The importance of using infrared thermography (IRT) to assess skin temperature (tsk) is increasing in clinical settings. Recently, its use has been increasing in sports and exercise medicine; however, no consensus guideline exists to address the methods for collecting data in such situations. The aim of this study was to develop a checklist for the collection of tsk using IRT in sports and exercise medicine. We carried out a Delphi study to set a checklist based on consensus agreement from leading experts in the field. Panelists (n = 24) representing the areas of sport science (n = 8; 33%), physiology (n = 7; 29%), physiotherapy (n = 3; 13%) and medicine (n = 6; 25%), from 13 different countries completed the Delphi process. An initial list of 16 points was proposed which was rated and commented on by panelists in three rounds of anonymous surveys following a standard Delphi procedure. The panel reached consensus on 15 items which encompassed the participants' demographic information, camera/room or environment setup and recording/analysis of tsk using IRT. The results of the Delphi produced the checklist entitled "Thermographic Imaging in Sports and Exercise Medicine (TISEM)" which is a proposal to standardize the collection and analysis of tsk data using IRT. It is intended that the TISEM can also be applied to evaluate bias in thermographic studies and to guide practitioners in the use of this technique.

Breast cancer is the most common type of malignant tumor among women and their second leading cause of cancer-related deaths. The most common method for screening and diagnosis is mammography. Nonetheless, two main problems have been identified. First, the dose of radiation received during the test prevents the method from the use on women who are < 40 years old. Second, there can be mammogram failure owing to the lack of tumor contrast with the fibrous tissue. Therefore, there is a need for screening methods that will help to identify high-risk cases. We developed a biological marker test that can help to identify them. Increased levels of sialic acid (SA) in saliva are known to correlated with breast cancer. In this study, we evaluated the feasibility of Raman spectroscopy as a method for quantification of SA in saliva, using citrate-reduced silver nanoparticles (cit-Ag-NPs) as a surface-enhanced Raman spectroscopy (SERS) substrate. Quantification of SA was accomplished by measuring its intensity in saliva and comparing it with a calibration curve of SA standards. The mean SA concentration in saliva was found to be significantly higher among 100 breast cancer patients (18.3 ± 9.4 mg·dL^–1; mean ± SD) than among 106 healthy controls (3.5 ± 1.0 mg·dL^–1). The SERS test showed sensitivity of 94% and specificity 98% for detection of patients with breast cancer, assuming that SA concentration >7 mg·dL^–1 is a cutoff for positive test results. Our findings prove the usefulness of this SERS technique as a simple, convenient, and highly sensitive method of quantitative analysis of SA in saliva. The simplicity of this nanotechnological test may help to substantially reduce the mortality among patients with breast cancer by providing women with a simple, noninvasive screening test that can be applied regardless of age or density of breast tissue.

Infrared thermography is a technique that can detect anomalies in temperature patterns which can indicate some breast pathologies including breast cancer. One limitation of the method is the absence of standardised thermography interpretation procedures. Deep learning models have been used for pattern recognition and classification of objects and have been adopted as an adjunct methodology in medical imaging diagnosis. In this paper, the use of a deep convolutional neural network (CNN) with transfer learning is proposed to automatically classify thermograms into two classes (normal and abnormal). A population of 311 female subjects was considered analysing two approaches to test the CNN’s performance: one with a balanced class distribution and the second study in a typical screening cohort, with a low prevalence of abnormal thermograms. Results showed that the transfer-learned ResNet-101 model had a sensitivity of 92.3% and a specificity of 53.8%, while with an unbalanced distribution the values were 84.6% and 65.3%, respectively. These results suggest that the model presented in this work can classify abnormal thermograms with high sensitivity which validates the use of infrared thermography as an adjunct method for breast cancer screening.

The clinical evaluation of a burn wound alone may not be adequate to predict the severity of the injury nor to guide clinical decision making. Infrared thermography provides information about soft tissue viability and has previously been used to assess burn depth. The objective of this study was to determine if temperature differences in burns assessed by infrared thermography could be used predict the treatment modality of either healing by re-epithelization, requiring skin grafts, or requiring amputations, and to validate the clinical predication algorithm in an independent cohort. Temperature difference (ΔT) between injured and healthy skin were recorded within the first three days after injury in previously healthy burn patients. After discharge, the treatment modality was categorized as re-epithelization, skin graft or amputation. Potential confounding factors were assessed through multiple linear regression models, and a prediction algorithm based on the ΔT was developed using a predictive model using a recursive partitioning Random Forest machine learning algorithm. Finally, the prediction accuracy of the algorithm was compared in the development cohort and an independent validation cohort. Significant differences were found in the ΔT between treatment modality groups. The developed algorithm correctly predicts into which treatment category the patient will fall with 85.35% accuracy. Agreement between predicted and actual treatment for both cohorts was weighted kappa 90%. Infrared thermograms obtained at first contact with a wounded patient can be used to accurately predict the definitive treatment modality for burn patients. This method can be used to rationalize treatment and streamline early wound closure.

Clinical trials have shown the effectiveness of systemic and local insulin therapy in improving wound healing. Diabetic wounds remain a challenge for healthcare providers. Impaired angiogenesis and reduced granulation tissue formation contribute to inadequate wound healing. The aim of this study was to investigate the effect of local insulin administration in acute and chronic diabetic wounds. Eight diabetic patients presenting with full-thickness wounds, of different causes, were included in this study. Five wounds were due to necrobiosis, one to trauma, and two to postneoplasm resection. All wounds were treated with regular bedside treatment. In addition, half of the wound surface was treated with insulin and the other half did not receive insulin. Thermographic and biopsy specimens of the two sides were obtained on days 0 and 14. The presence of fibrosis, change in temperature, and amount of blood were evaluated. Significant differences in the number of vessels were observed on the insulin-treated side (96 ± 47) when compared with the no-insulin side (32.88 ± 45) (p < 0.026). The percentage of fibrosis (insulin: 44.42 ± 30.42 percent versus no insulin: 12.38 ± 36.17 percent; p < 0.047) and the mean temperature (insulin: 1.27 ± 1.12°C versus no-insulin: 0.13 ± 1.22°C; p < 0.001) were also significantly different between sides. No adverse events related to the study occurred. The use of local insulin improves the formation of new blood vessels, increases fibrosis, and correlates with increased temperature. Therapeutic, II.

Introduction: Despite being widely used as a screening tool, a rigorous scientific evaluation of infrared thermography for the diagnosis of minimally symptomatic patients suspected of having COVID‐19 infection has not been performed.Methods: A consecutive sample of 60 adult individuals with a history of close contact with COVID‐19 infected individuals and mild respiratory symptoms for less than 7 days and 20 confirmed COVID‐19 negative healthy volunteers were enrolled in the study. Infrared thermograms of the face were obtained with a mobile camera, and RT‐PCR was used as the reference standard test to diagnose COVID‐19 infection. Temperature values and distribution of the face of healthy volunteers and patients with and without COVID‐19 infection were then compared.Results: Thirty‐four patients had an RT‐PCR confirmed diagnosis of COVID‐19 and 26 had negative test results. The temperature asymmetry between the lacrimal caruncles and the forehead was significantly higher in COVID‐19 positive individuals. Through a random forest analysis, a cut‐off value of 0.55°C was found to discriminate with an 82% accuracy between patients with and without COVID‐19 confirmed infection.Conclusions: Among adults with a history of COVID‐19 exposure and mild respiratory symptoms, a temperature asymmetry of ≥ 0.55°C between the lacrimal caruncle and the forehead is highly suggestive of COVID‐19 infection. This finding questions the widespread use of the measurement of absolute temperature values of the forehead as a COVID‐19 screening tool.