ongoing monitoring and treatment of breast disease. The surface temperature and the vascularization pattern of the breast could indicate breast diseases and early detection saves lives. To establish the surface isotherm pattern of the breast and the normal range of cyclic variations of temperature distribution can assist in identifying the abnormal infrared images of diseased breasts. Before these thermograms can be analyzed objectively via computer algorithm, they must be digitized and segmented. The authors present a method to segment thermograms and extract useful region from the background. Thermography could detect the presence of tumors much earlier and of much smaller size than mammography. This paper thus aims to develop an intelligent diagnostic system based on thermography for the detection of tumors in breast. Methods: We have examined about 50 normal, healthy female volunteers in Nanyang Technological University and 130 patients in Singapore General Hospital. We did the examinations for some of them continuously for two months. From these examinations, we obtained about 1000 thermograms for contact and 800 thermograms for non-contact approaches. Standard ambient conditions were observed for all examinations. The thermograms obtained were analyzed. The first step in processing these thermograms is image segmentation. Its aim is to discern the useful region from the background. In general, autonomous segmentation is one of the most difficult tasks in image processing. This step in the process determines the eventual success or failure of the analysis. In this work, two different techniques have been presented to extract the objects from the background. Results: After analyzing these thermograms and with reference to some relevant well-documented papers, we were able to classify the thermograms. The step is very useful in identifying the normal or suspected (abnormal) thermograms. A series of thermograms was studied with the help of the in-house developed computer software. On the basis of the anatomic and vascular symmetry, the surface temperature distributions of both left and right breasts were compared. The surface isotherm pattern of breasts can indicate the local metabolism and vascularity of the underlying tissues, and the change in local blood or glandular activities can be reflected in the surface temperature of breast. We evaluated the temperature distribution pattern and the menstrual cyclic variation of temperature with time. All these results can be used to detect breast cancer. Conclusion: Automatic identification of object and surface boundary of breast thermal images is a difficult and challenging task. Both the traditional snake and gradient vector flow snake failed to detect the boundary of these images successfully. In this work, a new method is proposed in conjunction with image pre-processing, image transition, image derivative, filtering and gradient vector flow snake. This novel method can easily detect the boundary of the breast thermal image with good agreement.

Thermography is a non-invasive and non-contact imaging technique widely used in the medical arena. This paper investigates the analysis of thermograms with the use of biostatistical methods and Artificial Neural Networks (ANN). It is desired that through these approaches, highly accurate diagnosis using thermography techniques can be established.

The proposed advanced technique is a multipronged approach comprising of Linear Regression (LR), Radial Basis Function Network (RBFN) and Receiver Operating Characteristics (ROC). It is a novel and integrative technique that can be used to analyze complicated and large numerical data. In this study, the advanced technique will be used to analyze breast cancer thermogram for diagnosis purposes.

The use of LR shows the correlation between the variables and the actual health status (healthy or cancerous) of the subject, which is decided by using mammography. This is important when selecting the variables to be used as inputs, in particular, for building the neural network.

For ANN, RBFN is applied. Based on the various inputs fed into the network, RBFN will be trained to produce the desired outcome, which is either positive for cancerous or negative for healthy cases. When this is done, the RBFN algorithm will possess the ability to predict the outcome when there are new input variables. The advantages of using RBFN include fast training, superior classification and decision making abilities as compared to other networks such as back-propagation.

Next, ROC is used to evaluate the accuracy, sensitivity and specificity of the outcome of RBFN Test files. The best results obtained are an accuracy (score) rate of 80.95%, with 81.2% sensitivity and 88.2% specificity. For breast cancer diagnosis, clinical examination by experienced doctors has an accuracy rate of approximately 60–70%. Hence, the proposed method has a higher accuracy rate than the existing practice.

Through the use of Bio-statistical methods and ANN, improvements are made in thermography application with regard to achieving a higher level of accuracy rate in diagnosis as compared to clinical examination. It has now become possible to use thermography as a powerful adjunct tool for breast cancer detection, together with mammography for diagnosis purposes.

Due to the successful union between computational technologies and basic laws of physics and biological sciences, many biomedical imaging systems now find significant presence in clinical settings, aiding physicians in diagnosing most forms of human illness with more confidence. In the case of breast imaging, apart from the basic diagnosis, these imaging systems also help in locating the abnormal tissues for biopsy, identifying the exact margins of the lesion for good lumpectomy results, staging and restaging the cancer, detecting locations of metastases, and planning and following up treatment protocols. It is well known that early detection of cancer is the only way to increase the survival rate of the patient. Without such imaging systems, it would be hard and almost impossible for the physicians to determine the nature and extent of the disease by merely simple physical examinations and biopsies. This article presents a description of most of these invaluable breast-imaging systems. Moreover, a comparison of these modalities and a review of a few of the developments these devices have come across over the years are also given.

Heel fissures are cracks in the skin over the heels that lead to pain, discomfort and decreased confidence levels. If left untreated, they may also lead to infections and in rare cases, become life-threatening. Therefore, people with heel fissures generally try to find some remedy to relieve their symptoms. The objectives of this study are as follows: (1) To use thermal imaging to determine whether a characteristic difference in temperature exists in the heel fissure regions before and after performing heel therapy; (2) To segment the images and extract the features using k-means, GLCM and SURF methods, respectively; (3) To implement machine learning classifier for classification on normal heel and fissured heel. A number of 30 heel fissure and 30 normal subjects were considered for this study. All the candidates were from the age group of 35–55 years. Thermography was used to acquire the images of heel regions, and the thermographs were analyzed for feature extraction. Naïve Bayes, Bagging, Random Forest, LMT and Simple Logistic classifiers were used for classification of the thermograms. After heel therapy, a 2.2% and 2.6% decrease in temperature was observed in the right and left heel, respectively. The GLCM mean is increased by 6% and 4.3% in the right and left heel, respectively. A considerable decrease in variance in the fissure regions after therapy has also been observed. All three classifiers were shown to be efficient, with Nave Bayes and Bagging classifier both showing accuracy of 89%. The ROC curves have also been obtained, with an area under curve equal to 0.97.

Chronic Venous Insufficiency (CVI) is a venous incompetence condition that leads to improper blood circulation from the lower limbs towards the heart. This occurs as a result of blood pooling in the veins of the leg, resulting in twisted, dilated, and tortuous veins. Aging, obesity, prolonged standing or sitting, and lack of mobility are all important causes of the occurrence of this chronic disease. The cost of CVI diagnosis and treatment is extremely high. Infrared thermographic image analysis is used for early detection and reduces the cost of diagnosis. Deep learning (DL) techniques play an important role in early prediction and may aid clinicians in diagnosing CVI. An automated classification model will assist the physician in making a precise diagnosis of the abnormal vein and treating the patient according to the severity of the condition. There is a need for a model that can perform successful classification without the need for pre-processing when compared to the traditional machine learning (ML) methods that depend on ideal manual feature extraction to achieve optimal outcomes. In this research, we recommend the customized DenseNet-121 architecture for CVI detection and compare it with other advanced DL models to determine its efficacy. DenseNet-121 and other pre-trained convolutional neural network models, including EfficientNetB0 and Inception_v3, were trained using a transfer learning strategy. The experimental findings indicate that the proposed modified DenseNet-121 model outperformed other classical methods. The reported results provide evidence of the robustness of the suggested method in addition to the high accuracy that it possessed, as shown by the overall testing accuracy of 97.4%. Thus, this study can be considered as a non-invasive and cost-effective approach for diagnosing chronic venous insufficiency condition in lower extremity.

In this paper, an intelligent method is developed for improving the performance of the Computer-Aided Detection (CAD) system. The research objective is to improve the performance of the CAD system in Breast Cancer (BC) detection with high accuracy using thermal images. The research strategy is efficient using feature extraction, feature selection, classification and artificial intelligence methods. In the developed method, the features in the Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) are extracted from images. The features are selected using the firefly feature selection algorithm. These selected features are observed to be relevant for the abnormality detection in healthy and unhealthy breasts. The $k$-Nearest Neighbors (kNN), Support Vector Machine (SVM), and Decision-Tree (D-Tree) classifiers are then applied to these features for the detection of malignancy in the breast. The breast thermograms of 200 subjects available at the Database for Mastology Research for breast research using InfraRed images, DMR-IR database, are considered for evaluation of our intelligent method. The results demonstrate that the accuracy is 98.8%, 81.5%, and 94.5%, the sensitivity is 99%, 83.15%, and 94%, and the specificity is 98.2%, 80%, and 93% when using SVM, kNN, and D-Tree classifier algorithm, respectively. This reveals the effectiveness of our intelligent method to improve the accuracy of the CAD system in the BC detection.