Shape Memory Polyurethane-Based Smart Polymer Substrates for Physiologically Responsive, Dynamic Pressure (Re)Distribution.

Shape memory polymers (SMPs) are an exciting class of stimuli-responsive smart materials that demonstrate reactive and reversible changes in mechanical property, usually by switching between different states due to external stimuli. We report on the development of a polyurethane-based SMP foam for effective pressure redistribution that demonstrates controllable changes in dynamic pressure redistribution capability at a low transition temperature (∼24 °C)-ideally suited to matching modulations in body contact pressure for dynamic pressure relief (e.g., for alleviation or pressure ulcer effects). The resultant SMP material has been extensively characterized by a series of tests including stress-strain testing, compression testing, dynamic mechanical analysis, optical microscopy, UV-visible absorbance spectroscopy, variable-temperature areal pressure distribution, Fourier transform infrared spectroscopy, Raman spectroscopy, X-ray diffraction, differential scanning calorimetry, dynamic thermogravimetric analysis, and 1H nuclear magnetic resonance spectroscopy. The foam system exhibits high responsivity when tested for plantar pressure modulation with significant potential in pressure ulcers treatment. Efficient pressure redistribution (∼80% reduction in interface pressure), high stress response (∼30% applied stress is stored in fixity and released on recovery), and excellent deformation recovery (∼100%) are demonstrated in addition to significant cycling ability without performance loss. By providing highly effective pressure redistribution and modulation when in contact with the body's surface, this SMP foam offers novel mechanisms for alleviating the risk of pressure ulcers

Thermal imaging in skin trauma evaluation: observations by CAT S60 mobile phone

The purpose of this study was to evaluate the usability of a mobile phone with inbuilt thermal camera in wound imaging for medical purposes. Thermal imaging could help in evaluating wound healing and in assisting doctors in diagnose making. By using CAT S60 smart phone with an inbuilt Flir thermal camera, thermal pictures from skin wounds and lower limbs were taken from six people in order to find out if thermal imaging could help the treatment and diagnosis of a patient. Thermal images were taken in order to find and visualize temperature changes (being normally invisible) in skin damage areas including deep skin damages especially from limbs and extremities. By using thermal imaging the beginning of treatment could be hastened and the monitoring of the state of a patient would be more efficient thus improving the prognosis of a patient. The thermal pictures taken from skin damages suggest that thermal imaging with CAT S60 smart phone can be used to improve nursing methods and may also help in diagnosis. Non-invasive thermal imaging may be a valuable asset and for its part hasten the beginning of treatment. The resolution and properties of CAT S60 smart phone was sufficient to detect skin damage temperature changes. This may suggest the usage of the CAT S60 smart in hospital, emergency ward and in home care services.The purpose of this study was to evaluate the usability of a mobile phone with inbuilt thermal camera in wound imaging for medical purposes. Thermal imaging could help in evaluating wound healing and in assisting doctors in diagnose making. By using CAT S60 smart phone with an inbuilt Flir thermal camera, thermal pictures from skin wounds and lower limbs were taken from six people in order to find out if thermal imaging could help the treatment and diagnosis of a patient. Thermal images were taken in order to find and visualize temperature changes (being normally invisible) in skin damage areas including deep skin damages especially from limbs and extremities. By using thermal imaging the beginning of treatment could be hastened and the monitoring of the state of a patient would be more efficient thus improving the prognosis of a patient. The thermal pictures taken from skin damages suggest that thermal imaging with CAT S60 smart phone can be used to improve nursing methods and may also help in diagnosis. Non-invasive thermal imaging may be a valuable asset and for its part hasten the beginning of treatment. The resolution and properties of CAT S60 smart phone was sufficient to detect skin damage temperature changes. This may suggest the usage of the CAT S60 smart in hospital, emergency ward and in home care services

Automatic segmentation of plantar thermograms using adaptive C means technique

Diabetic foot ulcer (DFU) is one of the major concern of diabetes and it is rapidly increasing, in worst case scenario this may lead to amputation. The DFU can be avoided by the early detection and proper diagnosis. Many of the studies carried out highlights that, thermography is the most useful technique to measure the changes in the temperature of plantar surface and alerts to indicate the risk associated with DFU. The distribution of temperature does not have a fixed pattern across the patients, hence it makes the difficulty in measuring the appropriate changes. This gap will provide a scope to improve the analysis technique so as to measure the plantar surface temperature effectively and identify any abnormal changes. In this paper, the segmentation algorithm namely adaptive C means (ACM) for the image segmentation is discussed. ACM is based on the spatial information and this method includes the two stage implementation. In the first stage, nonlocal spatial information is added and in the second stage, spatial shape information is used in order to refine the constraint of local spatial. Outcome of the proposed method shows that ACM is very much effective and it outperforms the other existing methods

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.info:eu-repo/semantics/publishedVersio

Plantar thermography is useful in the early diagnosis of diabetic neuropathy

OBJECTIVES: This study evaluated plantar thermography sensitivity and specificity in diagnosing diabetic polyneuropathy using cardiac tests (heart rate variability) as a reference standard because autonomic small fibers are affected first by this disease. METHODS: Seventy-nine individuals between the ages of 19 and 79 years old (28 males) were evaluated and divided into three groups: control (n = 37), pre-diabetics (n = 13) and type 2 diabetics (n = 29). The plantar images were recorded at baseline and then minutes after a provocative maneuver (Cold Stress Test) using an infrared camera that is appropriate for clinical use. Two thermographic variables were studied: the thermal recovery index and the interdigital anisothermal technique. Heart rate variability was measured in a seven-test battery that included three spectral indexes (in the frequency domain) and four Ewing tests (the Valsalva maneuver, the orthostatic test, a deep breathing test, and the orthostatic hypotension test). Other classically recommended tests were applied, including electromyography (EMG), Michigan inventory, and a clinical interview that included a neurological physical examination. RESULTS: Among the diabetic patients, the interdigital anisothermal technique alone performed better than the thermal recovery index alone, with a better sensitivity (81.3%) and specificity (46.2%). For the pre-diabetic patients, the three tests performed equally well. None of the control subjects displayed abnormal interdigital anisothermal readouts or thermal recovery indices, which precluded the sensitivity estimation in this sample of subjects. However, the specificity (70.6%) was higher in this group. CONCLUSION: In this study, plantar thermography, which predominately considers the small and autonomic fibers that are commonly associated with a sub-clinical condition, proved useful in diagnosing diabetic neuropathy early. The interdigital anisothermal test, when used alone, performed best

Liquid crystal thermography in neuropathic assessment of the diabetic foot.

Primary aetiologic factors of diabetic foot disease include peripheral neuropathy and peripheral vascular disease. Assessment of circulation, neuropathy, and foot pressure is employed routinely to determine the risk of foot ulceration in the patient with diabetes mellitus. Routine neuropathic evaluation includes assessment of sensory loss in the plantar skin of the foot using both the Semmes Weinstein monofilament and the biothesiometer. Progressive degeneration of sensory nerve pathways is thought to affect thermoreceptors and mechanoreceptors. However, thermological measurements of the foot to assess responses to thermal stimuli and cutaneous thermal discrimination threshold are relatively uncommon. Recent improvements in liquid crystal technology (LCT) including insensitivity to pressure, faster response times, lower cost and fast image acquisition offer potential for routine thermographic assessment of the diabetic foot. The present study was designed to evaluate if an association exists between abnormal plantar thermal images and sensory loss under conditions of normal loading. The system comprises a robust measurement platform, thermochromic liquid crystal polyester sheet (TLC), instrumentation and analysis software. In vitro calibration was performed to characterise three physical forms of TLC on the basis of linearity, hysteresis, pressure sensitivity and response time. An in vivo pilot evaluation study of the system was performed using three sub-groups (i) neuropathic diabetic (n=30), (ii) non neuropathic diabetic (n=30) and (iii) a healthy control group (n=30). The principal results of this study indicate raised plantar temperatures for the diabetic groups at baseline and post stress relative to the control group. Furthermore, poor recovery response to thermal stimulus in the neuropathic diabetic group suggests degeneration of thermoreceptors. Thus by assessing the thermal parameters at the same sites as that of sensory testing, the new LCT based approach appears capable of providing an alternative confirmation of clinical neuropathy and offers potential as an improved method compared to existing techniques

Development and characterisation of a novel three-dimensional inter-kingdom wound biofilm model

Chronic diabetic foot ulcers are frequently colonised and infected by polymicrobial biofilms that ultimately prevent healing. This study aimed to create a novel in vitro inter-kingdom wound biofilm model on complex hydrogel-based cellulose substrata to test commonly used topical wound treatments. Inter-kingdom triadic biofilms composed of Candida albicans, Pseudomonas aeruginosa, and Staphylococcus aureus were shown to be quantitatively greater in this model compared to a simple substratum when assessed by conventional culture, metabolic dye and live dead qPCR. These biofilms were both structurally complex and compositionally dynamic in response to topical therapy, so when treated with either chlorhexidine or povidone iodine, principal component analysis revealed that the 3-D cellulose model was minimally impacted compared to the simple substratum model. This study highlights the importance of biofilm substratum and inclusion of relevant polymicrobial and inter-kingdom components, as these impact penetration and efficacy of topical antiseptics

Application of infrared thermography in computer aided diagnosis

The invention of thermography, in the 1950s, posed a formidable problem to the research community: What is the relationship between disease and heat radiation captured with Infrared (IR) cameras? The research community responded with a continuous effort to find this crucial relationship. This effort was aided by advances in processing techniques, improved sensitivity and spatial resolution of thermal sensors. However, despite this progress fundamental issues with this imaging modality still remain. The main problem is that the link between disease and heat radiation is complex and in many cases even non-linear. Furthermore, the change in heat radiation as well as the change in radiation pattern, which indicate disease, is minute. On a technical level, this poses high requirements on image capturing and processing. On a more abstract level, these problems lead to inter-observer variability and on an even more abstract level they lead to a lack of trust in this imaging modality. In this review, we adopt the position that these problems can only be solved through a strict application of scientific principles and objective performance assessment. Computing machinery is inherently objective; this helps us to apply scientific principles in a transparent way and to assess the performance results. As a consequence, we aim to promote thermography based Computer-Aided Diagnosis (CAD) systems. Another benefit of CAD systems comes from the fact that the diagnostic accuracy is linked to the capability of the computing machinery and, in general, computers become ever more potent. We predict that a pervasive application of computers and networking technology in medicine will help us to overcome the shortcomings of any single imaging modality and this will pave the way for integrated health care systems which maximize the quality of patient care

Infrared Thermography for the Assessment of Lumbar Sympathetic Blocks in Patients with Complex Regional Pain Syndrome

[ES] El síndrome de dolor regional complejo (SDRC) es un trastorno de dolor crónico debilitante que suele afectar a una extremidad, y se caracteriza por su compleja e incomprendida fisiopatología subyacente, lo que supone un reto para su diagnóstico y tratamiento. Para evitar el deterioro de la calidad de vida de los pacientes, la consecución de un diagnóstico y tratamiento tempranos marca un punto de inflexión. Entre los diferentes tratamientos, los bloqueos simpáticos lumbares (BSLs) tienen como objetivo aliviar el dolor y reducir algunos signos simpáticos de la afección. Este procedimiento intervencionista se lleva a cabo inyectando anestesia local alrededor de los ganglios simpáticos y, hasta ahora, se realiza frecuentemente bajo el control de diferentes técnicas de imagen, como los ultrasonidos o la fluoroscopia. Dado que la termografía infrarroja (TIR) ha demostrado ser una herramienta eficaz para evaluar la temperatura de la piel, y teniendo en cuenta el efecto vasodilatador que presentan los anestésicos locales inyectados, se ha considerado el uso de la IRT para la evaluación de los BSLs. El objetivo de esta tesis es, estudiar la capacidad de la TIR como una técnica complementaria para la evaluación de la eficacia en la ejecución de los BSLs. Para cumplir este objetivo, se han realizado tres estudios implementando la TIR en pacientes diagnosticados de SDRC de miembros inferiores sometidos a BSLs. El primer estudio se centra en la viabilidad de la TIR como herramienta complementaria para la evaluación de la eficacia ejecución de los BSLs. Cuando se realizan los BSLs, la colocación correcta de la aguja es crítica para llevar realizar el procedimiento técnicamente correcto y, en consecuencia, para lograr los resultados clínicos deseados. Para verificar la posición de la aguja, tradicionalmente se han utilizado técnicas de imagen, sin embargo, los BSLs bajo control fluoroscópico no siempre aseguran su exacta ejecución. Por este motivo, se han aprovechado las alteraciones térmicas inducidas por los anestésicos locales y se han evaluado mediante la TIR. Así, cuando en las imágenes infrarrojas se observaron cambios térmicos en la planta del pie afectado tras la inyección de lidocaína, se consideró que el BSL era exitoso. El segundo estudio trata del análisis cuantitativo de los datos térmicos recogidos en el entorno clínico a partir de diferentes parámetros basados en las temperaturas extraídas de ambos pies. Según los resultados, para predecir adecuadamente los BSLs exitosos, se deberían analizar las temperaturas de las plantas de los pies durante los primeros cuatro minutos tras la inyección del anestésico local. Así, la aplicación de la TIR en el entorno clínico podría ser de gran ayuda para evaluar la eficacia de ejecución de los BSLs mediante la evaluación de las temperaturas de los pies en tiempo real. Por último, el tercer estudio aborda el análisis cuantitativo mediante la implementación de herramientas de machine learning (ML) para evaluar su capacidad de clasificar automáticamente los BSLs. En este estudio se han utilizado una serie de características térmicas extraídas de las imágenes infrarrojas para evaluar cuatro algoritmos de ML para tres momentos diferentes después del instante de referencia (inyección de lidocaína). Los resultados indican que los cuatro modelos evaluados presentan buenos rendimientos para clasificar automáticamente los BSLs entre exitosos y fallidos. Por lo tanto, la combinación de parámetros térmicos junto con de clasificación ML muestra ser eficaz para la clasificación automática de los procedimientos de BSLs. En conclusión, el uso de la TIR como técnica complementaria en la práctica clínica diaria para la evaluación de los BSLs ha demostrado ser totalmente eficaz. Dado que es un método objetivo y relativamente sencillo de implementar, puede permitir que los médicos especialistas en dolor identifiquen los bloqueos realizados fallidos y, en consecuencia, puedan revertir esta situación.[CA] La síndrome de dolor regional complex (SDRC) és un trastorn de dolor crònic debilitant que sol afectar una extremitat, i es caracteritza per la seua complexa i incompresa fisiopatologia subjacent, la qual cosa suposa un repte per al seu diagnòstic i tractament. Per a evitar la deterioració de la qualitat de vida dels pacients, la consecució d'un diagnòstic i tractament primerencs marca un punt d'inflexió. Entre els diferents tractaments , els bloquejos simpàtics lumbars (BSLs) tenen com a objectiu alleujar el dolor i reduir alguns signes simpàtics de l'afecció. Aquest procediment intervencionista es duu a terme injectant anestèsia local al voltant dels ganglis simpàtics i, fins ara, es realitza freqüentment sota el control de diferents tècniques d'imatge, com els ultrasons o la fluoroscopia. Atés que la termografia infraroja (TIR) ha demostrat ser una eina eficaç per a avaluar la temperatura de la pell, i tenint en compte l'efecte vasodilatador que presenten els anestèsics locals injectats, s'ha considerat l'ús de la TIR per a l'avaluació dels BSLs. L'objectiu d'aquesta tesi és, estudiar la capacitat de la TIR com una tècnica complementària per a l'avaluació de l'eficàcia en l'execució dels BSLs. Per a complir aquest objectiu, s'han realitzat tres estudis implementant la TIR en pacients diagnosticats de SDRC de membres inferiors sotmesos a BSLs. El primer estudi avalua la viabilitat de la TIR com a eina complementària per a l'analisi de l'eficàcia en l'execució dels BSLs. Quan es realitzen els BSLs, la col·locació correcta de l'agulla és crítica per a dur a terme el procediment tècnicament correcte i, en conseqüència, per a aconseguir els resultats clínics desitjats. Per a verificar la posició de l'agulla, tradicionalment s'han utilitzat tècniques d'imatge, no obstant això, els BSLs baix control fluoroscòpic no sempre asseguren la seua exacta execució. Per aquest motiu, s'han aprofitat les alteracions tèrmiques induïdes pels anestèsics locals i s'han avaluat mitjançant la TIR. Així, quan en les imatges infraroges es van observar canvis tèrmics en la planta del peu afectat després de la injecció de lidocaIna, es va considerar que el BSL era exitós. El segon estudi tracta de l'anàlisi quantitativa de les dades tèrmiques recollides en l'entorn clínic a partir de diferents paràmetres basats en les temperatures extretes d'ambdós peus. Segons els resultats, per a predir adequadament l'execució exitosa d'un BSL, s'haurien d'analitzar les temperatures de les plantes dels peus durant els primers quatre minuts després de la injecció de l'anestèsic local. Així, l'implementació de la TIR en l'entorn clínic podria ser de gran ajuda per a avaluar l'eficàcia d'execució dels BSLs mitjançant l'avaluació de les temperatures dels peus en temps real. El tercer estudi aborda l'anàlisi quantitativa mitjançant la implementació d'eines machine learning (ML) per a avaluar la seua capacitat de classificar automàticament els BSLs. En aquest estudi s'han utilitzat una sèrie de característiques tèrmiques extretes de les imatges infraroges per a avaluar quatre algorismes de ML per a tres moments diferents després de l'instant de referència (injecció de lidocaïna). Els resultats indiquen que els quatre models avaluats presenten bons rendiments per a classificar automàticament els BSLs en exitosos i fallits. Per tant, la combinació de paràmetres tèrmics juntament amb models de classificació ML mostra ser eficaç per a la classificació automàtica dels procediments de BSLs. En conclusió, l'ús de la TIR com a tècnica complementària en la pràctica clínica diària per a l'avaluació dels BSLs ha demostrat ser totalment eficaç. Atés que és un mètode objectiu i relativament senzill d'implementar, pot ajudar els metges especialistes en dolor a identificar els bloquejos realitzats fallits i, en conseqüència, puguen revertir aquesta situació.[EN] Complex regional pain syndrome (CRPS) is a debilitating chronic pain condition that usually affects one limb, and it is characterized by its misunderstood underlying pathophysiology, resulting in both challenging diagnosis and treatment. To avoid the patients' impairment quality of life, the achievement of both an early diagnosis and treatment marks a turning point. Among the different treatment approaches, lumbar sympathetic blocks (LSBs) are addressed to alleviate the pain and reduce some sympathetic signs of the condition. This interventional procedure is performed by injecting local anaesthetic around the sympathetic ganglia and, until now, it has been performed under different imaging techniques, including the ultrasound or the fluoroscopy approaches. Since infrared thermography (IRT) has proven to be a powerful tool to evaluate skin temperatures and taking into account the vasodilatory effects of the local anaesthetics injected in the LSB, the use of IRT has been considered for the LSBs assessment. Therefore, the purpose of this thesis is to evaluate the capability of IRT as a complementary assessment technique for the LSBs procedures performance. To fulfil this aim, three studies have been conducted implementing the IRT in patients diagnosed with lower limbs CRPS undergoing LSBs. The first study focuses on the feasibility of IRT as a complementary assessment tool for LSBs performance, that is, for the confirmation of the proper needle position. When LSBs are performed, the correct needle placement is critical to carry out the procedure technically correct and, consequently, to achieve the desired clinical outcomes. To verify the needle placement position, imaging techniques have traditionally been used, however, LSBs under radioscopic guidance do not always ensure an exact performance. For this reason, the thermal alterations induced by the local anaesthetics, have been exploited and assessed by means of IRT. Thus, the LSB procedure was considered successfully performed when thermal changes within the affected plantar foot were observed in the infrared images after the lidocaine injection. The second study deals with the quantitative analysis of the thermal data collected in the clinical setting through the evaluation of different temperature-based parameters extracted from both feet. According to the results, the proper LSB success prediction could be achieved in the first four minutes after the block through the evaluation of the feet skin temperatures. Therefore, the implementation of IRT in the clinical setting might be of great help in assessing the LSBs performance by evaluating the plantar feet temperatures in real time. Finally, the third study addresses the quantitative analysis by implementing machine learning (ML) tools to assess their capability to automatically classify LSBs. In this study, a set of thermal features retrieved from the infrared images have been used to evaluate four ML algorithms for three different moments after the baseline time (lidocaine injection). The results indicate that all four models evaluated present good performance metrics to automatically classify LSBs into successful and failed. Therefore, combining infrared features with ML classification models shows to be effective for the LSBs procedures automatic classification. In conclusion, the use of IRT as a complementary technique in daily clinical practice for LSBs assessment has been evidenced entirely effective. Since IRT is an objective method and it is not very demanding to perform, it is of great help for pain physicians to identify failed procedures, and consequently, it allow them to reverse this situation.Cañada Soriano, M. (2022). Infrared Thermography for the Assessment of Lumbar Sympathetic Blocks in Patients with Complex Regional Pain Syndrome [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/181699TESI

Application of machine learning algorithms in thermal images for an automatic classification of lumbar sympathetic blocks

Purpose There are no previous studies developing machine learning algorithms in the classification of lumbar sympathetic blocks (LSBs) performance using infrared thermography data. The objective was to assess the performance of different machine learning algorithms to classify LSBs carried out in patients diagnosed with lower limbs Complex Regional Pain Syndrome as successful or failed based on the evaluation of thermal predictors. Methods 66 LSBs previously performed and classified by the medical team were evaluated in 24 patients. 11 regions of interest on each plantar foot were selected within the thermal images acquired in the clinical setting. From every region of interest, different thermal predictors were extracted and analysed in three different moments (minutes 4, 5, and 6) along with the baseline time (just after the injection of a local anaesthetic around the sympathetic ganglia). Among them, the thermal variation of the ipsilateral foot and the thermal asymmetry variation between feet at each minute assessed and the starting time for each region of interest, were fed into 4 different machine learning classifiers: an Artificial Neuronal Network, K-Nearest Neighbours, Random Forest, and a Support Vector Machine. Results All classifiers presented an accuracy and specificity higher than 70%, sensitivity higher than 67%, and AUC higher than 0.73, and the Artificial Neuronal Network classifier performed the best with a maximum accuracy of 88%, sensitivity of 100%, specificity of 84% and AUC of 0.92, using 3 predictors. Conclusion These results suggest thermal data retrieved from plantar feet combined with a machine learning-based methodology can be an effective tool to automatically classify LSBs performance