Cerebral Palsy (CP) requires individualized interventions due to its complex nature affecting movement and coordination. Medical Human Digital Twin (MHDT) technology offers significant advancements in CP management by creating virtual representations of patients’ physical and neurological states. This paper reviews MHDT applications in CP diagnosis, therapy, and rehabilitation. Advanced imaging techniques and sensor integration enhance early and accurate diagnosis through detailed patient modeling. Therapeutic applications focus on personalized treatment plans using AI-driven models, VR/AR, and robotic-assisted devices, ensuring continuous optimization through real-time monitoring. Rehabilitation strategies benefit from immersive technologies and adaptive feedback, improving patient engagement and outcomes. The integration of human empathy with data-driven insights enables more precise and effective care. Ethical considerations, data management challenges, and future research directions, including interdisciplinary collaboration, are discussed. MHDTs present a transformative approach to CP care, promising improved patient outcomes and personalized healthcare solutions.

Implantable cardioverter-defibrillators (ICDs) present several challenges regarding their detection, localization, and continuous monitoring within patients’ bodies. These challenges include maintaining the integrity of electrodes, preventing local tissue irritation, avoiding device displacement, and ensuring compatibility with MRI. Continuous monitoring is critical to ensure patient safety and the functional integrity of the devices. This paper systematically reviews approaches for the comprehensive monitoring of Cardiac Implantable Electronic Devices (CIEDs) over the past ten years, focusing on the CaRDIA-X procedure. We examine advanced protocols and integrated algorithms designed for this purpose. Specifically, we introduce the CaRDIA-X protocol, a manual method for identifying CIEDs based on the analysis of device patterns and the arrangement of elements on X-ray images. We compare this protocol with alternative detection methods, including artificial intelligence algorithms, and emphasize the field’s most significant studies and comprehensive comparative analyses. In conclusion, we propose improvements to the classic CaRDIA-X algorithm, focusing on automating the CaRDIA-X procedure, which holds significant potential for enhancing patient management and therapeutic efficacy. This integrated proposition offers a holistic perspective on CIED performance and potential issues.

Synovitis, characterized by inflammation of the synovial membrane in human joints, poses significant diagnostic and treatment challenges. This review presents methods and recent machine learning (ML) developments to analyze synovial arthritis in joints that help overcome these challenges. The methods described in the review include traditional ML algorithms, novel deep learning architectures and recent medical imaging techniques. Key challenges are discussed including the need for large and diverse datasets, model interpretability, generalization to different patient populations, dealing with data variability, and reducing computational complexity. The review also examines integrating multimodal data sources, advances in transfer learning, and developing robust, interpretable models as future directions. It includes enhancing early diagnostic capabilities, leveraging joint-on-a-chip simulations, and investigating signaling pathways in rheumatoid arthritis. This study aims to provide a consolidated resource for interdisciplinary researchers, clinicians and practitioners in the fields of rheumatology and medical imaging as it synthesizes current research to understand better ML methods in the detection of synovitis in human joints, paving the way for improved diagnostic and care capabilities over the patient.

Synovitis is the inflammation of a synovial membrane surrounding a joint. Its assessment is an important step in the diagnosis and treatment of rheumatoid arthritis. Joint detection is the first stage of an automated method of assessment of a degree of synovitis, from an Ultrasound (USG) image of a finger joint and its surrounding area. A joint detector consists of three parts: image preprocessing, feature extraction, and classification. Each part contains adjustable parameters that must be set experimentally to ensure the proper operation of the detector. Both the structure of a joint detector and a procedure for finding a near-optimal configuration of the adjustable parameters are described. The optimization process is based on two evaluation measures: Area Under the Receiver Operating Characteristic Curve (AUC) and False Positive Count (FPC). The optimization process decreases the number of pictures with multiple detections, which was the main point of works presented in this paper. This was achieved by increasing the number of components of the homogeneous mixed-SURF descriptor which has the greatest influence on the final result. Non-SURF descriptors achieve poorer classification results. Our research led to the creation of a better joint detector which could positively influence the final results of inflammation level classification.

The paper deals with the issue of comparing trajectories in augmented and mixed realities incorporated in medical and healthcare systems. The comparison process brings to a sequence of comparing pairs of elements of these trajectories. We match up pairs using either time-domain mapping or projection in Euclidean space. The suitability of matched up pairs are assessed based on gold standard Dynamic Time Wrapping method. Finally, we propose two similarity relations which incorporate the idea of coordination space and position vectors.

The paper presents a comprehensive overview of intelligent video analytics and human action recognition methods. The article provides an overview of the current state of knowledge in the field of human activity recognition, including various techniques such as pose-based, tracking-based, spatio-temporal, and deep learning-based approaches, including visual transformers. We also discuss the challenges and limitations of these techniques and the potential of modern edge AI architectures to enable real-time human action recognition in resource-constrained environments.

Identifying the separate parts in ultrasound images such as bone and skin plays a crucial role in the synovitis detection task. This paper presents a detector of bone and skin regions in the form of a classifier which is trained on a set of annotated images. Selected regions have labels: skin or bone or none. Feature vectors used by the classifier are assigned to image pixels as a result of passing the image through the bank of linear and nonlinear filters. The filters include Gaussian blurring filter, its first and second order derivatives, Laplacian as well as positive and negative threshold operations applied to the filtered images. We compared multiple supervised learning classifiers including Naive Bayes, k-Nearest Neighbour, Decision Trees, Random Forest, AdaBoost and Support Vector Machines (SVM) with various kernels, using four classification performance scores and computation time. The Random Forest classifier was selected for the final use, as it gives the best overall evaluation results.

Detecting moving objects in video sequences may be particularly challenging because of the characteristics of the objects, such as their size, colour, contrast, velocity and trajectory. Industrial video tagging systems should generate tags based on information inferred from video frames and learn relations between given concepts. In opposite to traditional methods such systems should effectively segment semantic objects in tagged videos, even when the image-based object detectors provide inaccurate proposals. Such systems usually base on the knowledge which is constantly updated to acquire the dynamics of the indexed concepts. In this paper we present a short review of the most importants algorithms for detection of markers in video sequences and problems related with practical applications.

The cytoskeleton is a dynamical protein structure that plays a key role in cellular physiological processes. The assessment of the cytoskeleton dynamics is of prime importance to reveal mechanisms involved in cell adaptation to any type of stress. Fluorescence imaging of cytoskeleton structures enables analysis of the impact of mechanical stimulation on the cytoskeleton, e.g. the exposure to nanosecond pulsed electric field (nsPEF). NsPEF utilizes pulses in nanosecond range (from few up to 300 ns) and high voltage (up to 100 kVs), which can significantly affect all cell structures. This method can be applied in medicine, industry and molecular biology for the efficient molecule transport across the cell membranes. The detailed analysis of the separate cell structures and protein organization seems to be important in the overall evaluation of the nsPEF impact on treated cells. The automatic image analysis seems crucial for proper evaluation of the therapeutic efficiency. The aim of the study is a computational approach for efficient F-actin filament analysis in cells exposed to electroporation process in the nanosecond range. The research was performed on cancer cells derived from gastrointestinal tract exposed to the nanosecond pulsed electric fields in relation to untreated controls. The presented tool might be useful in precise evaluation of the electroporation protocols effectiveness in cancer and normal cells. The proposed methodology showed high sensitivity values and similar accuracy compared to state-of-the-art methods.

Farmers explore the capabilities for applications of Robotic Process Automation (RPA) with image processing, pattern recognition and machine learning, so its logical to ask where best to apply this technology for maximum effect. Machines can do automated tasks better, cheaper and faster. One can use the camera from the sky and measure things on the earth especially on cropland from drones and satellites. Over time, drones have increased in capabilities and fallen in cost, and their use has greatly expanded especially in complex terrain. High quality remote sensing with spectral imaging using drones makes them interesting for regular use in Precision Agriculture (PA). Drones are often used in agriculture in ways that were highly controversial only a short time ago even there are no unified legislation on drones usage in agriculture. In this paper we address problem of Remote Sensing (RS) technologies combined with Unmanned Aerial System (UAS) platforms to support and develop selected agriculture operations like map or sensor-based Variable Rate Application (VRA).

The intelligent video monitoring system SAVA has been implemented as a prototype at the 9th Technology Readiness Level. The source of data are video cameras located in the public space that provide HD video streaming. The aim of the study is to present an overview of the SAVA system enabling identification and classification in the real time of such behaviors as: walking, running, sitting down, jumping, lying, getting up, bending, squatting, waving, and kicking. It also can identify interactions between persons, such as: greeting, passing, hugging, pushing, and fighting. The system has module-based architecture and is combined of the following modules: acquisition, compression, path detection, path analysis, motion description, action recognition. The effect of the modules operation is a recognized behavior or interaction. The system achieves a classification correctness level of 80% when there are more than ten classes.

Ultrasound is widely used in the diagnosis and follow-up of chronic arthritis. We present an evaluation of a novel automatic ultrasound diagnostic tool based on image recognition technology. Methods used in developing the algorithm are described elsewhere. For the purpose of evaluation, we collected 140 ultrasound images of metacarpophalangeal and proximal interphalangeal joints from patients with chronic arthritis. They were classified, according to hypertrophy size, into four stages (0-3) by three independent human observers and the algorithm. An agreement ratio was calculated between all observers and the standard derived from results of human staging using. statistics kappa Results was significant in all pairs, with the highest p value of 3.9 x 10(-6) kappa coefficients were lower in algorithm/human pairs than between human assessors. The algorithm is effective in staging synovitis hypertrophy. It is, however, not mature enough to use in a daily practice because of limited accuracy and lack of color Doppler recognition. These limitations will be addressed in the future. (E-mail: pawel. franciszek. mielnik@helse-forde. no) (C) 2018World Federation for Ultrasound in Medicine & Biology. All rights reserved.

Algorithms of tracking and action recognition are still under development and many problems still have to be solved. New methods are usually tested on available benchmarks with defined actions and human behavior however such approach has many limitations. For that reason the authors proposed new procedure of generating random action instances using on graph-based scenarios. Such idea can be applied in creation of different datasets as well as in simulations.

Biological membrane images contain a variety of objects and patterns, which convey information about the underlying biological structures and mechanisms. The field of image analysis includes methods of computation which convert features and objects identified in images into quantitative information about biological structures represented in these images. Microscopy images are complex, noisy, and full of artifacts and consequently require multiple image processing steps for the extraction of meaningful quantitative information. This review is focused on methods of analysis of images of cells and biological membranes such as detection, segmentation, classification and machine learning, registration, tracking, and visualization. These methods could make possible, for example, to automatically identify defects in the cell membrane which affect physiological processes. Detailed analysis of membrane images could facilitate understanding of the underlying physiological structures or help in the interpretation of biological experiments.

Among a broad spectrum of published methods of recognition of human actions in video sequences, one approach stands out, different from the rest by not relying on detection of interest points or events, extraction of features, region segmentation or finding trajectories, which are all prone to errors. It is based on representation of a time segment of a video sequence as a point on a manifold, and uses a geodesic distance defined on manifold for comparing and classifying video segments. A manifold based representation of a video sequence is obtained starting with a 3d array of consecutive image frames or a 3rd order tensor, which is decomposed into three 3 × k arrays that are mapped to a point of a manifold. This article presents a review of manifold based methods for human activity recognition and sparse coding of images that also rely on a manifold representation. Results of a human activity classification experiment that uses an implemented action recognition method based on a manifold representation illustrate the presentation.

In this paper we present a method of anonymization of people's faces in video. Results are analyzed on the basis of optical flow methods. Anonymization bases on face detection. Because of mistakes made by such detectors in video sequences, gaps and false detections appear. They are recognized using the results of face detections and optical flow analysis. In this paper we describe: face detectors and the results of method of analysis of optical-flow based detector. We present novel method of filing gaps and false detection recognizing with use of optical flow. Then we present visual results.

Automatic license plate recognition (ALPR) methods and software are used in toll collection, traffic monitoring and other areas of road transport industry. Majority of ALPR methods and almost all in industrial use, try to recognize a license plate identifier from a single image. However, in a sequence of images, recognition of a license plate in any frame can be improved by considering the information from preceding and succeeding frames, using video object tracking. A new approach is presented, for combining a video tracking and a single frame ALPR method to improve the recognition rate. Unlike earlier techniques which are tied to specific object tracking and identifier recognition methods, the new method can be used with almost any tracking and single frame ALPR methods. Its key part is a method for clustering and alignment of candidate license plate identifiers in a video track. The results from five video sequences taken from a surveillance camera under various weather and light conditions demonstrate the recognition rate improvements.

Nuclear morphology abnormalities in cells are often the symptom of the cell death. However, minor disturbances in nuclear shape, such as a slight blebbing or deformation, may indicate characteristic medical disorders or therapeutic effect. The analysis of microscopic images requires time consuming observations and meticulous analysis that often are encumbered with human mistake. In the present work, image analysis of nuclei as a method of morphological verification is presented. The automated analysis of numerous cellular nuclei images may be a useful tool for any biological or analytical laboratory.

Human pose estimation from monocular images is one of the most significant aspects of modern computer vision tasks and its application demand is still increasing in such areas as automatic images indexing or human activity recognition from video. Among many approaches applied in these areas the one based on pose estimation gives, beyond all doubts, one of the most powerful representation of human on the picture in sense of sparsity and semantics. In this paper we provide a detailed survey of the most efficient methods in 2D pose estimation domain as well as the test results of selected methods on the LSP dataset, which is commonly used by state-of-the-art works.

Medical ultrasound imaging is an important tool in diagnosing and monitoring synovitis, which is an inflammation of the synovial membrane that surrounds a joint. Ultrasound images are examined by medical experts to assess the presence and progression of synovitis. Automating image analysis reduces the costs and increases the availability of the ultrasound diagnosis of synovitis and diminishes or eliminates subjective discrepancies. This article describes research that is concerned with the problem of the automatic estimation of the state of the activity of finger joint inflammation using the information that is present in ultrasonography imaging.