Distinguishing melanocytic nevi from melanomas using static and dynamic infrared thermal imaging

Beyond visual inspection: The value of infrared thermography in skin diseases, a scoping review

Although warmth is a key sign of inflammatory skin lesions, an objective assessment and follow-up of the temperature changes are rarely done in dermatology. The recent availability of accurate, sensitive and cost-effective thermography devices has made the implementation of thermography in clinical settings feasible. The aim of this scoping review is to summarize the evidence around the value and pitfalls of infrared thermography (IRT) when used in the dermatology clinic. A systematic literature search was done for original articles using IRT in skin disorders. The results concerning the potential of IRT for diagnosis, severity staging and monitoring of skin diseases were collected. The data on the sensitivity and specificity of IRT were extracted. Numerous studies have investigated IRT in various skin diseases, revealing its significant value in wound management, skin infections (e.g. cellulitis), vascular abnormalities and deep skin inflammation (e.g. hidradenitis suppurativa). For other dermatological applications such as the interpretation of intradermal and patch allergy testing, hyper-/anhidrosis, erythromelalgia, cold urticaria and lymph node metastases more complex calculations, provocation tests or active cooling procedures are required. Dermatologists should be aware of a learning curve of IRT and recognize factors contributing to false positive and false negative results. Nonetheless, enough evidence is available to recommend IRT as a supplement to the clinical evaluation for the diagnosis, severity and follow-up of several skin diseases.

Feasibility Study on the Use of Infrared Cameras for Skin Cancer Detection under a Proposed Data Degradation Model

Infrared thermography is considered a useful technique for diagnosing several skin pathologies but it has not been widely adopted mainly due to its high cost. Here, we investigate the feasibility of using low-cost infrared cameras with microbolometer technology for detecting skin cancer. For this purpose, we collected infrared data from volunteer subjects using a high-cost/high-quality infrared camera. We propose a degradation model to assess the use of lower-cost imagers in such a task. The degradation model was validated by mimicking video acquisition with the low-cost cameras, using data originally captured with a medium-cost camera. The outcome of the proposed model was then compared with the infrared video obtained with actual cameras, achieving an average Pearson correlation coefficient of more than 0.9271. Therefore, the model successfully transfers the behavior of cameras with poorer characteristics to videos acquired with higher-quality cameras. Using the proposed model, we simulated the acquisition of patient data with three different lower-cost cameras, namely, Xenics Gobi-640, Opgal Therm-App, and Seek Thermal CompactPRO. The degraded data were used to evaluate the performance of a skin cancer detection algorithm. The Xenics and Opgal cameras achieved accuracies of 84.33% and 84.20%, respectively, and sensitivities of 83.03% and 83.23%, respectively. These values closely matched those from the non-degraded data, indicating that employing these lower-cost cameras is appropriate for skin cancer detection. The Seek camera achieved an accuracy of 82.13% and a sensitivity of 79.77%. Based on these results, we conclude that this camera is appropriate for less critical applications.

Is thermography an effective screening tool for differentiating benign and malignant skin lesions in the head and neck? A systematic review

The aim of this study was to assess, through a systematic review, the status of infrared thermography (IRT) as a diagnostic tool for skin neoplasms of the head and neck region and in order to validate its effectiveness in differentiating benign and malignant lesions. A search was carried out in the LILACS, PubMed/MEDLINE, SCOPUS, Web of Science and EMBASE databases including studies published between 2004 and 2024, written in the Latin-Roman alphabet. Accuracy studies with patients aged 18 years or over presenting benign and malignant lesions in the head and neck region that evaluated the performance of IRT in differentiating these lesions were included. Lesions of mesenchymal origin and studies that did not mention histopathological diagnosis were excluded. The systematic review protocol was registered in the PROSPERO database (CRD42023416079). Reviewers independently analyzed titles, abstracts, and full-texts. After extracting data, the risk of bias of the selected studies was assessed using the QUADAS - 2 tool. Results were narratively synthesized and the certainty of evidence was measured using the GRADE approach. The search resulted in 1,587 records and three studies were included. Only one of the assessed studies used static IRT, while the other two studies used cold thermal stress. All studies had an uncertain risk of bias. In general, studies have shown wide variation in the accuracy of IRT for differentiating between malignant and benign lesions, with a low level of certainty in the evidence for both specificity and sensitivity.

Intraoperative thermal infrared imaging in neurosurgery: machine learning approaches for advanced segmentation of tumors

Surgical resection is one of the most relevant practices in neurosurgery. Finding the correct surgical extent of the tumor is a key question and so far several techniques have been employed to assist the neurosurgeon in preserving the maximum amount of healthy tissue. Some of these methods are invasive for patients, not always allowing high precision in the detection of the tumor area. The aim of this study is to overcome these limitations, developing machine learning based models, relying on features obtained from a contactless and non-invasive technique, the thermal infrared (IR) imaging. The thermal IR videos of thirteen patients with heterogeneous tumors were recorded in the intraoperative context. Time (TD)- and frequency (FD)-domain features were extracted and fed different machine learning models. Models relying on FD features have proven to be the best solutions for the optimal detection of the tumor area (Average Accuracy = 90.45%; Average Sensitivity = 84.64%; Average Specificity = 93,74%). The obtained results highlight the possibility to accurately detect the tumor lesion boundary with a completely non-invasive, contactless, and portable technology, revealing thermal IR imaging as a very promising tool for the neurosurgeon.

Infrared thermal imaging-based skin temperature response during cupping at two different negative pressures

Cupping therapy can relieve muscle fatigue and pain after exercise by increasing blood flow at the treatment site, which may lead to dynamic changes of the local skin temperature. This study aimed to analyze the effect of cupping on local skin temperature under two different negative pressures using infrared thermography (IRT). Cupping therapy was performed on the forearms of 22 healthy subjects using the negative pressures of − 0.03 and − 0.04 MPa. IRT was used to record the dynamic changes in skin temperature before, during, and after cupping. Both cupping pressures induced a non-linear skin temperature response: temperature decreased first and then increased during cupping, while it first increased and then decreased after cupping. A significant difference was noted between the two negative pressure groups in the maximum temperature increment after cupping (P < 0.001). Compared with the basal temperature before cupping, the maximum increase in skin temperature after cupping in the − 0.03 and − 0.04 MPa groups was 0.92 and 1.42 °C, respectively. The findings of this study can lay the foundation evaluating the curative effect of cupping based on IRT and provide an objective reference for selecting the cupping negative pressure.

Skin Cancer Detection Using Infrared Thermography: Measurement Setup, Procedure and Equipment

Infrared thermography technology has improved dramatically in recent years and is gaining renewed interest in the medical community for applications in skin tissue identification applications. However, there is still a need for an optimized measurement setup and protocol to obtain the most appropriate images for decision making and further processing. Nowadays, various cooling methods, measurement setups and cameras are used, but a general optimized cooling and measurement protocol has not been defined yet. In this literature review, an overview of different measurement setups, thermal excitation techniques and infrared camera equipment is given. It is possible to improve thermal images of skin lesions by choosing an appropriate cooling method, infrared camera and optimized measurement setup.

Dynamic thermal imaging for pigmented basal cell carcinoma and seborrheic keratosis

BACKGROUND

Clinical differentiation between pigmented basal cell carcinoma (BCC) and seborrheic keratosis (SK) can sometimes be difficult. Noninvasive diagnostic technologies, such as thermal imaging, can be helpful in these situations. This study explored the use of dynamic thermal imaging (DTI), which records thermal images after the application of external thermal stimuli (heat or cold) for the differential diagnosis of pigmented BCC and SK.

MATERIALS AND METHODS

Twenty-two patients with pigmented BCC and 15 patients with SK participated in this study. Dynamic thermal images of lesions (pigmented BCC or SK) and control sites (contralateral normal skin) were recorded after the heat and cold stimuli. Temperature changes in the region of interest (ROI) are plotted as a thermal response graph. After fitting an exponential equation to each thermal response graph, the rate constants were compared between groups (pigmented BCC versus control, SK versus control).

RESULTS

The thermal response graphs revealed that the average temperature of pigmented BCC showed faster thermal recovery to baseline than the control site. There was a significant difference in the rate constants of the fitted exponential equations between the pigmented BCCs and the control sites (p<.001). However, we did not find a significantly different thermal recovery pattern between SK lesions and control sites.

CONCLUSIONS

DTI can be used as a diagnostic tool for distinguishing pigmented BCC from SK by comparing thermal recovery patterns between target lesions (pigmented BCC or SK) and the control site.

Comparative Analysis of Diagnostic Techniques for Melanoma Detection: A Systematic Review of Diagnostic Test Accuracy Studies and Meta-Analysis

Melanoma has the highest mortality rate among skin cancers, and early-diagnosis is essential to maximize survival rate. The current procedure for melanoma diagnosis is based on dermoscopy, i.e., a qualitative visual inspection of lesions with intrinsic limited diagnostic reliability and reproducibility. Other non-invasive diagnostic techniques may represent valuable solutions to retrieve additional objective information of a lesion. This review aims to compare the diagnostic performance of non-invasive techniques, alternative to dermoscopy, for melanoma detection in clinical settings. A systematic review of the available literature was performed using PubMed, Scopus and Google scholar databases (2010-September 2020). All human, in-vivo, non-invasive studies using techniques, alternative to dermoscopy, for melanoma diagnosis were included with no restriction on the recruited population. The reference standard was histology but dermoscopy was accepted only in case of benign lesions. Attributes of the analyzed studies were compared, and the quality was evaluated using CASP Checklist. For studies in which the investigated technique was implemented as a diagnostic tool (DTA studies), the QUADAS-2 tool was applied. For DTA studies that implemented a melanoma vs. other skin lesions classification task, a meta-analysis was performed reporting the SROC curves. Sixty-two references were included in the review, of which thirty-eight were analyzed using QUADAS-2. Study designs were: clinical trials (13), retrospective studies (10), prospective studies (8), pilot studies (10), multitiered study (1); the remain studies were proof of concept or had undefined study type. Studies were divided in categories based on the physical principle employed by each diagnostic technique. Twenty-nine out of thirty-eight DTA studies were included in the meta-analysis. Heterogeneity of studies' types, testing strategy, and diagnostic task limited the systematic comparison of the techniques. Based on the SROC curves, spectroscopy achieved the best performance in terms of sensitivity (93%, 95% CI 92.8-93.2%) and specificity (85.2%, 95%CI 84.9-85.5%), even though there was high concern regarding robustness of metrics. Reflectance-confocal-microscopy, instead, demonstrated higher robustness and a good diagnostic performance (sensitivity 88.2%, 80.3-93.1%; specificity 65.2%, 55-74.2%). Best practice recommendations were proposed to reduce bias in future DTA studies. Particular attention should be dedicated to widen the use of alternative techniques to conventional dermoscopy.

Meta-Analysis and Systematic Review of the Application of Machine Learning Classifiers in Biomedical Applications of Infrared Thermography

Atypical body temperature values can be an indication of abnormal physiological processes associated with several health conditions. Infrared thermal (IRT) imaging is an innocuous imaging modality capable of capturing the natural thermal radiation emitted by the skin surface, which is connected to physiology-related pathological states. The implementation of artificial intelligence (AI) methods for interpretation of thermal data can be an interesting solution to supply a second opinion to physicians in a diagnostic/therapeutic assessment scenario. The aim of this work was to perform a systematic review and meta-analysis concerning different biomedical thermal applications in conjunction with machine learning strategies. The bibliographic search yielded 68 records for a qualitative synthesis and 34 for quantitative analysis. The results show potential for the implementation of IRT imaging with AI, but more work is needed to retrieve significant features and improve classification metrics.