6,563 research outputs found

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

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    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

    Earth observations from space: Outlook for the geological sciences

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    Remote sensing from space platforms is discussed as another tool available to geologists. The results of Nimbus observations, the ERTS program, and Skylab EREP are reviewed, and a multidisciplinary approach is recommended for meeting the challenges of remote sensing

    Remote sensing applications in forestry - Analysis of remote sensing data for range resource management Annual progress report

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    Interpretation of remote sensing data for evaluating range resource

    Thermal imaging and planimetry evaluation of the results of chronic wounds treatment with hyperbaric oxygen therapy

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    Background. One of the methods to treat chronic wounds is the use of hyperbaric oxygen (HBO). Objective measurement of the wound surface is an important element in the process of monitoring and predicting the progress of treatment. Objectives. The aim of the study was to evaluate the effect of hyperbaric oxygen therapy (HBOT) on ulcer wound healing in patients with chronic venous insufficiency ulcers and diabetic foot syndrome using thermal imaging and computerized planimetry. Material and methods. During a 3-year period, 284 digital computer planimetry measurements were gathered from 142 patients treated for leg ulcers caused by chronic venous insufficiency and ulcers from diabetic foot syndrome at HBOT Unit of the Dr Stanisław Sakiel Centre for Burns Treatment in Siemianowice Śląskie (Poland). Each patient took 30 HBOT sessions using a Haux multiplace HBO chamber at a pressure of 2.5 atmospheres absolute (ATA). The results of the treatment were monitored using thermovision and computerassisted planimetry measurements performed before and after HBOT. Results. Both groups of patients exhibited a reduction in the surface and perimeter of the wound after HBOT. The treatment effects were also confirmed with thermal imaging. The areas calculated from thermal imaging and planimetry are different but correlated. Conclusions. It seems that a combination of thermal imaging and planimetry may enhance the diagnosis as well as provide the physician with more information about therapy effects

    Reporting of thermography parameters in biology: a systematic review of thermal imaging literature

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    This is the final version. Available from the Royal Society via the DOI in this record. Data accessibility: All data are available in the electronic supplementary material.Infrared (IR) thermography, where temperature measurements are made with IR cameras, has proven to be a very useful and widely used tool in biological science. Several thermography parameters are critical to the proper operation of thermal cameras and the accuracy of measurements, and these must usually be provided to the camera. Failure to account for these parameters may lead to less accurate measurements. Furthermore, the failure to provide information of parameter choices in reports may compromise appraisal of accuracy and replicate studies. In this review, we investigate how well biologists report thermography parameters. This is done through a systematic review of biological thermography literature that included articles published between years 2007 and 2017. We found that in primary biological thermography papers, which make some kind of quantitative temperature measurement, 48% fail to report values used for emissivity (an object's capacity to emit thermal radiation relative to a black body radiator), which is the minimum level of reporting that should take place. This finding highlights the need for life scientists to take into account and report key parameter information when carrying out thermography, in the future.Natural Environment Research Counci

    Tool to visualize and evaluate operator proficiency in laser hair-removal treatments

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    BACKGROUND: The uniform delivery of laser energy is particularly important for safe and effective laser hair removal (LHR) treatment. Although it is necessary to quantitatively assess the spatial distribution of the delivered laser, laser spots are difficult to trace owing to a lack of visual cues. This study proposes a novel preclinic tool to evaluate operator proficiency in LHR treatment and applies this tool to train novice operators and compare two different treatment techniques (sliding versus spot-by-spot). METHODS: A simulation bed is constructed to visualize the irradiated laser spots. Six novice operators are recruited to perform four sessions of simulation while changing the treatment techniques and the presence of feedback (sliding without feedback, sliding with feedback, spot-by-spot without feedback, and spot-by-spot with feedback). Laser distribution maps (LDMs) are reconstructed through a series of images processed from the recorded video for each simulation session. Then, an experienced dermatologist classifies the collected LDMs into three different performance groups, which are quantitatively analyzed in terms of four performance indices. RESULTS: The performance groups are characterized by using a combination of four proposed indices. The best-performing group exhibited the lowest amount of randomness in laser delivery and accurate estimation of mean spot distances. The training was only effective in the sliding treatment technique. After the training, omission errors decreased by 6.32% and better estimation of the mean spot distance of the actual size of the laser-emitting window was achieved. Gels required operators to be trained when the spot-by-spot technique was used, and imposed difficulties in maintaining regular laser delivery when the sliding technique was used. CONCLUSIONS: Because the proposed system is simple and highly affordable, it is expected to benefit many operators in clinics to train and maintain skilled performance in LHR treatment, which will eventually lead to accomplishing a uniform laser delivery for safe and effective LHR treatment

    Reconnaissance de l'émotion thermique

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    Pour améliorer les interactions homme-ordinateur dans les domaines de la santé, de l'e-learning et des jeux vidéos, de nombreux chercheurs ont étudié la reconnaissance des émotions à partir des signaux de texte, de parole, d'expression faciale, de détection d'émotion ou d'électroencéphalographie (EEG). Parmi eux, la reconnaissance d'émotion à l'aide d'EEG a permis une précision satisfaisante. Cependant, le fait d'utiliser des dispositifs d'électroencéphalographie limite la gamme des mouvements de l'utilisateur. Une méthode non envahissante est donc nécessaire pour faciliter la détection des émotions et ses applications. C'est pourquoi nous avons proposé d'utiliser une caméra thermique pour capturer les changements de température de la peau, puis appliquer des algorithmes d'apprentissage machine pour classer les changements d'émotion en conséquence. Cette thèse contient deux études sur la détection d'émotion thermique avec la comparaison de la détection d'émotion basée sur EEG. L'un était de découvrir les profils de détection émotionnelle thermique en comparaison avec la technologie de détection d'émotion basée sur EEG; L'autre était de construire une application avec des algorithmes d'apprentissage en machine profonds pour visualiser la précision et la performance de la détection d'émotion thermique et basée sur EEG. Dans la première recherche, nous avons appliqué HMM dans la reconnaissance de l'émotion thermique, et après avoir comparé à la détection de l'émotion basée sur EEG, nous avons identifié les caractéristiques liées à l'émotion de la température de la peau en termes d'intensité et de rapidité. Dans la deuxième recherche, nous avons mis en place une application de détection d'émotion qui supporte à la fois la détection d'émotion thermique et la détection d'émotion basée sur EEG en appliquant les méthodes d'apprentissage par machine profondes - Réseau Neuronal Convolutif (CNN) et Mémoire à long court-terme (LSTM). La précision de la détection d'émotion basée sur l'image thermique a atteint 52,59% et la précision de la détection basée sur l'EEG a atteint 67,05%. Dans une autre étude, nous allons faire plus de recherches sur l'ajustement des algorithmes d'apprentissage machine pour améliorer la précision de détection d'émotion thermique.To improve computer-human interactions in the areas of healthcare, e-learning and video games, many researchers have studied on recognizing emotions from text, speech, facial expressions, emotion detection, or electroencephalography (EEG) signals. Among them, emotion recognition using EEG has achieved satisfying accuracy. However, wearing electroencephalography devices limits the range of user movement, thus a noninvasive method is required to facilitate the emotion detection and its applications. That’s why we proposed using thermal camera to capture the skin temperature changes and then applying machine learning algorithms to classify emotion changes accordingly. This thesis contains two studies on thermal emotion detection with the comparison of EEG-base emotion detection. One was to find out the thermal emotional detection profiles comparing with EEG-based emotion detection technology; the other was to implement an application with deep machine learning algorithms to visually display both thermal and EEG based emotion detection accuracy and performance. In the first research, we applied HMM in thermal emotion recognition, and after comparing with EEG-base emotion detection, we identified skin temperature emotion-related features in terms of intensity and rapidity. In the second research, we implemented an emotion detection application supporting both thermal emotion detection and EEG-based emotion detection with applying the deep machine learning methods – Convolutional Neutral Network (CNN) and LSTM (Long- Short Term Memory). The accuracy of thermal image based emotion detection achieved 52.59% and the accuracy of EEG based detection achieved 67.05%. In further study, we will do more research on adjusting machine learning algorithms to improve the thermal emotion detection precision
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