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Anterior cruciate ligament (ACL) tears are prevalent career-ending sports injuries. A barrier to successful return to activity is fear of re-injury. Evaluating psychological readiness is however limited to insufficient self-reported assessments. We developed machine learning models using biomechanical data from standardized rebound side hops (SRSH) to objectively classify fear levels post-ACL reconstruction (ACLR) and identify key biomechanical variables. Sixty individuals with ACLR and 47 controls performed up to 10 side hops per leg. Kinematic and kinetic data were collected using motion capture and force platforms. ACLR participants were classified (Tampa Scale for Kinesiophobia-17) as HIGH-FEAR (n = 32) or LOW-FEAR (n = 28). Analyses involved 1D convolutional neural networks (1D CNN) and logistic regression. Integrated gradients identified influential movement variables. The 1-D CNN distinguished HIGH-FEAR versus LOW-FEAR ACLR individuals in agreement with Tampa Scale scores, achieving a mean accuracy of 75.6% (F₁ Score = 0.76, Matthews Correlation Coefficient = 0.52), which was 8.6% better than logistic regression. Influential variables included trunk tilt, hip flexion/extension, and ankle supination/pronation. Machine learning from biomechanics can identify movement linked to fear of re-injury post-ACLR, potentially informing personalised rehabilitation to mitigate fear and enhance recovery.

Climate change impacts pose challenges to a dependable operation of railway infrastructure assets, thus necessitating understanding and mitigating its effects. This study proposes a machine learning framework to distinguish between climatic and non-climatic failures in railway infrastructure. The maintenance data of turnout assets from Sweden's railway were collected and integrated with asset design, geographical and meteorological parameters. Various machine learning algorithms were employed to classify failures across multiple time horizons. The Random Forest model demonstrated a high accuracy of 0.827 and stable F1-scores across all time horizons. The study identified minimum-temperature and quantity of snow and rain prior to the event as the most influential factors. The 24-hour time horizon prior to failure emerged as the most effective time window for the classification. The practical implications and applications include enhancement of maintenance and renewal process, supporting more effective resource allocation, and implementing climate adaptation measures towards resilience railway infrastructure management.

Since 2008, a variety of systems have been designed to detect events in security cameras. There are also more than a hundred journal articles and conference papers published in this field. However, no survey has focused on recognizing events in the surveillance system. Thus, motivated us to provide a comprehensive review of the different developed event detection systems. We start our discussion with the pioneering methods that used the TRECVid-SED dataset and then developed methods using VIRAT dataset in TRECVid evaluation. To better understand the designed systems, we describe the components of each method and the modifications of the existing method separately. We have outlined the significant challenges related to untrimmed security video action detection. Suitable metrics are also presented for assessing the performance of the proposed models. Our study indicated that the majority of researchers classified events into two groups on the basis of the number of participants and the duration of the event for the TRECVid-SED Dataset. Depending on the group of events, one or more models to identify all the events were used. For the VIRAT dataset, object detection models to localize the first stage activities were used throughout the work. Except one study, a 3D convolutional neural network (3D-CNN) to extract Spatio-temporal features or classifying different activities were used. From the review that has been carried, it is possible to conclude that developing an automatic surveillance event detection system requires three factors: accurate and fast object detection in the first stage to localize the activities, and classification model to draw some conclusion from the input values.

Objectives: The aim of this study was to determine capillaroscopic changes in patients with systemic lupus erythematosus (SLE) and their predictors.

Methods: Fifty-nine SLE patients and 31 controls were enrolled in a cross-sectional study. Nailfold capillaroscopy examinations were performed, and qualitative semi-quantitative and quantitative parameters were evaluated in all fingers. Demographic features and lupus characteristics, such as renal involvement, medications, laboratory data, disease activity (SLEDAI) and damage, were recorded. The predictors of capillaroscopic abnormalities were obtained by backward stepwise regression analysis.

Results: Capillary numbers of right hands were significantly lower in patients than in controls [8.74 (1.66) vs. 9.63 (1.80), P = 0.0001]. Capillaries were wider in patients than in controls in right [56.32 μm (16.76) vs. 50.43 μm (10.16), P = 0.002] and left hands [54.40 (15.02) vs. 49.71 (9.77), P = 0.005]. Capillaries were shorter in SLE patients than in controls. Multivariate analysis revealed that the main associative factors of microvascular abnormalities were gender, drinking tea and hydroxychloroquine use for giant capillaries, SLEDAI and low C3 for avascularity and age, lupus nephritis and corticosteroid use for ramification.

Conclusion: Most nailfold capillaroscopic abnormalities were more common in SLE patients than in controls. Hydroxychloroquine, corticosteroids, SLEDAI, low complement and lupus nephritis may be the major prognostic factors for microvascular changes in SLE patients.

Nailfold capillaroscopy (NC) is a highly sensitive, safe, and non-invasive technique to assess involvement rate of microvascularity in dermatomyositis and systemic sclerosis. A large number of studies have focused on NC pattern description, classification, and scoring system validation, but minimal information has been published on the accuracy and precision of the measurement. The objective of this review article is to identify different factors affecting the reliability and validity of the assessment in NC. Several factors can affect the reliability of the examination, e.g., physiological artifacts, the nailfold imaging instrument, human factors, and the assessment rules and standards. It is impossible to avoid all artifacts, e.g., skin transparency, physically injured fingers, and skin pigmentation. However, minimization of the impact of some of these artifacts by considering some protocols before the examination and by using specialized tools, training, guidelines, and software can help to reduce errors in the measurement and assessment of NC images. Establishing guidelines and instructions for automatic characterization and measurement based on machine learning techniques also may reduce ambiguities and the assessment time.

Nailfold capillaroscopy (NC) is a diagnostic imaging technique that is used to assess the blood capillary network in the nailfold area. NC is routinely used for patients with microcirculation problems, such as systemic sclerosis and other connective tissue diseases. Experts commonly use subjective evaluation as a reference point in images of nailfold video capillaroscopy, so it is important to reduce the inherent ambiguities in human judgment and diagnosis. Image quality is an important factor that affects measurement error and assessment time of NC images.

Nailfold capillaroscopy (NC) is an invasive imaging technique that is used to assess the blood capillary network in the nailfold area. NC is routinely used for patients with microcirculation problems, such as systemic sclerosis and other connective tissue diseases. NC is repeatable and appears to be simple and harmless. However, there is a lack of established guidelines [QCE1] and instructions for both the acquisition and interpretation of the images obtained, which may lead to various ambiguities.

The general aim of this thesis was to reduce the inherent ambiguities in human judgment and diagnosis by introducing novel techniques for assessing the nailfold capillary density, which is an important parameter in NC. To achieve this goal, a toolkit was developed that includes a method to measure the capillary density, an image enhancement technique and a graphical user interface (GUI).

In the first study, a summary of the nailfold videocapillaroscopy procedure was presented, and the common techniques used to evaluate capillaroscopic parameters were reviewed. In the second study, common methods for calculating the capillary density were reviewed, and a survey of the relationship between the number of capillaries as well as the existence of digital ulcers, pulmonary arterial hypertension, autoantibodies, scleroderma patterns and the different scoring systems was performed. In the third study, a novel method was proposed to determine the nailfold capillary density, and this method is referred to as the elliptic broken line (EBL) method. In the fourth study, the EBL method for measuring capillary density was evaluated. In the fifth study, a new image enhancement technique was introduced and evaluated both subjectively and objectively. Finally, clinical applications of the EBL method and enhancement techniques were demonstrated in the sixth study. The result verifies the potential of the proposed EBL method to improve the reliability and repeatability of assessments. Additionally, improving the image quality, i.e., using the proposed enhancement method, can reveal more capillary details for an observer compared to the raw original image, and the results motivate the future development of automatic tools for the EBL method, e.g., automatic segmentation and capillary detection and characterization.

Nailfold capillaroscopy is one of the various noninvasive bioengineering methods used to investigate skin microcirculation. It is an effective examination for assessing microvascular changes in the peripheral circulation; hence it has a significant role for the diagnosis of Systemic sclerosis with the classic changes of giant capillaries as well as the decline in capillary density with capillary dropout. The decline in capillary density is one of microangiopathic features existing in connective tissue disease. It is detectable with nailfold capillaroscopy. This parameter is assessed by applying quantitative measurement. In this article, we reviewed a common method for calculating the capillary density and the relation between the number of capillaries as well as the existence of digital ulcers, pulmonary arterial hypertension, autoantibodies, scleroderma patterns and different scoring system.

Nailfold capillaroscopy is a practical method for identifying and obtaining morphological changes in capillaries which might reveal relevant information about diseases and health. Capillaroscopy is harmless, and seems simple and repeatable. However, there is lack of established guidelines and instructions for acquisition as well as the interpretation of the obtained images; which might lead to various ambiguities. In addition, assessment and interpretation of the acquired images are very subjective. In an attempt to overcome some of these problems, in this study a new modified technique for assessment of nailfold capillary density is introduced. The new method is named elliptic broken line (EBL) which is an extension of the two previously known methods by defining clear criteria for finding the apex of capillaries in different scenarios by using a fitted elliptic. A graphical user interface (GUI) is developed for pre-processing, manual assessment of capillary apexes and automatic correction of selected apexes based on 90° rule. Intra- and inter-observer reliability of EBL and corrected EBL is evaluated in this study. Four independent observers familiar with capillaroscopy performed the assessment for 200 nailfold videocapillaroscopy images, form healthy subject and systemic lupus erythematosus patients, in two different sessions. The results show elevation from moderate (ICC = 0.691) and good (ICC = 0.753) agreements to good (ICC = 0.750) and good (ICC = 0.801) for intra- and inter-observer reliability after automatic correction of EBL. This clearly shows the potential of this method to improve the reliability and repeatability of assessment which motivates us for further development of automatic tool for EBL method.

Capillaries play a crucial role in the microcircu-latory system by exchanging metabolic substrates and waste products between blood and various tissues. The behavior of capillaries is affiliated with the number of capillaries per unit volume of tissue. Among the various noninvasive techniques available for analyzing skin microcirculation, nailfold capillaroscopy is considered to be a simple and easy-to-perform technique that allows a direct in-vivo visualization of the capillary network. Capillary density is one of the most important parameters in the studies involving capillaroscopy images. Capillary density in most of studies is defined as the number of capillaries in one millimeter span of the distal row in each finger or toe. This definition is silent about counting or excluding the number of capillary with different shapes. However, there is no single standard for counting the number of capillaries in a span of one millimeter. In this paper, a novel method is proposed for determining the nailfold capillary density. This method is a modified combination of two existing techniques: the direct observation and the 90° method. Compared to the two existing approaches, the proposed method is more straightforward and easy to use for cases in which the capillaries have different shapes and sizes. Through different examples, we have shown how this method can be used to select the apex point of capillary and subsequently count the number of capillaries with several papillae.