288 research outputs found

    Diseases of Iberian ibex (Capra pyrenaica)

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    Iberian ibex (Capra pyrenaica) is an ecologically and economically relevant medium-sized emblematic mountain ungulate. Diseases participate in the population dynamics of the species as a regulating agent, but can also threaten the conservation and viability of vulnerable population units. Moreover, Iberian ibex can also be a carrier or even a reservoir of pathogens shared with domestic animals and/or humans, being therefore a concern for livestock and public health. The objective of this review is to compile the currently available knowledge on (1) diseases of Iberian ibex, presented according to their relevance on the health and demography of free-ranging populations; (2) diseases subjected to heath surveillance plans; (3) other diseases reported in the species; and (4) diseases with particular relevance in captive Iberian ibex populations. The systematic review of all the information on diseases affecting the species unveils unpublished reports, scientific communications in meetings, and scientific articles, allowing the first comprehensive compilation of Iberian ibex diseases. This review identifies the gaps in knowledge regarding pathogenesis, immune response, diagnostic methods, treatment, and management of diseases in Iberian ibex, providing a base for future research. Moreover, this challenges wildlife and livestock disease and wildlife population managers to assess the priorities and policies currently implemented in Iberian ibex health surveillance and monitoring and disease management

    Evaluation of different segmentation-based approaches for skin disorders from dermoscopic images

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    Treballs Finals de Grau d'Enginyeria BiomĂšdica. Facultat de Medicina i CiĂšncies de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Mata Miquel, Christian, Munuera, JosepSkin disorders are the most common type of cancer in the world and the incident has been lately increasing over the past decades. Even with the most complex and advanced technologies, current image acquisition systems do not permit a reliable identification of the skin lesion by visual examination due to the challenging structure of the malignancy. This promotes the need for the implementation of automatic skin lesion segmentation methods in order to assist in physicians’ diagnostic when determining the lesion's region and to serve as a preliminary step for the classification of the skin lesion. Accurate and precise segmentation is crucial for a rigorous screening and monitoring of the disease's progression. For the purpose of the commented concern, the present project aims to accomplish a state-of-the-art review about the most predominant conventional segmentation models for skin lesion segmentation, alongside with a market analysis examination. With the rise of automatic segmentation tools, a wide number of algorithms are currently being used, but many are the drawbacks when employing them for dermatological disorders due to the high-level presence of artefacts in the image acquired. In light of the above, three segmentation techniques have been selected for the completion of the work: level set method, an algorithm combining GrabCut and k-means methods and an intensity automatic algorithm developed by Hospital Sant Joan de DĂ©u de Barcelona research group. In addition, a validation of their performance is conducted for a further implementation of them in clinical training. The proposals, together with the got outcomes, have been accomplished by means of a publicly available skin lesion image database

    Mobile teledermoscopy for patients at high risk of cutaneous melanoma: A single-arm, feasibility study of a clinical intervention at two tertiary centres (MOBILEMEL study)

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    Mobile teledermoscopy provides faster patient-to-specialist access. We studied the feasibility of its implementation in high-risk melanoma patients. In a prospective dual-cohort study, 75 high-risk patients from two Australian tertiary centres were given phone-compatible teledermoscopes. Cohort 1 provided transmissions for lesions initiated by the (i) patient’s concern and/or (ii) dermatologist’s recommendation for sequential digital dermoscopy imaging (SDDI) over 12 months of participation. Paired data collected before (conventional face-to-face practice, FTF) and after (1 year) the teledermoscopy implementation was compared. The primary outcome was the earlier detection of skin cancers. Secondary outcomes included transmission quality of mobile teledermoscopy and associations with clinical practice, cost, and level of acceptance. Cohort 2 performed transmissions for SDDI to enhance the above data. Mobile teledermoscopy used by 75 participants (Cohort 1, n=45; Cohort 2, n=30) reduced the time-to-treatment by 50 days (p=0.039). There were 302 intended transmissions: lesion of concern comprised 22%(67/302), and SDDI comprised 78%(235/302). One-fifth of the latter (43/235;18%) were not transmitted – half either converted to visits by participants or not attempted despite reminders. Of the actual transmissions – 86%(259/302) of intended transmissions – evaluable transmissions comprised 78%(201/259). Participants >40 years old (p=0.014) or with tertiary education (p=0.015) provided more reliable transmissions. Mobile teledermoscopy had 89% diagnostic accuracy and treatment concordance with FTF visits. Visits were averted (39%) or fast-tracked (37%). Most(16/19) melanomas were identified at FTF visits. Mobile teledermoscopy had a $28/patient/year increment to the healthcare system but participants found it highly acceptable (mean confidence level 4.2/5). Earlier skin cancer treatment can occur with mobile teledermoscopy when used to support conventional practice

    Revista de Științe ale Sănătății din Moldova = Moldovan Journal of Health Sciences. 2023, Vol. 10(2)

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    Revista de Științe ale Sănătății din Moldova (Moldovan Journal of Health Sciences) a fost lansată Ăźn octombrie 2014. Aceasta este editată Ăźn limbile romĂąnă și engleză, conform standardelor și ghidurilor internaționale actuale Ăźn domeniul științelor medicale, și are o apariție trimestrială. Revista este Ăźnregistrată Ăźn Instrumentul Bibliometric Național IBN/IDSI (nr.1 din 16.11.2015), iar din 21 decembrie 2017, prin HotărĂąrea Consiliului Suprem pentru Știință și Dezvoltare Tehnologică nr. 169, a fost inclusă Ăźn lista revistelor științifice de Tip B. Revista este Ăźnregistrată Ăźn 2 baze de date internaționale

    Evaluating EEG–EMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation

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    Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices. One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEG–EMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEG–EMG fusion and to develop a novel control system based on the incorporation of EEG–EMG fusion classifiers. A dataset of EEG and EMG signals were collected during dynamic elbow flexion–extension motions and used to develop EEG–EMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEG–EMG fusion can classify more indirect tasks. A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEG–EMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEG–EMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation

    Evaluation of automated organ segmentation for total-body PET-CT

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    The ability to diagnose rapidly and accurately and treat patients is substantially facilitated by medical images. Radiologists' visual assessment of medical images is crucial to their study. Segmenting images for diagnostic purposes is a crucial step in the medical imaging process. The purpose of medical image segmentation is to locate and isolate ‘Regions of Interest’ (ROI) within a medical image. Several medical uses rely on this procedure, including diagnosis, patient management, and medical study. Medical image segmentation has applications beyond just diagnosis and treatment planning. Quantitative information from medical images can be extracted by image segmentation and employed in the research of new diagnostic and treatment procedures. In addition, image segmentation is a critical procedure in several programs for image processing, including image fusion and registration. In order to construct a single, high-resolution, high-contrast image of an item or organ from several images, a process called "image registration" is used. A more complete picture of the patient's anatomy can be obtained through image fusion, which entails integrating numerous images from different modalities such as computed tomography (CT) and Magnetic resonance imaging (MRI). Once images are obtained using imaging technologies, they go through post-processing procedures before being analyzed. One of the primary and essential steps in post-processing is image segmentation, which involves dividing the images into parts and utilizing only the relevant sections for analysis. This project explores various imaging technologies and tools that can be utilized for image segmentation. Many open-source imaging tools are available for segmenting medical images across various applications. The objective of this study is to use the Jaccard index to evaluate the degree of similarity between the segmentations produced by various medical image visualization and analysis programs

    A survey, review, and future trends of skin lesion segmentation and classification

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    The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis

    Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study

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    Background Dermoscopy is commonly used for the evaluation of pigmented lesions, but agreement between experts for identification of dermoscopic structures is known to be relatively poor. Expert labeling of medical data is a bottleneck in the development of machine learning (ML) tools, and crowdsourcing has been demonstrated as a cost- and time-efficient method for the annotation of medical images. Objective The aim of this study is to demonstrate that crowdsourcing can be used to label basic dermoscopic structures from images of pigmented lesions with similar reliability to a group of experts. Methods First, we obtained labels of 248 images of melanocytic lesions with 31 dermoscopic “subfeatures” labeled by 20 dermoscopy experts. These were then collapsed into 6 dermoscopic “superfeatures” based on structural similarity, due to low interrater reliability (IRR): dots, globules, lines, network structures, regression structures, and vessels. These images were then used as the gold standard for the crowd study. The commercial platform DiagnosUs was used to obtain annotations from a nonexpert crowd for the presence or absence of the 6 superfeatures in each of the 248 images. We replicated this methodology with a group of 7 dermatologists to allow direct comparison with the nonexpert crowd. The Cohen Îș value was used to measure agreement across raters. Results In total, we obtained 139,731 ratings of the 6 dermoscopic superfeatures from the crowd. There was relatively lower agreement for the identification of dots and globules (the median Îș values were 0.526 and 0.395, respectively), whereas network structures and vessels showed the highest agreement (the median Îș values were 0.581 and 0.798, respectively). This pattern was also seen among the expert raters, who had median Îș values of 0.483 and 0.517 for dots and globules, respectively, and 0.758 and 0.790 for network structures and vessels. The median Îș values between nonexperts and thresholded average–expert readers were 0.709 for dots, 0.719 for globules, 0.714 for lines, 0.838 for network structures, 0.818 for regression structures, and 0.728 for vessels. Conclusions This study confirmed that IRR for different dermoscopic features varied among a group of experts; a similar pattern was observed in a nonexpert crowd. There was good or excellent agreement for each of the 6 superfeatures between the crowd and the experts, highlighting the similar reliability of the crowd for labeling dermoscopic images. This confirms the feasibility and dependability of using crowdsourcing as a scalable solution to annotate large sets of dermoscopic images, with several potential clinical and educational applications, including the development of novel, explainable ML tools

    Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks

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    Robustness has become an important consideration in deep learning. With the help of explainable AI, mismatches between an explained model's decision strategy and the user's domain knowledge (e.g. Clever Hans effects) have been identified as a starting point for improving faulty models. However, it is less clear what to do when the user and the explanation agree. In this paper, we demonstrate that acceptance of explanations by the user is not a guarantee for a machine learning model to be robust against Clever Hans effects, which may remain undetected. Such hidden flaws of the model can nevertheless be mitigated, and we demonstrate this by contributing a new method, Explanation-Guided Exposure Minimization (EGEM), that preemptively prunes variations in the ML model that have not been the subject of positive explanation feedback. Experiments demonstrate that our approach leads to models that strongly reduce their reliance on hidden Clever Hans strategies, and consequently achieve higher accuracy on new data.Comment: 18 pages + supplemen
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