Search Results (237)

Background/Objectives: Cellulite is a common aesthetic condition in women, traditionally assessed using visual inspection and palpation-based scales that are inherently subjective. Therefore, image-based methods that may support standardized severity grading are of growing interest. To evaluate infrared thermography as an imaging-based method for grading cellulite severity and to perform methodological validation of a newly developed thermographic classification scale by comparing it with clinical palpation and anthropometric parameters. Methods: This retrospective, non-interventional study analyzed anonymized clinical and thermographic data from 81 women with clinically assessed cellulite. Cellulite severity was evaluated using the Nürnberger–Müller palpation scale and a newly developed five-point thermographic scale based on skin surface temperature differentials and histogram pattern analysis. The associations between the assessment methods were evaluated using ordinal statistical measures, and agreement was assessed using weighted Cohen’s kappa statistics. Results: Thermographic grading demonstrated high agreement with palpation-based assessment, with a percentage agreement of 93.8% and an almost perfect agreement based on weighted Cohen’s κ. A strong ordinal association was observed between the methods. Thermography consistently classified a subset of cases as one grade higher compared with palpation. No statistically significant associations were observed between thermographic grade and body mass index or waist-to-hip ratio. Conclusions: Infrared thermography enables image-based grading of cellulite severity and shows a strong concordance with established palpation scales. The proposed thermographic classification provides preliminary methodological validation of an imaging-based grading approach. Further multicenter studies involving multiple assessors and diverse populations are required to assess reproducibility, specificity, and potential clinical applicability. Full article

The study proposes a sensor-based experimental protocol for quantifying dynamic facial temperature changes during face mask use by means of infrared thermography (IRT). Eight face masks, including filtering respirators, surgical masks, and one textile mask, were evaluated on three participants under controlled indoor conditions. Thermographic data were acquired at four defined measurement stages: prior to mask application, immediately after donning, after 15 min of continuous wear, and immediately after removal. The measurements reveal a reproducible temporal temperature pattern across participants and mask types, consisting of an initial cooling phase, subsequent heat accumulation during wear, and a pronounced temperature increase following removal. Thermal variations were observed both in mask-covered and uncovered facial regions. The inner canthus exhibited high sensitivity to these changes, supporting its use as a stable reference area. The study demonstrates the suitability of IRT for protocol-driven, non-contact assessment of dynamic facial thermal response during mask use. Full article

In this paper, the impact of negative bias temperature instabilities (NBTIs) on commercial power p-channel Vertical Double-Diffused MOS (VDMOS) transistors from the standpoint of practical applications was analyzed. The effects of NBTI are one of the main reliability concerns for this type of device, so it is necessary to investigate how these effects influence various applications. A series of experiments were carried out including negative bias temperature stressing, infra-red thermographic recording and circuit characterization, with the goal of evaluating the effects of negative bias temperature stressing on the self-heating of samples in load-driving circuits operating with higher currents and circuit performance of a CMOS inverter circuit containing the examined samples. The findings suggest that negative bias temperature stressing-induced threshold voltage shift directly affects increased self-heating in load-driving circuits and that it also directly affects transfer and dynamics characteristics in CMOS inverters. Full article

A computational approach for early breast cancer detection using Dynamic Infrared Thermography (DIT) was developed. Thermograms are represented by multivariate time series extracted from thermal hotspots in the breast, capturing five features: maximum and mean temperature, spatial heterogeneity, heat flux, and tumor depth, over 20 thermograms. Features are estimated based on the inverse solution of the Pennes bio-heat equation. Classification is performed using a Time Series Forest (TSF) and a Long Short-Term Memory (LSTM) network. The TSF achieved an accuracy of 86%, while the LSTM reached 94% accuracy. These results indicate that dynamic thermal responses under cold-stress conditions reflect tumor angiogenesis and metabolic activity, demonstrating the potential of combining multivariate thermographic sequences, biophysical modeling, and machine learning for non-invasive breast cancer screening. Full article

Photovoltaic (PV) systems represent one of the pillars of the global energy transition, yet their reliability and long-term efficiency are constantly threatened by hidden defects and progressive degradation. Early and precise identification of such anomalies is essential for ensuring safety, enhancing performance, and facilitating predictive maintenance plans. Infrared thermography (IRT) is a non-invasive and cost-effective technique for the inspection of PV modules. This study proposes an efficient attentive U-Net architecture for the semantic segmentation of thermographic images, aimed at supporting predictive maintenance and power loss assessment. The model integrates squeeze-and-excitation (SE) and attention gate (AG) modules with atrous spatial pyramid pooling (ASPP), achieving an optimal balance between accuracy and computational complexity. A comprehensive ablation study, including input resolution and module combinations, was conducted on a dataset of 500 thermograms annotated into six defect classes. The proposed configuration (256 × 256 input) achieved a mean Intersection over Union (mIoU) of 81.4% and a macro-F1 score of 87.5%, outperforming U-Net and DeepLabv3+ by over 4 percentage points, with only 5.24 M parameters and an inference time of 118.6 ms per image. These results confirm the suitability of the framework for energy-oriented fault diagnosis and near real-time monitoring of photovoltaic plants. Full article

Introduction: Infrared thermography (IRT) is a non-invasive, non-ionizing imaging modality capable of rapidly capturing surface temperature variation. In dentistry, particularly orthodontics and TMD evaluation, IRT may serve as a valuable complementary tool to be added in conventional diagnostic protocols. Objective: Correlate possible relationships between thermographic findings of orofacial structures and cephalometric landmarks. Methods: An infrared imaging camera, FLIR^® i7, was used to record the regions of interest, correspondent to the temporal, masseter and orbicular oris muscles, in adolescents (n = 22). Bilateral temperature differences were considered as thermal asymmetries with a conventional threshold of 0.3 °C to distinguish an eventual hyperactivity or hyperfunctions of detrimental structures. The Trevisi cephalometric parameters that were taken into consideration for the study were SNA, SNB, ANB, OccltoSn, Wits relation to base and Molar/canine classes. Results: Most of the participants showed a normal temperature difference ΔΤ for the upper and lower orbicular oris muscle, right vs. left, 96% and 92%, respectively. The other ROIs presented a mixed pattern of thermal asymmetries; however, no statistically significant differences were found when crossed with the cephalometric landmarks. Conclusions: Asymmetrical patterns of infrared thermography can aid on the diagnosis and treatment plan of an orthodontic appointment, since the actual stability of pos-orthodontic treatment is highly dependent on the muscular activity of the tongue and lips, in particular when the patient has atypical swallowing. Our findings suggest that this technique can be used to quantify anatomical landmarks relevant to craniofacial morphology in specific populations, particularly at ages where muscular functional activity is strongly correlated with dentoskeletal development. Full article

The increasing deployment of photovoltaic (PV) installations presents critical challenges related to module safety and efficiency. Early detection of hotspots on PV modules is crucial to prevent degradation and mitigate fire risk. This study investigates the effectiveness of fiber optic sensors, specifically fiber Bragg gratings (FBGs) and distributed temperature sensing (DTS) based on Brillouin backscattering, to detect and localize hotspots on PV modules. Both sensor types successfully identified hotspot occurrences, with validation conducted through simultaneous thermocouple measurements and infrared thermographic imaging. The tests provide a comprehensive analysis of measurement methodologies, highlighting the advantages and limitations of fiber optic sensing techniques. While FBG sensors offer the most precise temperature measurements at the PV module surface, DTS systems demonstrate superior capability in hotspot detection. Full article

This study investigates the Fe₂₂Ni₁₆Co₁₉Mn₁₂Cr₁₆P₁₅ alloy designed to enhance glass-forming ability. The alloy was synthesized by arc melting and examined using infrared thermography, differential scanning calorimetry (DSC), scanning electron microscopy with energy-dispersive spectroscopy (SEM/EDS), and X-ray diffraction (XRD). Thermographic measurements revealed a temperature arrest at ~1007 K associated with eutectic crystallization, accompanied by contraction visible as a flattened ingot surface. DSC confirmed the dominant eutectic transformation (−170.7 J/g). Compared with the previously studied Fe₂₂Ni₁₆Co₁₉Mn₁₂Cr₁₆P₁₅ alloy, this composition showed a simplified transformation sequence and a larger eutectic fraction. DSC of melt-spun ribbons demonstrated a three-step crystallization (659 K, 699 K, 735–773 K, completion ~820 K) with a total enthalpy of 180.4 J/g. The broad crystallization interval (ΔTc ≈ 161 K) indicates enhanced thermal stability compared with simpler Ni–P or Fe–Ni–P–C alloys. SEM/EDS observations revealed eutectic colonies with predominantly rod-like morphology and chemical partitioning in inter-colony regions, favoring precipitation of transition metal phosphides. XRD confirmed four crystalline phases (Fe–Ni, CrCoP, Ni₃P, MnNiP) in ingots, while ribbons exhibited a fully amorphous structure. These findings demonstrate that Fe₂₂Ni₁₆Co₁₉Mn₁₂Cr₁₆P₁₅ possesses good glass-forming ability but forms multiple phosphides under slower cooling. Precise cooling control is thus essential for tailoring its amorphous or crystalline state. Full article

This study presents comprehensive experimental, analytical, and numerical analyses of heat transfer during countercurrent flow of Fluorinert FC-72 and distilled water within a multi-mini-channel (MMCH) module under steady-state conditions. The experimental investigation was conducted in a test section inclined at an angle of 165 degrees relative to the horizontal plane, utilizing an infrared camera to measure the external temperature of the heated mini-channel (MCH) wall. The test module comprised twelve MCHs: six hot (HMCH) and six cold mini-channels (CMCH), each with a rectangular cross-section. The dimensions of each MCH are 140 mm in length, 18.3 mm in width, and 1.5 mm in depth, with a hydraulic diameter of d_h = 2.77 mm. The heating system on the top wall of the external heated copper comprises a halogen heating lamp. Results include infrared thermographs, temperature distributions, and heat transfer coefficients (HTCs) along the channels. Local HTCs were calculated using a one-dimensional (1D) approach, a simple analytical method, at interfaces such as the heated plate—HMCHs, HMCHs—separating plate, separating plate—CMCHs, and CMCHs—closing plate. CFD simulations conducted with Simcenter STAR-CCM+ incorporated empirical data from experiments, using parameters like temperature, pressure, velocity profiles, and heat flux density to determine HTCs. The maximum difference between the 1D method and CFD results was 29% at the HMCHs/separating plate interface. In comparison, the minimum was 13.5% at the separating plate/CMCHs interface, with an average across all channels and heat flux densities. Full article

This work examines how thermal turbulence patterns can be identified on the blades of operating wind turbines—an issue that plays a key role in preventive maintenance and overall safety assurance. Using the publicly available KI-VISIR dataset, containing annotated infrared images collected under real-world operating conditions, four object detection architectures were evaluated: YOLOv8, the baseline YOLOv9, the transformer-based RT-DETR, and an enhanced variant introduced as A-BiYOLOv9. The proposed approach extends the YOLOv9 backbone with convolutional block attention modules (CBAM) and integrates a bidirectional feature pyramid network (BiFPN) in the neck to improve feature fusion. All models were trained for thirty epochs on single-class turbulence annotations. The experiments confirm that YOLOv8 provides fast and efficient detection, YOLOv9 delivers higher accuracy and more stable convergence, and RT-DETR exhibits strong precision and consistent localization performance. A-BiYOLOv9 maintains stable and reliable accuracy even when the thermal patterns vary significantly between scenes. These results confirm that attention-augmented and feature-fusion-centric architectures improve detection sensitivity and reliability in the thermal domain. Consequently, the proposed A-BiYOLOv9 represents a promising candidate for real-time, contactless thermographic monitoring of wind turbines, with the potential to extend turbine lifespan through predictive maintenance strategies. Full article

Background: Infrared thermography and acute muscle fatigue are often correlated in sports medicine for assessing muscle health during and after exercise, but there are no known studies describing the response throughout exercise; therefore, this study aims to observe the variation in skin temperature (Tsk) during the execution of a maximal muscle fatigue protocol using concentric exercises in the triceps suralis. Methods: An open cross-sectional descriptive observational study of the posterior region of the leg (triceps suralis) was performed using 98 healthy subjects. Volunteers were subjected to a maximal fatigue protocol, with thermographic images analyzed at 25%, 50%, 75%, and 100% cumulative maximal fatigue. Results: Results showed a significant difference in time (p = 0.039; η²p = 0.026) and side (p =≤ 0.001; η²p = 0.120). Tukey’s post hoc analysis detected a significant difference in Tsk B between the R and L side (R, 30.3 ± 1.39 °C; L, 30.2 ± 1.39 °C), in Tsk at 25% (R, 30.2 ± 1. 27 °C; L; 30.1 ± 1.27 °C), in Tsk at 50% (R, 30.2 ± 1.29 °C; L, 30.1 ± 1.29 °C), and in Tsk at 75% (R, 30.3 ± 1.33 °C; L, 30.2 ± 1.33 °C). Conclusions: The results observed in this study show that infrared thermography is a valid tool for the measurement, analysis, and quantification of the tissue metabolic response of the muscular system, pre, during, and after exercise. Therefore, we believe that, for future studies, it would be interesting to find relationships between Tsk variations and other performance and metabolic variables. The focus on a single muscle and not directly measuring muscle activity are two limitations of this study. Full article

The study presents original research focused on improving the reliability and energy efficiency of electric railway turnout heating systems under severe winter conditions. An innovative diagnostic methodology using high-resolution infrared thermography was developed and applied to evaluate heating uniformity and technical performance within the Polish railway infrastructure. Field investigations were carried out on operational turnouts at Gdańsk Osowa and Międzylesie stations, covering both conventional EOR systems and the advanced ESAR system. The results demonstrated that the ESAR system effectively prevented ice and snow accumulation while enabling up to a 30% reduction in active power supplied to heating elements, resulting in annual energy savings of approximately 750 kWh per turnout (29% compared with the reference system). Incorporating radiative overlays in ESAR allowed lower average surface temperatures and improved heat distribution efficiency. Temperature and energy indicators confirmed significantly higher performance of ESAR, with annual CO₂ emissions reduced by 447.75 kg and air pollutants (SO_x, NO_x, CO, particulates) by around 30%. The proposed thermographic approach proved to be a non-invasive and efficient diagnostic tool, supporting adaptive control, enhanced operational reliability, and reduced environmental impact of turnout heating systems. Full article

Breast cancer is the most commonly diagnosed cancer in women globally and represents the leading cause of mortality related to malignant tumors. Currently, healthcare professionals are focused on developing and implementing innovative techniques to improve the early detection of this disease. Thermography, studied as a complementary method to traditional approaches, captures infrared radiation emitted by tissues and converts it into data about skin surface temperature. During tumor development, angiogenesis occurs, increasing blood flow to support tumor growth, which raises the surface temperature in the affected area. Automatic classification techniques have been explored to analyze thermographic images and develop an optimal classification tool to identify thermal anomalies. This study aims to design a concise description using statistical and texture features to accurately classify thermograms as control or highly probable to be cancer (with thermal anomalies). The importance of employing a short description lies in facilitating interpretation by medical professionals. In contrast, a characterization based on a large number of variables could make it more challenging to identify which values differentiate the thermograms between groups, thereby complicating the explanation of results to patients. A maximum accuracy of 91.97% was achieved by applying only seven features and using a Coarse Decision Tree (DT) classifier and robust Machine Learning (ML) model, which demonstrated competitive performance compared with previously reported studies. Full article

Arthritis and arthrosis are prevalent joint diseases that cause pain and disability, and their early diagnosis is crucial for preventing irreversible damage. Conventional diagnostic methods such as X-ray, ultrasound, and MRI have limitations in early detection, prompting interest in alternative techniques. This work presents the design and clinical evaluation of a prototype device for non-invasive early diagnosis of arthritis (inflammatory joint disease) and arthrosis (osteoarthritis) using infrared thermography and deep neural networks. The portable prototype integrates a Raspberry Pi 4 microcomputer, an infrared thermal camera, and a touchscreen interface, all housed in a 3D-printed PLA enclosure. A custom Flask-based application enables two operational modes: (1) thermal image acquisition for training data collection, and (2) automated diagnosis using a pre-trained ResNet50 deep learning model. A clinical study was conducted at a university clinic in a temperature-controlled environment with 100 subjects (70% with arthritic conditions and 30% healthy). Thermal images of both hands (four images per hand) were captured for each participant, and all patients provided informed consent. The ResNet50 model was trained to classify three classes (healthy, arthritis, and arthrosis) from these images. Results show that the system can effectively distinguish healthy individuals from those with joint pathologies, achieving an overall test accuracy of approximately 64%. The model identified healthy hands with high confidence (100% sensitivity for the healthy class), but it struggled to differentiate between arthritis and arthrosis, often misclassifying one as the other. The prototype’s multiclass ROC (Receiver Operating Characteristic) analysis further showed excellent discrimination between healthy vs. diseased groups (AUC, Area Under the Curve ~1.00), but lower performance between arthrosis and arthritis classes (AUC ~0.60–0.68). Despite these challenges, the device demonstrates the feasibility of AI-assisted thermographic screening: it is completely non-invasive, radiation-free, and low-cost, providing results in real-time. In the discussion, we compare this thermography-based approach with conventional diagnostic modalities and highlight its advantages, such as early detection of physiological changes, portability, and patient comfort. While not intended to replace established methods, this technology can serve as an early warning and triage tool in clinical settings. In conclusion, the proposed prototype represents an innovative application of infrared thermography and deep learning for joint disease screening. With further improvements in classification accuracy and broader validation, such systems could significantly augment current clinical practice by enabling rapid and non-invasive early diagnosis of arthritis and arthrosis. Full article

To implement predictive maintenance of units in the practice of metallurgical manufacturers, computer information and diagnostic systems are being developed to assess the current state of individual units throughout their entire life cycle. This publication presents the results of a study on developing an infrared diagnostic system for predictive maintenance of converter units in the non-ferrous metallurgy industry. A 3D mathematical model of the transient heat transfer in the wall of a real operating unit has been developed and numerically implemented to study, analyze, and diagnose surface temperature fields resulting from wear and local damage. To adjust the operation of the mathematical model, the design parameters and the results for operating and technological parameters from an industrial experiment are taken into consideration. Using the model, a full-factor experiment was simulated to study the surface temperature fields resulting from the erosion wear of the wall and the presence of local damage. Based on the simulation results, the optimal time range for thermographic monitoring is determined. A regression dependence was derived to predict the refractory wall wear as a function of the outer surface temperature of the converter unit. The results are part of a comprehensive investigation aimed at developing thermal imaging techniques for converter units in non-ferrous metallurgy. Full article