Quantitative estimation of temperature variations in plantar angiosomes: a study case for diabetic foot

Pilot Implementation of a Thermography-Driven Preventive Foot Care Protocol for Diabetes-Related Foot Ulcers in Community Health Centers: Non-Randomized Controlled Trial

AimThe purpose of this study was to clarify the effectiveness and implementation issues of a thermography-driven preventive foot care protocol for diabetes-related foot ulcers in Indonesian community health centers.MethodsIn the two trial centers examined between May 2023 and May 2024, one provided protocol-based care (Intervention group; n = 71), while the other provided standard education (Control group; n = 71). Participants were monitored for ulceration over a 1-year period.ResultsThere was no significant difference in the number of participants with ulceration between the intervention and control group (6 vs 8 cases, p = 0.573). However, there was a shorter time to ulceration (203.8 ± 8.7 vs 350.0 ± 13.9 days, p = 0.048) in the intervention group. Over the 1-year follow-up, protocol compliance was 100% for examinations, but 0% for callus and nail care. Participants without risk factors, such as neuropathy or angiopathy, also developed ulcers.ConclusionsResults suggested this protocol was ineffective in the community health center. It will be necessary to clarify the risk factors for foot ulcers in the target population, establish an appropriate protocol, prepare an environment for its implementation, and then conduct a randomized controlled trial in the future.

Thermal simulation of the lower limb in vascular medicine: A proof-of-concept by using computed tomography images

Simulations of physiology based on patient-specific anatomical structures have several potential applications in medicine. A few fields, such as radiotherapy and neurophysiology already utilize such methods in clinical practice, yet a number of disciplines could benefit from similar technologies, especially when imaging data is already available. The major problem in patient-specific simulation is the data conversion to simulation-compatible form i.e., data preparation and the coupling of the underlying physics to the anatomical model. In this work we present such a methodology in the context of vascular medicine, consisting of a three-dimensional blood flow-temperature simulation model of the lower limb built from computed tomography data. We also simulate a clinical condition of chronic limb-threatening ischemia, a severe complication of peripheral arterial disease. This proof-of-concept model simulates the limb's surface temperature with respect to the vascular structure. The methodology, depicting accurate patient anatomy, is a promising step towards individualized physiological simulations in vascular medicine, although more research and validation are required. Such a model could eventually outline a deeper understanding of the relation between vascular changes and peripheral thermal behavior.

Digital infrared thermography and machine learning for diabetic foot assessment: thermal patterns and classification

Objectives

Digital infrared thermography is a noninvasive tool used for assessing diseases, including the diabetic foot. This study aims to analyze thermal patterns of the foot sole in patients with type 2 diabetes mellitus using thermography and explore correlations with clinical variables. Additionally, a machine learning approach was developed for classification.

Methods

A total of 23 diabetic patients and 27 age- and sex-matched controls were included. Thermograms of the plantar foot surface were acquired and segmented into regions of interest. Mean foot temperature and temperature change index were calculated from predefined regions of interest. Pearson's correlation analysis was conducted for temperature measures, glycated hemoglobin, and body mass index. A two-layered cross-validation model using principal component analysis and support vector machines were employed for classification.

Results

Significant positive correlations were found between mean foot temperature and glycated hemoglobin (ρ = 0.44, p = 0.0015), as well as between mean foot temperature and body mass index (ρ = 0.35, p = 0.013). Temperature change index did not show significant correlations with clinical variables. The machine learning model achieved high overall accuracy (90%) and specificity (100%) with a moderate sensitivity (78.3%) for classifying diabetic and control groups based on thermal data.

Conclusions

Thermography combined with machine learning shows potential for assessing diabetic foot complications. Correlations between mean foot temperature and clinical variables suggest foot temperature changes as potential indicators. The machine learning model demonstrates promising accuracy for classification, suitable for screening purposes. Further research is needed to understand underlying mechanisms and establish clinical utility in diagnosing and managing diabetic foot complications.

An overview of current developments and methods for identifying diabetic foot ulcers: A survey

Diabetic foot ulcers (DFUs) present a substantial health risk across diverse age groups, creating challenges for healthcare professionals in the accurate classification and grading. DFU plays a crucial role in automated health monitoring and diagnosis systems, where the integration of medical imaging, computer vision, statistical analysis, and gait information is essential for comprehensive understanding and effective management. Diagnosing DFU is imperative, as it plays a major role in the processes of diagnosis, treatment planning, and neuropathy research within automated health monitoring and diagnosis systems. To address this, various machine learning and deep learning‐based methodologies have emerged in the literature to support healthcare practitioners in achieving improved diagnostic analyses for DFU. This survey paper investigates various diagnostic methodologies for DFU, spanning traditional statistical approaches to cutting‐edge deep learning techniques. It systematically reviews key stages involved in diabetic foot ulcer classification (DFUC) methods, including preprocessing, feature extraction, and classification, explaining their benefits and drawbacks. The investigation extends to exploring state‐of‐the‐art convolutional neural network models tailored for DFUC, involving extensive experiments with data augmentation and transfer learning methods. The overview also outlines datasets commonly employed for evaluating DFUC methodologies. Recognizing that neuropathy and reduced blood flow in the lower limbs might be caused by atherosclerotic blood vessels, this paper provides recommendations to researchers and practitioners involved in routine medical therapy to prevent substantial complications. Apart from reviewing prior literature, this survey aims to influence the future of DFU diagnostics by outlining prospective research directions, particularly in the domains of personalized and intelligent healthcare. Finally, this overview is to contribute to the continual evolution of DFU diagnosis in order to provide more effective and customized medical care.

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Region-wise severity analysis of diabetic plantar foot thermograms

OBJECTIVES

Diabetic foot ulcers (DFU) can be avoided if symptoms of diabetic foot complications are detected early and treated promptly. Early detection requires regular examination, which might be limited for many reasons. To identify affected or potentially affected regions in the diabetic plantar foot, the region-wise severity of the plantar foot must be known.

METHODS

A novel thermal diabetic foot dataset of 104 subjects was developed that is suitable for Indian healthcare conditions. The entire plantar foot thermogram is divided into three parts, i.e., forefoot, midfoot, and hindfoot. The division of plantar foot is based on the prevalence of foot ulcers and the load on the foot. To classify the severity levels, conventional machine learning (CML) techniques like logistic regression, decision tree, KNN, SVM, random forest, etc., and convolutional neural networks (CNN), such as EfficientNetB1, VGG-16, VGG-19, AlexNet, InceptionV3, etc., were applied and compared for robust outcomes.

RESULTS

The study successfully developed a thermal diabetic foot dataset, allowing for effective classification of diabetic foot ulcer severity using the CML and CNN techniques. The comparison of different methods revealed variations in performance, with certain approaches outperforming others.

CONCLUSIONS

The region-based severity analysis offers valuable insights for targeted interventions and preventive measures, contributing to a comprehensive assessment of diabetic foot ulcer severity. Further research and development in these techniques can enhance the detection and management of diabetic foot complications, ultimately improving patient outcomes.

Assessment of chronic limb threatening ischemia using thermal imaging

OBJECTIVES

Current chronic limb threatening ischemia (CLTI) diagnostics require expensive equipment, using ionizing radiation or contrast agents, or summative surrogate methods lacking in spatial information. Our aim is to develop and improve contactless, non-ionizing and cost-effective diagnostic methods for CLTI assessment with high spatial accuracy by utilizing dynamic thermal imaging and the angiosome concept.

APPROACH

Dynamic thermal imaging test protocol was suggested and implemented with a number of computational parameters. Pilot data was measured from 3 healthy young subjects, 4 peripheral artery disease (PAD) patients and 4 CLTI patients. The protocol consists of clinical reference measurements, including ankle- and toe-brachial indices (ABI, TBI), and a modified patient bed for hydrostatic and thermal modulation tests. The data was analyzed using bivariate correlation.

RESULTS

The thermal recovery time constant was on average higher for the PAD (88%) and CLTI (83%) groups with respect to the healthy young subjects. The contralateral symmetry was high for the healthy young group and low for the CLTI group. The recovery time constants showed high negative correlation to TBI (ρ = -0.73) and ABI (ρ = -0.60). The relation of these clinical parameters to the hydrostatic response and absolute temperatures (|ρ|<0.3) remained unclear.

CONCLUSION

The lack of correlation for absolute temperatures or their contralateral differences with the clinical status, ABI and TBI disputes their use in CLTI diagnostics. Thermal modulation tests tend to augment the signs of thermoregulation deficiencies and accordingly high correlations were found with all reference metrics. The method is promising for establishing the connection between impaired perfusion and thermography. The hydrostatic modulation test requires more research with stricter test conditions.

Feature Ranking by Variational Dropout for Classification Using Thermograms from Diabetic Foot Ulcers

Diabetes mellitus presents a high prevalence around the world. A common and long-term derived complication is diabetic foot ulcers (DFUs), which have a global prevalence of roughly 6.3%, and a lifetime incidence of up to 34%. Infrared thermograms, covering the entire plantar aspect of both feet, can be employed to monitor the risk of developing a foot ulcer, because diabetic patients exhibit an abnormal pattern that may indicate a foot disorder. In this study, the publicly available INAOE dataset composed of thermogram images of healthy and diabetic subjects was employed to extract relevant features aiming to establish a set of state-of-the-art features that efficiently classify DFU. This database was extended and balanced by fusing it with private local thermograms from healthy volunteers and generating synthetic data via synthetic minority oversampling technique (SMOTE). State-of-the-art features were extracted using two classical approaches, LASSO and random forest, as well as two variational deep learning (DL)-based ones: concrete and variational dropout. Then, the most relevant features were detected and ranked. Subsequently, the extracted features were employed to classify subjects at risk of developing an ulcer using as reference a support vector machine (SVM) classifier with a fixed hyperparameter configuration to evaluate the robustness of the selected features. The new set of features extracted considerably differed from those currently considered state-of-the-art but provided a fair performance. Among the implemented extraction approaches, the variational DL ones, particularly the concrete dropout, performed the best, reporting an F1 score of 90% using the aforementioned SVM classifier. In comparison with features previously considered as the state-of-the-art, approximately 15% better performance was achieved for classification.

Segmentation of Plantar Foot Thermal Images Using Prior Information

Diabetic foot (DF) complications are associated with temperature variations. The occurrence of DF ulceration could be reduced by using a contactless thermal camera. The aim of our study is to provide a decision support tool for the prevention of DF ulcers. Thus, the segmentation of the plantar foot in thermal images is a challenging step for a non-constraining acquisition protocol. This paper presents a new segmentation method for plantar foot thermal images. This method is designed to include five pieces of prior information regarding the aforementioned images. First, a new energy term is added to the snake of Kass et al. in order to force its curvature to match that of the prior shape, which has a known form. Second, we defined the initial contour as the downsized prior-shape contour, which is placed inside the plantar foot surface in a vertical orientation. This choice makes the snake avoid strong false boundaries present outside the plantar region when evolving. As a result, the snake produces a smooth contour that rapidly converges to the true boundaries of the foot. The proposed method is compared to two classical prior-shape snake methods, that of Ahmed et al. and that of Chen et al. A database of 50 plantar foot thermal images was processed. The results show that the proposed method outperforms the previous two methods with a root-mean-square error of 5.12 pixels and a dice similarity coefficient of 94%. The segmentation of the plantar foot regions in the thermal images helped us to assess the point-to-point temperature differences between the two feet in order to detect hyperthermia regions. The presence of such regions is the pre-sign of ulcers in the diabetic foot. Furthermore, our method was applied to hyperthermia detection to illustrate the promising potential of thermography in the case of the diabetic foot. Associated with a friendly acquisition protocol, the proposed segmentation method is the first step for a future mobile smartphone-based plantar foot thermal analysis for diabetic foot patients.

The Current State of Optical Sensors in Medical Wearables

Optical sensors play an increasingly important role in the development of medical diagnostic devices. They can be very widely used to measure the physiology of the human body. Optical methods include PPG, radiation, biochemical, and optical fiber sensors. Optical sensors offer excellent metrological properties, immunity to electromagnetic interference, electrical safety, simple miniaturization, the ability to capture volumes of nanometers, and non-invasive examination. In addition, they are cheap and resistant to water and corrosion. The use of optical sensors can bring better methods of continuous diagnostics in the comfort of the home and the development of telemedicine in the 21st century. This article offers a large overview of optical wearable methods and their modern use with an insight into the future years of technology in this field.

A machine learning model for early detection of diabetic foot using thermogram images

Diabetes foot ulceration (DFU) and amputation are a cause of significant morbidity. The prevention of DFU may be achieved by the identification of patients at risk of DFU and the institution of preventative measures through education and offloading. Several studies have reported that thermogram images may help to detect an increase in plantar temperature prior to DFU. However, the distribution of plantar temperature may be heterogeneous, making it difficult to quantify and utilize to predict outcomes. We have compared a machine learning-based scoring technique with feature selection and optimization techniques and learning classifiers to several state-of-the-art Convolutional Neural Networks (CNNs) on foot thermogram images and propose a robust solution to identify the diabetic foot. A comparatively shallow CNN model, MobilenetV2 achieved an F1 score of ∼95% for a two-feet thermogram image-based classification and the AdaBoost Classifier used 10 features and achieved an F1 score of 97%. A comparison of the inference time for the best-performing networks confirmed that the proposed algorithm can be deployed as a smartphone application to allow the user to monitor the progression of the DFU in a home setting.

Clinical Study of MEBO Combined with Jinhuang Powder for Diabetic Foot with Infection

Background

To investigate the efficacy and safety of MEBO combined with Jinhuang powder for the treatment of diabetic foot with infection.

Methods

From August 2015 to August 2019, patients with diabetic foot in our hospital were divided into the treatment group and control group. The treatment group was treated with moist exposed burn ointment (MEBO) combined with Jinhuang powder, while the control group was treated with MEBO only.

Results

After one week of treatment, the effective rate in the treatment group was 100%, and the effective rate in the control group was only 76%. The difference between the two groups was statistically significant (P < 0.05). The wound pain score was 2.40 ± 1.38 in the treatment group and 3.76 ± 1.85 in the control group. The difference was statistically significant (P < 0.01). After one month of treatment, the effective rate of wound healing was 92.0% in the treatment group and 68% in the control group. The difference between the two groups was statistically significant (P < 0.05).

Conclusion

MEBO combined with Jinhuang powder is effective in treating diabetic foot with infection wound.

Modeling, Fabrication and Integration of Wearable Smart Sensors in a Monitoring Platform for Diabetic Patients

The monitoring of some parameters, such as pressure loads, temperature, and glucose level in sweat on the plantar surface, is one of the most promising approaches for evaluating the health state of the diabetic foot and for preventing the onset of inflammatory events later degenerating in ulcerative lesions. This work presents the results of sensors microfabrication, experimental characterization and FEA-based thermal analysis of a 3D foot-insole model, aimed to advance in the development of a fully custom smart multisensory hardware-software monitoring platform for the diabetic foot. In this system, the simultaneous detection of temperature-, pressure- and sweat-based glucose level by means of full custom microfabricated sensors distributed on eight reading points of a smart insole will be possible, and the unit for data acquisition and wireless transmission will be fully integrated into the platform. Finite element analysis simulations, based on an accurate bioheat transfer model of the metabolic response of the foot tissue, demonstrated that subcutaneous inflamed lesions located up to the muscle layer, and ischemic damage located not below the reticular/fat layer, can be successfully detected. The microfabrication processes and preliminary results of functional characterization of flexible piezoelectric pressure sensors and glucose sensors are presented. Full custom pressure sensors generate an electric charge in the range 0-20 pC, proportional to the applied load in the range 0-4 N, with a figure of merit of 4.7 ± 1 GPa. The disposable glucose sensors exhibit a 0-6 mM (0-108 mg/dL) glucose concentration optimized linear response (for sweat-sensing), with a LOD of 3.27 µM (0.058 mg/dL) and a sensitivity of 21 µA/mM cm2 in the PBS solution. The technical prerequisites and experimental sensing performances were assessed, as preliminary step before future integration into a second prototype, based on a full custom smart insole with enhanced sensing functionalities.

Bimodal microwave and ultrasound phantoms for non-invasive clinical imaging

A precise and thorough methodology is presented for the design and fabrication of bimodal phantoms to be used in medical microwave and ultrasound applications. Dielectric and acoustic properties of human soft tissues were simultaneously mimicked. The phantoms were fabricated using polyvinyl alcohol cryogel (PVA-C) as gelling agent at a 10% concentration. Sucrose was employed to control the dielectric properties in the microwave spectrum, whereas cellulose was used as acoustic scatterer for ultrasound. For the dielectric properties at microwaves, a mathematical model was extracted to calculate the complex permittivity of the desired mimicked tissues in the frequency range from 500 MHz to 20 GHz. This model, dependent on frequency and sucrose concentration, was in good agreement with the reference Cole-Cole model. Regarding the acoustic properties, the speed of sound and attenuation coefficient were employed for validation. In both cases, the experimental data were consistent with the corresponding theoretical values for soft tissues. The characterization of these PVA-C phantoms demonstrated a significant performance for simultaneous microwave and ultrasound operation. In conclusion, PVA-C has been validated as gelling agent for the fabrication of complex multimodal phantoms that mimic soft tissues providing a unique tool to be used in a range of clinical applications.

Plantar thermography predicts freedom from major amputation after endovascular therapy in critical limb ischemic patients

Although plantar thermography can evaluate the immediate perfusion result after an endovascular therapy (EVT) has been performed, a relevant wound outcome study is still lacking.This study was to investigate whether angiosome-based plantar thermography could predict wound healing and freedom from major amputation after EVT in patients with critical limb ischemia (CLI).All 124 patients with CLI (Rutherford category 5 and 6) who underwent EVT from January 2017 to February 2019 were prospectively enrolled. All patients received thermography both before and after EVT. Both wound healing and freedom from major amputation at the 6-month follow-up period were recorded. There were 61 patients in the healing group and 63 patients in the non-healing group, whereas the major amputation total was 14 patients. The mean pre- and post-EVT temperature of the foot was significantly higher in the healing group than in the non-healing group (30.78 °C vs 29.42 °C, P = .015; and 32.34 °C vs 30.96 °C, P = .004, respectively). DIFF2 was significantly lower in the non-healing group (-1.38 vs -0.90, P = .009). DIFF1 and DIFF2 were significantly lower in the amputation group (-1.85 °C vs -1.11 °C, P = .026; and -1.82 °C vs -1.08 °C, P = .004). Multivariate analysis showed that DIFF2 stood out as an independent predictor for freedom from major amputation (hazard ratio 0.51, P = .045). Receiver operating characteristic curve analysis showed a DIFF2 cut-off value of -1.30 °C, which best predicts freedom from major amputation.Plantar thermography is associated with wound healing and helps predict freedom from major amputation in CLI patients undergoing EVT.

Performance Assessment of Low-Cost Thermal Cameras for Medical Applications

* Correspondence: evilla@iac [...].

Unilateral remote temperature monitoring to predict future ulceration for the diabetic foot in remission

OBJECTIVE

Daily remote foot temperature monitoring is an evidence-based preventive practice for patients at risk for diabetic foot complications. Unfortunately, the conventional approach requires comparison of temperatures between contralaterally matched anatomy, limiting practice in high-risk cohorts such as patients with a wound to one foot and those with proximal lower extremity amputation (LEA). We developed and assessed a novel approach for monitoring of a single foot for the prevention and early detection of diabetic foot complications. The purpose of this study was to assess the sensitivity, specificity, and lead time associated with unilateral diabetic foot temperature monitoring.

RESEARCH DESIGN AND METHODS

We used comparisons among ipsilateral foot temperatures and between foot temperatures and ambient temperature as a marker of inflammation. We analyzed data collected from a 129-participant longitudinal study to evaluate the predictive accuracy of our approach. To evaluate classification accuracy, we constructed a receiver operator characteristic curve and reported sensitivity, specificity, and lead time for four different monitoring settings.

RESULTS

Using this approach, monitoring a single foot was found to predict 91% of impending non-acute plantar foot ulcers on average 41 days before clinical presentation with a resultant mean 4.2 alerts per participant-year. By adjusting the threshold temperature setting, the specificity could be increased to 78% with corresponding reduced sensitivity of 53%, lead time of 33 days, and 2.2 alerts per participant-year.

CONCLUSIONS

Given the high incidence of subsequent diabetic foot complications to the sound foot in patients with a history of proximal LEA and patients being treated for a wound, practice of daily temperature monitoring of a single foot has the potential to significantly improve outcomes and reduce resource utilization in this challenging high-risk population.

Infrared Thermography and Soft Computing for Diabetic Foot Assessment

Recent advancements in digital image processing and soft computing techniques have widened the scope of computer aided diagnosis of medical conditions. Many imaging modalities like MRI, CT, PET, Ultrasound combined with soft computing techniques is already contributing to this trend. With the recent inclusion of infrared thermal imaging, the capability of computer aided diagnosis has increased and has become more safe and convenient. Research in this noncontact and noninvasive imaging technology has steadily increased over the last 50 years. Disease diagnosis based on the correlation of surface temperature distribution of skin is being studied at large and has shown promising results. This chapter will give the reader a solid understanding of the theory behind infrared thermography and the use of soft computing techniques applied to medical image analysis, particularly for diabetic foot complication assessment. The issues and challenges to be addressed in using infrared thermography for diagnostic purposes are also discussed. The reader will get a complete overview of building an intelligent diagnostic system using the two sensational topics of research in machine learning and medical imaging—infrared thermography and soft computing.

Mobile Application for Ulcer Detection

Purpose

The number of patients who are suffering from diabetes nowadays is increasing significantly. In some countries, the percentage of population who suffer from this disease can reach up to 20%. Diabetic patients have to deal with their medical conditions and any further complications that this disease may cause. One of the most common conditions is the Diabetic Foot Ulcer (DFU). The early detection of these ulcers can help and may save the life of diabetic patients.

Methods

This work proposes a mobile application for the detection of possible ulcers using a smart phone along with a mobile thermal camera (FLIR ONE). The proposed system captures thermal images of the feet from the thermal camera. The app that identifies ulcers was built using Android studio. The images were acquired to the Samsung S6 smart phone using the FLIR ONE SDK. Image processing techniques were deployed based on Open CV Library. The procedure of detecting possible ulcers was implemented based on analyzing the thermal distribution on the two feet. The developed application compares the difference between the temperature distribution on the two feet and checks if there is a Mean Temperature Difference (MTD) greater than 2.2^oC (the value which indicates a possible ulcer development).

Results

The system was tested under simulated conditions by heating different locations of the subjects' feet to different temperature ranges; one image with temperature less than 2.2^oC and another three images with temperature greater than 2.2^oC. The system has successfully identified possible ulcer regions along with an image showing the location of the possible ulcers.

Conclusions

This work is a very first step in developing a complete mobile thermal imaging system that can be validated clinically in the future.

Computer aided diagnosis of diabetic foot using infrared thermography: A review

Diabetes mellitus (DM) is a chronic metabolic disorder that requires regular medical care to prevent severe complications. The elevated blood glucose level affects the eyes, blood vessels, nerves, heart, and kidneys after the onset. The affected blood vessels (usually due to atherosclerosis) may lead to insufficient blood circulation particularly in the lower extremities and nerve damage (neuropathy), which can result in serious foot complications. Hence, an early detection and treatment can prevent foot complications such as ulcerations and amputations. Clinicians often assess the diabetic foot for sensory deficits with clinical tools, and the resulting foot severity is often manually evaluated. The infrared thermography is a fast, nonintrusive and non-contact method which allows the visualization of foot plantar temperature distribution. Several studies have proposed infrared thermography-based computer aided diagnosis (CAD) methods for diabetic foot. Among them, the asymmetric temperature analysis method is more superior, as it is easy to implement, and yielded satisfactory results in most of the studies. In this paper, the diabetic foot, its pathophysiology, conventional assessments methods, infrared thermography and the different infrared thermography-based CAD analysis methods are reviewed.

Diabetic foot ulcer mobile detection system using smart phone thermal camera: a feasibility study

BACKGROUND

Nowadays, the whole world is being concerned with a major health problem, which is diabetes. A very common symptom of diabetes is the diabetic foot ulcer (DFU). The early detection of such foot complications can protect diabetic patients from any dangerous stages that develop later and may require foot amputation. This work aims at building a mobile thermal imaging system that can be used as an indicator for possible developing ulcers.

METHODS

The proposed system consists of a thermal camera connected to a Samsung smart phone, which is used to acquire thermal images. This thermal imaging system has a simulated temperature gradient of more than 2.2 °C, which represents the temperature difference (in the literature) than can indicate a possible development of ulcers. The acquired images are processed and segmented using basic image processing techniques. The analysis and interpretation is conducted using two techniques: Otsu thresholding technique and Point-to-Point mean difference technique.

RESULTS

The proposed system was implemented under MATLAB Mobile platform and thermal images were analyzed and interpreted. Four testing images (feet images) were used to test this procedure; one image with any temperature variation to the feet, and three images with skin temperature increased to more than 2.2 °C introduced at different locations. With the two techniques applied during the analysis and interpretation stage, the system was successful in identifying the location of the temperature increase.

CONCLUSION

This work successfully implemented a mobile thermal imaging system that includes an automated method to identify possible ulcers in diabetic patients. This may give diabetic patients the ability for a frequent self-check of possible ulcers. Although this work was implemented in simulated conditions, it provides the necessary feasibility to be further developed and tested in a clinical environment.

A Study of Thermistor Performance within a Textile Structure

Textiles provide an ideal structure for embedding sensors for medical devices. Skin temperature measurement is one area in which a sensor textile could be particularly beneficial; pathological skin is normally very sensitive, making the comfort of anything placed on that skin paramount. Skin temperature is an important parameter to measure for a number of medical applications, including for the early detection of diabetic foot ulcer formation. To this end an electronic temperature-sensor yarn was developed by embedding a commercially available thermistor chip into the fibres of a yarn, which can be used to produce a textile or a garment. As part of this process a resin was used to encapsulate the thermistor. This protects the thermistor from mechanical and chemical stresses, and also allows the sensing yarn to be washed. Building off preliminary work, the behaviour and performance of an encapsulated thermistor has been characterised to determine the effect of encapsulation on the step response time and absolute temperature measurements. Over the temperature range of interest only a minimal effect was observed, with step response times varying between 0.01–0.35 s. A general solution is presented for the heat transfer coefficient compared to size of the micro-pod formed by the encapsulation of the thermistor. Finally, a prototype temperature-sensing sock was produced using a network of sensing yarns as a demonstrator of a system that could warn of impending ulcer formation in diabetic patients.

Infrared thermography as option for evaluating the treatment effect of percutaneous transluminal angioplasty by patients with peripheral arterial disease

Aim of this study was to evaluate the possible use of infrared thermography as a supplementary method to the ankle-brachial index used in assessing the treatment effect of percutaneous transluminal angioplasty. The study included 21 patients, mean age was 60.22 years. Healthy control group included 20 persons, mean age was 55.60 years. Patients with symptomatic peripheral arterial disease (Fontaine stages I-III) were admitted for endovascular treatment by percutaneous transluminal angioplasty. Thermal images and ankle-brachial index values were obtained before and after treatment by percutaneous transluminal angioplasty. Median temperature change in the treated limb was 0.4℃, for non-treated limb was -0.5℃. The median value of ankle-brachial index in the treated limb increased by 0.17 from 0.81 after the procedure. The median value of ankle-brachial index in the non-treated limb decreased by 0.03 from the value of 1.01. Significant difference between treated limb and non-treated limb in change of ankle-brachial index was found with p value = .0035. The surface temperature obtained by the infrared thermography correlates with ankle-brachial index. We present data showing that the increase of ankle-brachial index is associated with increase of skin temperature in the case of limbs treated by percutaneous transluminal angioplasty. Our results also suggest potential of the use of infrared thermography for monitoring foot temperature as a means of early detection of onset of foot ischemic disorders.

Portable System for Monitoring the Microclimate in the Footwear-Foot Interface

A new, continuously-monitoring portable device that monitors the diabetic foot has shown to help in reduction of diabetic foot complications. Persons affected by diabetic foot have shown to be particularly sensitive in the plantar surface; this sensitivity coupled with certain ambient conditions may cause dry skin. This dry skin leads to the formation of fissures that may eventually result in a foot ulceration and subsequent hospitalization. This new device monitors the micro-climate temperature and humidity areas between the insole and sole of the footwear. The monitoring system consists of an array of ten sensors that take readings of relative humidity within the range of 100% ± 2% and temperature within the range of −40 °C to 123.8 ± 0.3 °C. Continuous data is collected using embedded C software and the recorded data is processed in Matlab. This allows for the display of data; the implementation of the iterative Gauss-Newton algorithm method was used to display an exponential response curve. Therefore, the aim of our system is to obtain feedback data and provide the critical information to various footwear manufacturers. The footwear manufactures will utilize this critical information to design and manufacture diabetic footwear that reduce the risk of ulcers in diabetic feet.

A review of thermal methods and technologies for diabetic foot assessment

Temperature analysis has been considered as a complementary method in medical evaluation and diagnosis. Several studies demonstrated that monitoring the temperature variations of the feet of diabetic patients can be helpful in the early identification of diabetic foot manifestations, and also in changing behaviors, which may contribute to reducing its incidence. In this review, several and most used techniques for assessing the temperature of the feet are presented, along with original published work on specific applications in diabetic foot complications. A review of solutions and equipment that operate according to the temperature assessment techniques is also presented. Finally, a comparison between the various technologies is presented, and the authors share their perspective on what will be the state of affairs in 5 years.