Search Results (107)

Background and Objectives: Lower limb defects often present significant reconstructive challenges due to limited soft tissue availability and exposure of critical structures. Perforator-based flaps offer reliable solutions, with minimal donor site morbidity. This study aimed to evaluate the efficacy of infrared thermography (IRT) in preoperative planning and postoperative monitoring of perforator-based flaps, assessing its accuracy in identifying perforators, predicting complications, and optimizing outcomes. Materials and Methods: A prospective observational study was conducted on 76 patients undergoing lower limb reconstruction with fascio-cutaneous perforator flaps between 2022 and 2024. Perforator mapping was performed concurrently with IRT and Doppler ultrasonography (D-US), with intraoperative confirmation. Flap design variables and systemic parameters were recorded. Postoperative monitoring employed thermal imaging on days 1 and 7. Outcomes were correlated with thermal, anatomical, and systemic factors using statistical analyses, including t-tests and Pearson correlation. Results: IRT showed high sensitivity (97.4%) and positive predictive value (96.8%) for perforator detection. A total of nine minor complications occurred, predominantly in patients with diabetes mellitus and/or elevated glycemia (p = 0.05). Larger flap-to-defect ratios (A/C and B/C) correlated with increased complications in propeller flaps, while smaller ratios posed risks for V-Y and Keystone flaps. Thermal analysis indicated significantly lower flap temperatures and greater temperature gradients in flaps with complications by postoperative day 7 (p < 0.05). CRP levels correlated with glycemia and white blood cell counts, highlighting systemic inflammation’s impact on outcomes. Conclusions: IRT proves to be a reliable, non-invasive method for perforator localization and flap monitoring, enhancing surgical planning and early complication detection. Combined with D-US, it improves perforator selection and perfusion assessment. Thermographic parameters, systemic factors, and flap design metrics collectively predict flap viability. Integration of IRT into surgical workflows offers a cost-effective tool for optimizing reconstructive outcomes in lower limb surgery. Full article

Nutrition plays a fundamental role in promoting health and preventing chronic diseases, with bioactive food components offering a therapeutic potential in biomedical applications. Among these, edible oils are recognised for their functional properties, which contribute to disease prevention and metabolic regulation. The proposed study aims to evaluate the quality of four bioactive oils (olive oil, sunflower oil, tomato seed oil, and pumpkin seed oil) by analysing their thermal behaviour through infrared (IR) imaging. The study designed a customised electronic system to acquire thermographic signals under controlled temperature and humidity conditions. The acquisition system was used to extract thermal data. Analysis of the acquired thermal signals revealed characteristic heat absorption profiles used to infer differences in oil properties related to stability and degradation potential. A hybrid deep learning model that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) units was used to classify and differentiate the oils based on stability, thermal reactivity, and potential health benefits. A signal analysis showed that the AI-based method improves both the accuracy (achieving an F1-score of 93.66%) and the repeatability of quality assessments, providing a non-invasive and intelligent framework for the validation and traceability of nutritional compounds. Full article

Background/Objectives: The heterogeneity of cancer disease and the frequent ineffectiveness and resistance observed with currently available treatments highlight the importance of developing new antitumor therapies. The properties of gold nanoparticles, such as their photon-energy heating, are attractive for oncology therapy; this can be effective and localized. The combination of chemotherapy and hyperthermia is promising. Our aim was to evaluate the combination therapy of photon hyperthermia with 5-fluorouracil (5-FU) both in vitro and in vivo. Methods: This study evaluated the antitumor efficacy of a combined chemo-photothermal therapy using 5-fluorouracil (5-FU) and branched gold nanoshells (BGNSs) in a colorectal cancer model. BGNSs were synthesized via a seed-mediated method and characterized by electron microscopy and UV–vis spectroscopy, revealing an average diameter of 126.3 nm and a plasmon resonance peak at 800 nm, suitable for near-infrared (NIR) photothermal applications. In vitro assays using SW620-GFP colon cancer cells demonstrated a ≥90% reduction in cell viability after 24 h of combined treatment with 5-FU and BGNS under NIR irradiation. In vivo, xenograft-bearing nude mice received weekly intratumoral administrations of the combined therapy for four weeks. The group treated with 5-FU + BGNS + NIR exhibited a final tumor volume of 0.4 mm³ on day 28, compared to 1010 mm³ in the control group, corresponding to a tumor growth inhibition (TGI) of 100.74% (p < 0.001), which indicates not only complete inhibition of tumor growth but also regression below the initial tumor volume. Thermographic imaging confirmed that localized hyperthermia reached 45 ± 0.5 °C at the tumor site. Results: These findings suggest that the combination of 5-FU and BGNS-mediated hyperthermia may offer a promising strategy for enhancing therapeutic outcomes in patients with colorectal cancer while potentially minimizing systemic toxicity. Conclusions: This study highlights the potential of integrating nanotechnology with conventional chemotherapy for more effective and targeted cancer treatment. Full article

Hypocalcemia is common in dairy cows within the first 72 h post-calving, and can be either clinical or subclinical. Early detection is critical, but traditional laboratory tests are time-consuming and cow-side tests remain costly. A classic symptom of hypocalcemia is reduced ear skin temperature, which has been explored as a diagnostic tool in a previous study, but was not recommended at the end. Additionally, ambient temperature was found to strongly influence ear skin temperature, complicating diagnosis. The present study investigates infrared thermography of the ear as a potential non-invasive method for helping in the detection of hypocalcemia in Holstein cows. In order to differ from the previous study, with the goal of improving diagnosis accuracy, this research analyzed the entire ear temperature using infrared imaging software. Ambient temperature was factored in by categorizing samples into two groups based on air temperature: colder (−1.6 to 14.6 °C) and hotter (15.3 to 31.2 °C). Forty-two cows were monitored during the perinatal period, with blood samples and thermographic images taken twice a day until 48 h after calving. This study found that the median surface temperature of the ear correlated strongly with environmental temperature (r = 0.806, p < 0.001) and weakly with blood ionized calcium levels (r = 0.310, p < 0.01). In colder air temperatures, ear surface temperature was significantly different between healthy and hypocalcemic cows (p = 0.014). Logistic regression models were used to assess ionized calcium status based on different combinations of ear surface temperature, its difference from air temperature, and days in milk. In hotter air temperatures, only ear surface temperature, with no other covariates, was able to generate a valid model (p = 0.029). In colder air temperatures, multiple combinations of those variables generated valid models (p < 0.05), with the difference between ear and air temperature, together with days in milk, performing the best. Thus, this study concluded that ear surface temperature obtained through infrared thermography, while not promising for warmer environments, does show application potential for helping in the detection of hypocalcemia in colder environments. Full article

Background/Objectives: Chronic low back pain (CLBP) is a prevalent condition that significantly impacts the aging population. Among non-invasive assessment tools, infrared thermography (IRT) has been highlighted as a radiation-free method to evaluate thermal variations in the lumbar region. However, its applicability in clinical practice and correlation with functional and pain-related parameters remain unclear. This study aimed to analyze the thermal profile of the lumbar region in elderly individuals with CLBP and explore potential correlations between lumbar temperature patterns and clinical factors such as pain intensity and functional capacity. Methods: A cross-sectional observational study was performed in an outpatient setting. The population included thirty-one elderly individuals diagnosed with CLBP. IRT was used to assess the lumbar temperature distribution, including participants who reported pain radiating to the lower limbs. Pain intensity was measured using a numerical rating scale (0–10). The functional assessments included spine mobility tests and validated questionnaires evaluating clinical characteristics. Results: No significant differences in lumbar temperature patterns were observed among the participants. Additionally, no correlation was found between pain intensity and functional capacity based on a thermographic analysis. Nonetheless, individuals reporting lower fatigue levels and those with a higher body mass index (BMI) were generally associated with cooler thermal readings on the lumbar region’s thermographic maps. Conclusions: These findings suggest that IRT may require methodological refinements, including optimized technical specifications and image acquisition protocols, to enhance its applicability in assessing CLBP. Indeed, IRT might not be the most effective tool for evaluating pain-related thermal changes in elderly populations. Further research is needed to clarify its role in clinical assessments. Full article

This study explores the integration of advanced artificial intelligence (AI) techniques with infrared thermography for diagnosing diabetic peripheral neuropathy (DPN) and peripheral arterial disease (PAD). Diabetes-related foot complications, including DPN and PAD, are leading causes of morbidity and disability worldwide. Traditional diagnostic methods, such as the monofilament test for DPN and ankle–brachial pressure index for PAD, have limitations in sensitivity, highlighting the need for improved solutions. Thermographic imaging, a non-invasive, cost-effective, and reliable tool, captures temperature distributions of the patient plantar surface, enabling the detection of physiological changes linked to these conditions. This study collected thermographic data from diabetic patients and employed convolutional neural networks (CNNs) and vision transformers (ViTs) to classify individuals as healthy or affected by DPN or PAD (not healthy). These neural networks demonstrated superior diagnostic performance, compared to traditional methods (an accuracy of 95.00%, a sensitivity of 100.00%, and a specificity of 90% in the case of the ResNet-50 network). The results underscored the potential of combining thermography with AI to provide scalable, accurate, and patient-friendly diagnostics for diabetic foot care. Future work should focus on expanding datasets and integrating explainability techniques to enhance clinical trust and adoption. Full article

Mechanization in timber harvesting has improved the comfort and safety of operator workstations. However, there is an imminent ergonomic risk in relation to the repetition of movements, which can cause musculoskeletal injuries. The aim of this study was to apply infrared thermography to identify musculoskeletal overload in the hand region of harvester operators. This study was conducted on wood harvesting of homogeneous Eucalyptus urophylla × Eucalyptus grandis stands using a forestry harvester tractor. Thermographic images were taken of seven operators at the beginning and end of the working day during six days of the shift. The maximum, average, and minimum temperatures were measured in 14 hand regions of interest (ROI), verifying the existence of a difference between the beginning and the end of work (p-value < 5%) by using the Mann–Whitney test. The operators presented hyperradiant temperature variation in the hand region after work, with a variation above 2.5 °C, indicating a high degree of abnormality. There was greater temperature variation in the right hand (3.7 °C) due to the greater concentration of commands on the machine’s right joystick. Infrared thermography has proven to be an important tool for functional imaging diagnosis, contributing to the reduction in risks of developing Repetitive Strain Injury/Work-Related Musculoskeletal Disorder (RSI-WMSD). Full article

Current techniques for fault analysis in photovoltaic (PV) systems plants involve either electrical performance measurements or image processing, as well as line infrared thermography for visual inspection. Deep convolutional neural networks (CNNs) are machine learning algorithms that perform tasks involving images, such as image classification and object recognition. However, to train a model effectively to recognize different patterns, it is crucial to have a sufficiently balanced dataset. Unfortunately, this is not always feasible owing to the limited availability of publicly accessible datasets for PV thermographic data and the unequal distribution of different faults in real-world systems. In this study, three data augmentation techniques—geometric transformations (GTs), generative adversarial networks (GANs), and the denoising diffusion probabilistic model (DDPM)—were combined with a CNN to classify faults in PV modules through thermographic images and identify the type of fault in 11 different classes (i.e., soiling, shadowing, and diode). Through the cross-validation method, the main results found with the Wasserstein GAN (WGAN) and DDPM networks combined with the CNN for anomaly classification achieved testing accuracies of 86.98% and 89.83%, respectively. These results demonstrate the effectiveness of both networks for accurately classifying anomalies in the dataset. The results corroborate the use of the diffusion model as a PV data augmentation technique when compared with other methods such as GANs and GTs. Full article

Breast cancer remains one of the most prevalent and deadly cancers affecting women worldwide. Early detection is crucial, particularly for younger women, as traditional screening methods like mammography often struggle with accuracy in cases of dense breast tissue. Infrared thermography offers a non-invasive imaging alternative that enhances early detection by capturing subtle thermal variations indicative of breast abnormalities. This study investigates and compares the performance of various deep learning and machine learning models in analyzing thermographic data to classify breast tissue as healthy, benign, or malignant. To maximize detection accuracy, data preprocessing, feature extraction, and dimensionality reduction were implemented to isolate distinguishing characteristics across tissue types. Leveraging advanced feature extraction and visualization techniques inspired by geospatial data methodologies, we evaluated several deep learning architectures and classical classifiers using the DRM-IR and Breast Thermography Mendeley thermal datasets. Among the tested models, the ResNet152 architecture combined with a Support Vector Machine (SVM) classifier delivered the highest performance, achieving 97.62% accuracy, 95.79% precision, 98.53% recall, 94.52% specificity, an F1 score of 97.16%, an area under the curve (AUC) of 99%, a latency of 0.06 s, and CPU utilization of 88.66%. These findings underscore the potential of integrating infrared thermography with advanced deep learning and machine learning approaches to significantly improve the accuracy and efficiency of breast cancer detection, supporting its role as a valuable tool for early diagnosis. Full article

Breast cancer is one of the leading causes of death for women worldwide, and early detection can help reduce the death rate. Infrared thermography has gained popularity as a non-invasive and rapid method for detecting this pathology and can be further enhanced by applying neural networks to extract spatial and even temporal data derived from breast thermographic images if they are acquired sequentially. In this study, we evaluated hybrid convolutional-recurrent neural network (CNN-RNN) models based on five state-of-the-art pre-trained CNN architectures coupled with three RNNs to discern tumor abnormalities in dynamic breast thermographic images. The hybrid architecture that achieved the best performance for detecting breast cancer was VGG16-LSTM, which showed accuracy (ACC), sensitivity (SENS), and specificity (SPEC) of 95.72%, 92.76%, and 98.68%, respectively, with a CPU runtime of 3.9 s. However, the hybrid architecture that showed the fastest CPU runtime was AlexNet-RNN with 0.61 s, although with lower performance (ACC: 80.59%, SENS: 68.52%, SPEC: 92.76%), but still superior to AlexNet (ACC: 69.41%, SENS: 52.63%, SPEC: 86.18%) with 0.44 s. Our findings show that hybrid CNN-RNN models outperform stand-alone CNN models, indicating that temporal data recovery from dynamic breast thermographs is possible without significantly compromising classifier runtime. Full article

The use of artificial intelligence-based techniques to solve engineering problems is increasing. One of the most challenging tasks facing industry is the timely diagnosis of failures in electromechanical systems, as they are an essential part of production systems. In this sense, the earlier the detection, the higher the economic loss reduction. For this reason, this work proposes the development of a new methodology based on infrared thermography and an artificial intelligence-based classifier for the detection of multiple faults in an electromechanical system. The proposal combines the intensity profile of the grey-scale image, the use of Fast Fourier Transform and an artificial neural network to perform the detection of twelve states for the state of an electromechanical system: healthy, bearing defect, broken rotor bar, misalignment and gear wear on the gearbox. From the experimental setup, 50 thermographic images were obtained for each state. The method was implemented and tested under different conditions to verify its reliability. The results show that the precision, accuracy, recall and F1-score are higher than 99%. Thus, it can be concluded that it is possible to detect multiple conditions in an electromechanical system using the intensity profile and an artificial neural network, achieving good accuracy and reliability. Full article

Medical thermography is a non-invasive technique that allows the measurement of the temperature of the human body surface, exploiting the heat emitted by the body through the skin in the form of infrared electromagnetic radiation. Recently, smartphone-based thermography (ST) has drawn considerable attention. This scoping review (SR) aims to describe its current applications and reliability based on currently available research findings, also taking into account the medico-legal implications linked to its use. A search of the sources was conducted on multiple databases (PubMed, Scopus, Cochrane, Lilacs, Google Scholar). Based on a set of eligibility criteria, all articles deemed useful were included in the SR. Collected data, processed with descriptive statistics, are then discussed. From the initial 241 results, after duplicate removal and full-text reading based on inclusion/exclusion criteria, 20 articles were classified according to the main characteristics and indications and outcomes are highlighted based on clinical evidence. The most frequently documented fields of ST are wound care management and vascular surgery. Other disciplines are less explored (dentistry, ophthalmology, otorhinolaryngology, orthopedics, etc.). Practicality, operational simplicity and affordability of mobile thermographic devices are the chief strengths of this technology. Comparative studies with traditional thermal imaging methods are poor in terms of the number of patients analyzed but this technology showed high sensitivity and accuracy in the large number of patients enrolled in observational studies, encouraging the development of further operational protocols in all medical specialties. Gaining a deeper understanding of such techniques will also help settle the medico-legal issues which may arise from the clinical implementation of ST, thus appraising its reliability and safety from that perspective as well. Full article

Considering animal welfare, the free-range laying hen farming model is increasingly gaining attention. However, in some countries, large-scale farming still relies on the cage-rearing model, making the focus on the welfare of caged laying hens equally important. To evaluate the health status of caged laying hens, a dataset comprising visible light and thermal infrared images was established for analyses, including morphological, thermographic, comb, and behavioral assessments, enabling a comprehensive evaluation of the hens’ health, behavior, and population counts. To address the issue of insufficient data samples in the health detection process for individual and group hens, a dataset named BClayinghens was constructed containing 61,133 images of visible light and thermal infrared images. The BClayinghens dataset was completed using three types of devices: smartphones, visible light cameras, and infrared thermal cameras. All thermal infrared images correspond to visible light images and have achieved positional alignment through coordinate correction. Additionally, the visible light images were annotated with chicken head labels, obtaining 63,693 chicken head labels, which can be directly used for training deep learning models for chicken head object detection and combined with corresponding thermal infrared data to analyze the temperature of the chicken heads. To enable the constructed deep-learning object detection and recognition models to adapt to different breeding environments, various data enhancement methods such as rotation, shearing, color enhancement, and noise addition were used for image processing. The BClayinghens dataset is important for applying visible light images and corresponding thermal infrared images in the health detection, behavioral analysis, and counting of caged laying hens under large-scale farming. Full article

Brazilian poultry slaughterhouses employ many workers, consequently exposing them to various ergonomic risks. This study aimed to analyze the effects of knife use and overlapping gloves on the finger temperatures of poultry slaughterhouse workers. Employees (n = 571) from seven Brazilian poultry slaughterhouses participated in this cross-sectional study. A Flir^® T450SC infrared camera was used to record thermographic images of the workers’ hands. The workers were interviewed about work organization, cold thermal sensations, and the perception of upper-limb musculoskeletal discomfort. Dependent and independent sample t-tests and binary logistic regression models were applied. The results proved that the workers wore up to five overlapping gloves and had at least one finger with temperatures of ≤15 °C (46.6%) or ≤24 °C (98.1%). Workers that used a knife and wore a chainmail (CM) glove on their non-dominant hand had average finger temperatures significantly colder on the palmar surface than the anti-cut (AC) glove group (p = 0.029). The chance of one worker who wore a CM glove to have finger temperatures of ≤15 °C was 2.26 times greater than a worker who wore an AC glove. Those who wore an AC glove and those wearing a CM glove presented average overall finger temperatures significantly lower on the non-dominant hand (products) than the dominant hand (knife) (p < 0.001). Full article

Water stress on crops can severely disrupt crop growth and reduce yields, requiring the accurate and prompt diagnosis of crop water stress, especially in semiarid regions. Infrared thermal imaging cameras are effective tools to monitor the spatial distribution of canopy temperature (Tc), which is the basis of the daily water stress index (DWSI) calculation. This research aimed to evaluate the variability of plant water stress under different soil cover conditions through geostatistical techniques, using detailed thermographic images of Neem canopies in the Brazilian northeastern semiarid region. Two experimental plots were established with Neem cropped under mulch and bare soil conditions. Thermal images of the leaves were taken with a portable thermographic camera and processed using Python language and the OpenCV database. The application of the geostatistical technique enabled stress indicator mapping at the leaf scale, with the spherical and exponential models providing the best fit for both soil cover conditions. The results showed that the highest levels of water stress were observed during the months with the highest air temperatures and no rainfall, especially at the apex of the leaf and close to the central veins, due to a negative water balance. Even under extreme drought conditions, mulching reduced Neem physiological water stress, leading to lower plant water stress, associated with a higher soil moisture content and a negative skewness of temperature distribution. Regarding the mapping of the stress index, the sequential Gaussian simulation method reduced the temperature uncertainty and the variation on the leaf surface. Our findings highlight that mapping the Water Stress Index offers a robust framework to precisely detect stress for agricultural management, as well as soil cover management in semiarid regions. These findings underscore the impact of meteorological and planting conditions on leaf temperature and baseline water stress, which can be valuable for regional water resource managers in diagnosing crop water status more accurately. Full article