Search

Research has tremendously contributed to the developments in both practical and fundamental aspects of limb prosthetics. These advancements are reflected in scientific articles, particularly in the most cited papers. This article aimed to identify the 100 top-cited articles in the field of limb prosthetics and to investigate their main characteristics. Articles related to the field of limb prosthetics and published in the Web of Knowledge database of the Institute for Scientific Information (ISI) from the period of 1980 to 2012. The 100 most cited articles in limb prosthetics were selected based on the citation index report. All types of articles except for proceedings and letters were included in the study. The study design and level of evidence were determined using Sackett's initial rules of evidence. The level of evidence was categorized either as a systematic review or meta-analysis, randomized controlled trial, cohort study, case-control study, case series, expert opinion, or design and development. The top cited articles in prosthetics were published from 1980 to 2012 with a citation range of 11 to 90 times since publication. The mean citation rate was 24.43 (SD 16.7) times. Eighty-four percent of the articles were original publications and were most commonly prospective (76%) and case series studies (67%) that used human subjects (96%) providing level 4 evidence. Among the various fields, rehabilitation (47%), orthopedics (29%), and sport sciences (28%) were the most common fields of study. The study established that studies conducted in North America and were written in English had the highest citations. Top cited articles primarily dealt with lower limb prosthetics, specifically, on transtibial and transradial prosthetic limbs. Majority of the articles were experimental studies. [ABSTRACT FROM AUTHOR]

Medical imaging datasets for research are frequently collected from multiple imaging centers using different scanners, protocols, and settings. These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent differences between various vendors, hardware upgrades, protocol changes, and scanner calibration drift, as well as to ensure consistent data for medical image processing techniques. Given the critical importance and widespread relevance of this issue, a vast array of image harmonization methodologies have emerged, with deep learning-based approaches driving substantial advancements in recent times. The goal of this review paper is to examine the latest deep learning techniques employed for image harmonization by analyzing cutting-edge architectural approaches in the field of medical image harmonization, evaluating both their strengths and limitations. This paper begins by providing a comprehensive fundamental overview of image harmonization strategies, covering three critical aspects: established imaging datasets, commonly used evaluation metrics, and characteristics of different scanners. Subsequently, this paper analyzes recent structural MRI (Magnetic Resonance Imaging) harmonization techniques based on network architecture, network learning algorithm, network supervision strategy, and network output. The underlying architectures include U-Net, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based generative models, transformer-based approaches, as well as custom-designed network architectures. This paper investigates the effectiveness of Disentangled Representation Learning (DRL) as a pivotal learning algorithm in harmonization. Lastly, the review highlights the primary limitations in harmonization techniques, specifically the lack of comprehensive quantitative comparisons across different methods. The overall aim of this review is to serve as a guide for researchers and practitioners to select appropriate architectures based on their specific conditions and requirements. It also aims to foster discussions around ongoing challenges in the field and shed light on promising future research directions with the potential for significant advancements. [ABSTRACT FROM AUTHOR]

Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation methods have far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. We searched Google Scholar for papers published from January 1, 2016 to December 31, 2023, using keywords "multi-organ segmentation" and "deep learning", resulting in 327 papers. We followed the PRISMA guidelines for paper selection, and 195 studies were deemed to be within the scope of this review. We summarized the two main aspects involved in multi-organ segmentation: datasets and methods. Regarding datasets, we provided an overview of existing public datasets and conducted an in-depth analysis. Concerning methods, we categorized existing approaches into three major classes: fully supervised, weakly supervised and semi-supervised, based on whether they require complete label information. We summarized the achievements of these methods in terms of segmentation accuracy. In the discussion and conclusion section, we outlined and summarized the current trends in multi-organ segmentation. [ABSTRACT FROM AUTHOR]

Recently, the incidence rate and mortality of various acute or chronic vascular occlusive diseases have increased yearly. As one of the most effective measures to treat them, vascular stents have been widely studied by researchers, and presently, the most commonly used is a drug-eluting stent, which reduces the process of rapid endothelialization because the drug is not selective. Fortunately, with the discovery and exploration of micro–nanostructures that can regulate cells selectively, reducing the incidence of "intravascular restenosis" and achieving rapid endothelialization simultaneously are possible through a special structure that cannot only improve endothelial cells (ECs), but also inhibit smooth muscle cells (SMCs). Therefore, this paper mainly introduces the preparation methods of micro–nanostructures used in the past, as well as the detection methods of EC and SMC. Then, the various functions of different dimensional structures for different cells are summarized and analyzed. Finally, the application of micro–nanostructure in future stent materials is summarized and proposed. [ABSTRACT FROM AUTHOR]

Transformers have been widely used in many computer vision challenges and have shown the capability of producing better results than convolutional neural networks (CNNs). Taking advantage of capturing long-range contextual information and learning more complex relations in the image data, Transformers have been used and applied to histopathological image processing tasks. In this survey, we make an effort to present a thorough analysis of the uses of Transformers in histopathological image analysis, covering several topics, from the newly built Transformer models to unresolved challenges. To be more precise, we first begin by outlining the fundamental principles of the attention mechanism included in Transformer models and other key frameworks. Second, we analyze Transformer-based applications in the histopathological imaging domain and provide a thorough evaluation of more than 100 research publications across different downstream tasks to cover the most recent innovations, including survival analysis and prediction, segmentation, classification, detection, and representation. Within this survey work, we also compare the performance of CNN-based techniques to Transformers based on recently published papers, highlight major challenges, and provide interesting future research directions. Despite the outstanding performance of the Transformer-based architectures in a number of papers reviewed in this survey, we anticipate that further improvements and exploration of Transformers in the histopathological imaging domain are still required in the future. We hope that this survey paper will give readers in this field of study a thorough understanding of Transformer-based techniques in histopathological image analysis, and an up-to-date paper list summary will be provided at https://github.com/S-domain/Survey-Paper. [ABSTRACT FROM AUTHOR]

Deep Self-Attention Network (Transformer) is an encoder–decoder architectural model that excels in establishing long-distance dependencies and is first applied in natural language processing. Due to its complementary nature with the inductive bias of convolutional neural network (CNN), Transformer has been gradually applied to medical image processing, including kidney image processing. It has become a hot research topic in recent years. To further explore new ideas and directions in the field of renal image processing, this paper outlines the characteristics of the Transformer network model and summarizes the application of the Transformer-based model in renal image segmentation, classification, detection, electronic medical records, and decision-making systems, and compared with CNN-based renal image processing algorithm, analyzing the advantages and disadvantages of this technique in renal image processing. In addition, this paper gives an outlook on the development trend of Transformer in renal image processing, which provides a valuable reference for a lot of renal image analysis. [ABSTRACT FROM AUTHOR]

Background: Metastasis is one of the main factors leading to the high mortality rate of gastric cancer. The current monitoring methods are not able to accurately monitor gastric cancer metastasis. Methods: In this paper, we constructed a new type of hollow Mn 3 O 4 nanocomposites, Mn 3 O 4 @HMSN-Cy7.5-FA, which had a size distribution of approximately 100 nm and showed good stability in different liquid environments. The in vitro magnetic resonance imaging (MRI) results show that the nanocomposite has good response effects to the acidic microenvironment of tumors. The acidic environment can significantly enhance the contrast of T 1 -weighted MRI. The cellular uptake and endocytosis results show that the nanocomposite has good targeting capabilities and exhibits good biosafety, both in vivo and in vitro. In a gastric cancer nude mouse orthotopic metastatic tumor model, with bioluminescence imaging's tumor location information, we realized in vivo MRI/fluorescence imaging (FLI) guided precise monitoring of the gastric cancer orthotopic and metastatic tumors with this nanocomposite. Results: This report demonstrates that Mn 3 O 4 @HMSN-Cy7.5-FA nanocomposites is a promising nano-diagnostic platform for the precision diagnosis and therapy of gastric cancer metastasis in the future. Conclusions: In vivo MRI/FLI imaging results show that the nanocomposites can achieve accurate monitoring of gastric cancer tumors in situ and metastases. BLI's tumor location information further supports the good accuracy of MRI/FLI dual-modality imaging. The above results show that the MHCF NPs can serve as a good nano-diagnostic platform for precise in vivo monitoring of tumor metastasis. This nanocomposite provides more possibilities for the diagnosis and therapy of gastric cancer metastases. [ABSTRACT FROM AUTHOR]

Multiphase information fusion and spatiotemporal feature modeling play a crucial role in the task of four-phase CT lesion recognition. In this paper, we propose a four-phase CT lesion recognition algorithm based on multiphase information fusion framework and spatiotemporal prediction module. Specifically, the multiphase information fusion framework uses the interactive perception mechanism to realize the channel-spatial information interactive weighting between multiphase features. In the spatiotemporal prediction module, we design a 1D deep residual network to integrate multiphase feature vectors, and use the GRU architecture to model the temporal enhancement information between CT slices. In addition, we employ CT image pseudo-color processing for data augmentation and train the whole network based on a multi-task learning framework. We verify the proposed network on a four-phase CT dataset. The experimental results show that the proposed network can effectively fuse the multi-phase information and model the temporal enhancement information between CT slices, showing excellent performance in lesion recognition. [ABSTRACT FROM AUTHOR]

Background: Sarcopenia is a muscle disorder causing a progressive reduction of muscle mass and strength, but the mechanism of its manifestation is still partially unknown. The three main parameters to assess are: muscle strength, muscle volume or quality and low physical performance. There is not a definitive approach to assess the musculoskeletal condition of frail population and often the available tests to be performed in those clinical bedridden patients is reduced because of physical impairments. In this paper, we propose a novel instrumental multi-domain and non-invasive approach during a well-defined protocol of measurements for overcoming these limitations. A group of 28 bedridden elder people, subjected to surgery after hip fracture, was asked to perform voluntary isometric contractions at the 80% of their maximum voluntary contraction with the non-injured leg. The sensor employed before and/or during the exercise were: ultrasound to determine the muscle architecture (vastus lateralis); force acquisition with a load cell placed on the chair, giving an indication of the muscle strength; surface electromyography (EMG) for monitoring muscular electrical activity; time-domain (TD) near-infrared spectroscopy (NIRS) for evaluating muscle oxidative metabolism. Results: A personalized "report card" for each subject was created. It includes: the force diagram (both instantaneous and cumulative, expected and measured); the EMG–force diagram for a comparison between EMG derived median frequency and measured force; two graphs related to the hemodynamic parameters for muscle oxidative metabolism evaluation, i.e., oxy-, deoxy-, total-hemoglobin and tissue oxygen saturation for the whole exercise period. A table with the absolute values of the previous hemodynamic parameters during the rest and the ultrasound related parameters are also included. Conclusions: In this work, we present the union of protocols, multi-domain sensors and parameters for the evaluation of the musculoskeletal condition. The novelties are the use of sensors of different nature, i.e., force, electrical and optical, together with a new way to visualize and combine the results, by means of a concise, exhaustive and personalized medical report card for each patient. This assessment, totally non-invasive, is focused on a bedridden population, but can be extended to the monitoring of rehabilitation progresses or of the training of athletes. [ABSTRACT FROM AUTHOR]

Background: The impact of the pandemic caused by the coronavirus (SARS-CoV-2), causing the disease COVID-19, has brought losses to the world in terms of deaths, economic and health problems. The expected return of the public to activities adapted to the new health situation led to discussions about the use of vaccination and its effects. However, the demand for proof of vaccination showed how inconsistent, unregistered, and uncontrolled this health process is with current technologies. Despite the proven effectiveness of vaccines in reducing infection rates, mortality, and morbidity, there are still doubts about their use in preventing certain infections and injuries, as well as the use of digital medical records for identification at public events and disease prevention. Therefore, this review aims to analyze the use of digital immunization cards in disease prevention in general.Methods: A systematic review of Science, PubMed/MEDLINE, LILACS /BSV, CINALH, and IEEE and Xplore was performed using PRISMA guidelines. The authors summarized the studies conducted over the last decade on the impacts of prophylaxis by control through immunization cards. Studies were selected using the following terms: Vaccination; Mobile Applications; Health Smarts Cards; Immunization Programs; Vaccination Coverage. For data analysis, we used Mendeley, Excel, RStudio, and Bibliometrix software among others.Results: A total of 1828 publications were found. After applying eligibility criteria (Articles published in Portuguese, Spanish or English in the last 10 years). Studies that only dealt with paper or physical records were excluded, as well as studies that were not linked to their country's health Department, as a possibility of bias exists with these types of information). After removing duplicates and applying filters 1 and 2, we included 18 studies in this review. This resulted in 18 papers that met our priori inclusion criteria; it was found that the most relevant sources were from the databases of the Institute of Electrical and Electronics Engineers (IEEE).Conclusions: Considering the selected studies, we found that scientific evidence and epidemiological surveillance are essential tools to characterize the efficiency and effectiveness of immunization passport protection intervention and to ethically justify them. Technological development of digital vaccine passports can assist in vaccination programs and positively impact disease prophylaxis. [ABSTRACT FROM AUTHOR]

This review presents an in-depth examination of implantable antennas for various biomedical purposes. The development of implantable antennas, including their designs, materials, and operating principles, are introduced at the beginning of the discussion. An overview of the many kinds of implantable antennas utilized in implantable medical devices (IMDs) are presented in this study. The article then discusses the important factors to consider when developing implantable antennas for biomedical purposes, including implant placement, frequency range, and power needs. This investigation additionally examines the challenges and limitations encountered with implantable antennas, including the limited space available within the human body, the requirement for biocompatible materials, the impact of surrounding tissue on antenna performance, tissue attenuation, and signal interference. This review also emphasizes the most recent advances in implanted antenna technology, such as wireless power transmission, multiband operation, and miniaturization. Furthermore, it offers illustrations of several biomedical uses for implantable antennas, including pacemaker, capsule endoscopy, intracranial pressure monitoring, retinal prostheses, and bone implants. This paper concludes with a discussion of the future of implantable antennas and their possible use in bioelectronic medicine and novel medical implants. Overall, this survey offers a thorough analysis of implantable antennas in biomedical applications, emphasizing their importance in the development of implantable medical technology. [ABSTRACT FROM AUTHOR]

Nanotechnology has contributed important innovations to medicine and dentistry, and has also offered various applications to the field of orthodontics. Intraoral appliances must function in a complex environment that includes digestive enzymes, a diverse microbiome, mechanical stress, and fluctuations of pH and temperature. Nanotechnology can improve the performance of orthodontic brackets and archwires by reducing friction, inhibiting bacterial growth and biofilm formation, optimizing tooth remineralization, improving corrosion resistance and biocompatibility of metal substrates, and accelerating or decelerating orthodontic tooth movement through the application of novel nanocoatings, nanoelectromechanical systems, and nanorobots. This comprehensive review systematically explores the orthodontic applications of nanotechnology, particularly its impacts on tooth movement, antibacterial activity, friction reduction, and corrosion resistance. A search across PubMed, the Web of Science Core Collection, and Google Scholar yielded 261 papers, of which 28 met our inclusion criteria. These selected studies highlight the significant benefits of nanotechnology in orthodontic devices. Recent clinical trials demonstrate that advancements brought by nanotechnology may facilitate the future delivery of more effective and comfortable orthodontic care. [ABSTRACT FROM AUTHOR]

Background: Estimating relative tidal volume is an important factor when monitoring breathing status. The relationship between temperature and respiration volume has rarely been studied. In this paper, a formula was derived for calculating thermodiluted respiration volume from temperature changes in the nasal cavity. To evaluate the proposed formula, the study compared the relative tidal volume estimated by the proposed formula with that recorded by a respiration volume monitor (Exspiron1Xi, RVM). Thermal data were obtained for 8 cases at a rate of 10 measurements per second. Simultaneous recordings by the RVM are regarded as the reference.Results: The mean of ICC coefficient is 0.948 ± 0.030, RMSE is 0.1026 ± 0.0284, R-squared value is 0.8962 ± 0.065 and linear regression coefficient [Formula: see text] is 0.966 ± 0.104, [Formula: see text] is 0.042 ± 0.057. Bland-Altman plot showed 96.01% of samples that the difference between the measured and estimated values exists within 2 standard deviations.Conclusions: In this paper, a model that can thermodynamically calculate the relationship between thermal energy and respiration volume is proposed. The thermodiluted model is a feasible method for estimating relative respiration tidal volumes. [ABSTRACT FROM AUTHOR]

Background: The effectiveness of in vitro fertilization depends on the assessment and selection of oocytes and embryos with the highest developmental potential. One of the tasks in the ICSI (intracytoplasmic sperm injection) procedure is the classification of oocytes according to the stages of their meiotic maturity. Oocytes classification traditionally is done manually during their observation under the light microscope. The paper is part of the bigger task, the development of the system for optimal oocyte and embryos selection. In the hereby work, we present the method for the automatic classification of oocytes based on their images, that employs DNN algorithms. Results: For the purpose of oocyte class determination, two structures based on deep neural networks were applied. DeepLabV3Plus was responsible for the analysis of oocyte images in order to extract specific regions of oocyte images. Then extracted components were transferred to the network, inspired by the SqueezeNet architecture, for the purpose of oocyte type classification. The structure of this network was refined by a genetic algorithm in order to improve generalization abilities as well as reduce the network's FLOPs thus minimizing inference time. As a result, Acc ¯ at the level of 0.964 was obtained at the level of the validation set and 0.957 at the level of the test set. Generated neural networks as well as code that allows running the processing pipe were made publicly available. Conclusions: In this paper, the complete pipeline was proposed that is able to automatically classify human oocytes into three classes MI, MII, and PI based on the oocytes' microscopic image. [ABSTRACT FROM AUTHOR]

Background: It is known that inflammatory bowel disease is the result of a defective immune system, and immunotherapy and biological therapy have gradually become important means to treat it. This paper focused on the bibliometric statistical analysis of the current research progress to summarize the research status of this field and analyze the research trends in recent years.Methods: Two visualization tools, CiteSpace and VOSviewer, were used to explore the data of journals, institutions, countries/regions, authors, references, and keywords for the literature included in the Web of Science Core Collection from January 1, 2002, to December 31, 2021.Results: A total of 312 papers were published in 120 journals by 603 institutions from 40 countries/regions, with 9463 co-cited references. The United States has the most publications with the highest total citations in the world. Inflammatory Bowel Diseases published the maximum number of papers, and Gastroenterology devoted the most co-citations to immunotherapy and biological therapy for IBD. In addition, we found that the studies before 2009 mostly focused on clinical trials while researchers have paid more attention to clinical management in therapy for IBD since 2009. Combination therapy and management of the treatment for the disease have become research hotspots.Conclusion: The focus of immunotherapy and biotherapy for IBD has shifted from clinical trials to the management of the risks and benefits of immunotherapy. [ABSTRACT FROM AUTHOR]

Background: Tube misplacement into the tracheobronchial tract is associated with pneumothorax in 0.5% of cases. NGT verification only detects the position of the tube at the end of the procedure. Therefore, a safe nasogastric tube (SNGT) was created to detect the NGT position in real time in a simple and inexpensive way. This study aimed to prove the effectiveness of the SNGT prototype in Macaca fascicularis. Result: An SNGT producing 50% of the TV had 100% sensitivity and specificity in detecting the position of the tube at 100% of the TV, with a sensitivity of 100% and a specificity of 87.5%. There was a significant difference between the movement of the SNGT 50% TV and SNGT 100% TV airbags (p ≤ 0.05). However, there was no significant difference between the accuracy of placement of the 50% TV SNGT, 100% TV SNGT, and conventional NGT (p > 0.05). The pepsin enzyme had better sensitivity (100%) than pH paper (91.66%) in detecting the end-of-procedure tube position. This research has the potential to advance into human clinical trials. Conclusion: SNGTs are highly effective in detecting the NGT position inside the respiratory and digestive tracts to prevent misplacement. [ABSTRACT FROM AUTHOR]

Background: Over the decades, many publications have established respiratory muscle training (RMT) as an effective way in improving respiratory dysfunction in multiple populations. The aim of the paper is to determine the trend of research and multidisciplinary collaboration in publications related to RMT over the last 6 decades. The authors also sought to chart the advancement of RMT among people with spinal cord injury (SCI) over the last 60 years. Methods: Bibliometric analysis was made, including the publications' profiles, citation analysis and research trends of the relevant literature over the last 60 years. Publications from all time frames were retrieved from Scopus database. A subgroup analysis of publications pertinent to people with SCI was also made. Results: Research on RMT has been steadily increasing over the last 6 decades and across geographical locations. While medicine continues to dominate the research on RMT, this topic also continues to attract researchers and publications from other areas such as engineering, computer science and social science over the last 10 years. Research collaboration between authors in different backgrounds was observed since 2006. Source titles from non-medical backgrounds have also published articles pertinent to RMT. Among people with SCI, researchers utilised a wide range of technology from simple spirometers to electromyography in both intervention and outcome measures. With various types of interventions implemented, RMT generally improves pulmonary function and respiratory muscle strength among people with SCI. Conclusions: While research on RMT has been steadily increasing over the last 6 decades, more collaborations are encouraged in the future to produce more impactful and beneficial research on people who suffer from respiratory disorders. [ABSTRACT FROM AUTHOR]

Background: Striae gravidarum is a common occurrence in pregnancy and many women expect to prevent its development. At present, laser treatment has been used to improve the appearance of striae gravidarum, but the choice of laser type, treatment time, and frequency depend on the therapeutic effect. How to obtain an effective evaluation of striae gravidarum during and after treatment is very important. However, there is no unified evaluation parameter about striae gravidarum. In this paper, we studied the methodology evaluation of striae gravidarum severity. First, the laser therapeutic apparatus was selected as the experimental equipment and different striae gravidarum photos during treatment were obtained. Second, the subject evaluation parameters were chosen based on the literature research and the dermatologists' guidance. Then, the striae gravidarum photos were divided into different groups by dermatologists based on these parameters. Finally, the objective detection parameters were designed based on the photos feature and subject evaluation parameters. Then, the objective detection parameters were used as the input of the support vector machine and the evaluation results were compared. Results: Based on the subject evaluation parameters, the experimental data could be divided into mild, moderate and severe groups. The experiment results showed that the striae gravidarum severity of two randomly patients were improved before and after treatment, which verified the validity of the parameters. In addition, the chosen objective detection parameters were different among different groups. With all the objective parameters as the support vector machine input, we could achieve the best recognition rate (82.71%) in the striae gravidarum severity classification. The four parameters (color difference, average density, average width, distribution area) calculated from the photos as the input could achieve acceptable accuracy (81.69%). Conclusions: The subject evaluation parameters and objective detection parameters proposed in this paper can be used to evaluate the striae gravidarum severity, which is of great significance for the construction of auxiliary diagnostic instrument for striae gravidarum treatment. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 disease is putting unprecedented pressure on the global healthcare system. The CT (computed tomography) examination as a auxiliary confirmed diagnostic method can help clinicians quickly detect lesions locations of COVID-19 once screening by PCR test. Furthermore, the lesion subtypes classification plays a critical role in the consequent treatment decision. Identifying the subtypes of lesions accurately can help doctors discover changes in lesions in time and better assess the severity of COVID-19. Method: The most four typical lesion subtypes of COVID-19 are discussed in this paper, which are GGO (ground-glass opacity), cord, solid and subsolid. A computer-aided diagnosis approach of lesion subtype is proposed in this paper. The radiomics data of lesions are segmented from COVID-19 patients CT images with diagnosis and lesions annotations by radiologists. Then the three-dimensional texture descriptors are applied on the volume data of lesions as well as shape and first-order features. The massive feature data are selected by HAFS (hybrid adaptive feature selection) algorithm and a classification model is trained at the same time. The classifier is used to predict lesion subtypes as side decision information for radiologists. Results: There are 3734 lesions extracted from the dataset with 319 patients collection and then 189 radiomics features are obtained finally. The random forest classifier is trained with data augmentation that the number of different subtypes of lesions is imbalanced in initial dataset. The experimental results show that the accuracy of the four subtypes of lesions is (93.06%, 96.84%, 99.58%, and 94.30%), the recall is (95.52%, 91.58%, 95.80% and 80.75%) and the f-score is (93.84%, 92.37%, 95.47%, and 84.42%). Conclusion: The three-dimensional radiomics features used in this paper can better express the high-level information of COVID-19 lesions in CT slices. HAFS method aggregates the results of multiple feature selection algorithms intersects with traditional methods to filter out redundant features more accurately. After selection, the subtype of COVID-19 lesion can be judged by inputting the features into the RF (random forest) model, which can help clinicians more accurately identify the subtypes of COVID-19 lesions and provide help for further research. [ABSTRACT FROM AUTHOR]

Background: Because of osteoporosis, traffic accidents, falling from high places, and other reasons, the vertebral body can be compressed and even collapse. Vertebral implants can be used for clinical treatment. Because of the advantages of honeycomb sandwich structures, such as low cost, less material, light weight, high strength, and good cushioning performance. In this paper, the honeycomb sandwich structure was used as the basic structure of vertebral implants.Methods: The orthogonal experiment method is applied to analyse the size effect of honeycomb sandwich structures by the finite element method. Based on the minimum requirements of three indexes of peak stress, axial deformation, and anterior-posterior deformation, the optimal structure size was determined. Furthermore, through local optimization of the overall structure of the implant, a better honeycomb sandwich structure vertebral implant was designed.Results: The optimal structure size combination was determined as a panel thickness of 1 mm, wall thickness if 0.49 mm, cell side length of 1 mm, and height of 6 mm. Through local optimization, the peak stress was further reduced, the overall stress distribution was uniform, and the deformation was reduced. The optimized peak stress decreased to 1.041 MPa, the axial deformation was 0.1110%, and the anterior-posterior deformation was 0.0145%. A vertebral implant with good mechanical performance was designed.Conclusions: This paper is the first to investigate vertebral implants with honeycomb sandwich structures. The design and analysis of the vertebral implant with a honeycomb sandwich structure were processed by the finite element method. This research can provide a feasible way to analyse and design clinical implants based on biomechanical principles. [ABSTRACT FROM AUTHOR]

Background: People living with dementia often exhibit behavioural and psychological symptoms of dementia that can put their and others' safety at risk. Existing video surveillance systems in long-term care facilities can be used to monitor such behaviours of risk to alert the staff to prevent potential injuries or death in some cases. However, these behaviours of risk events are heterogeneous and infrequent in comparison to normal events. Moreover, analysing raw videos can also raise privacy concerns. Purpose: In this paper, we present two novel privacy-protecting video-based anomaly detection approaches to detect behaviours of risks in people with dementia. Methods: We either extracted body pose information as skeletons or used semantic segmentation masks to replace multiple humans in the scene with their semantic boundaries. Our work differs from most existing approaches for video anomaly detection that focus on appearance-based features, which can put the privacy of a person at risk and is also susceptible to pixel-based noise, including illumination and viewing direction. We used anonymized videos of normal activities to train customized spatio-temporal convolutional autoencoders and identify behaviours of risk as anomalies. Results: We showed our results on a real-world study conducted in a dementia care unit with patients with dementia, containing approximately 21 h of normal activities data for training and 9 h of data containing normal and behaviours of risk events for testing. We compared our approaches with the original RGB videos and obtained a similar area under the receiver operating characteristic curve performance of 0.807 for the skeleton-based approach and 0.823 for the segmentation mask-based approach. Conclusions: This is one of the first studies to incorporate privacy for the detection of behaviours of risks in people with dementia. Our research opens up new avenues to reduce injuries in long-term care homes, improve the quality of life of residents, and design privacy-aware approaches for people living in the community. [ABSTRACT FROM AUTHOR]

Purpose: Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has been applied in Vision Transformer to achieve state-of-the-art performance on image classification tasks. Recently, researchers have extended transformer to medical image segmentation tasks, resulting in good models. Methods: This review comprises publications selected through a Web of Science search. We focused on papers published since 2018 that applied the transformer architecture to medical image segmentation. We conducted a systematic analysis of these studies and summarized the results. Results: To better comprehend the benefits of convolutional neural networks and transformers, the construction of the codec and transformer modules is first explained. Second, the medical image segmentation model based on transformer is summarized. The typically used assessment markers for medical image segmentation tasks are then listed. Finally, a large number of medical segmentation datasets are described. Conclusion: Even if there is a pure transformer model without any convolution operator, the sample size of medical picture segmentation still restricts the growth of the transformer, even though it can be relieved by a pretraining model. More often than not, researchers are still designing models using transformer and convolution operators. [ABSTRACT FROM AUTHOR]

Background: Turning in place is a challenging motor task and is used as a brief assessment test of lower limb function and dynamic balance. This review aims to examine how research of instrumented analysis of turning in place is implemented. In addition to reporting the studied population, we covered acquisition systems, turn detection methods, quantitative parameters, and how these parameters are computed. Methods: Following the development of a rigorous search strategy, the Web of Science and Scopus were systematically searched for studies involving the use of turning-in-place. From the selected articles, the study population, types of instruments used, turn detection method, and how the turning-in-place characteristics were calculated. Results: Twenty-one papers met the inclusion criteria. The subject groups involved in the reviewed studies included young, middle-aged, and older adults, stroke, multiple sclerosis and Parkinson's disease patients. Inertial measurement units (16 studies) and motion camera systems (5 studies) were employed for gathering measurement data, force platforms were rarely used (2 studies). Two studies used commercial software for turn detection, six studies referenced previously published algorithms, two studies developed a custom detector, and eight studies did not provide any details about the turn detection method. The most frequently used parameters were mean angular velocity (14 cases, 7 studies), turn duration (13 cases, 13 studies), peak angular velocity (8 cases, 8 studies), jerkiness (6 cases, 5 studies) and freezing-of-gait ratios (5 cases, 5 studies). Angular velocities were derived from sensors placed on the lower back (7 cases, 4 studies), trunk (4 cases, 2 studies), and shank (2 cases, 1 study). The rest (9 cases, 8 studies) did not report sensor placement. Calculation of the freezing-of-gait ratio was based on the acceleration of the lower limbs in all cases. Jerkiness computation employed acceleration in the medio-lateral (4 cases) and antero-posterior (1 case) direction. One study did not reported any details about jerkiness computation. Conclusion: This review identified the capabilities of turning-in-place assessment in identifying movement differences between the various subject groups. The results, based on data acquired by inertial measurement units across studies, are comparable. A more in-depth analysis of tests developed for gait, which has been adopted in turning-in-place, is needed to examine their validity and accuracy. [ABSTRACT FROM AUTHOR]

Background: Atherosclerosis is one of the most frequent cardiovascular diseases. The dilemma faced by physicians is whether to treat or postpone the revascularization of lesions that fall within the intermediate range given by an invasive fractional flow reserve (FFR) measurement. The paper presents a monocentric study for lesions significance assessment that can potentially cause ischemia on the large coronary arteries. Methods: A new dataset is acquired, comprising the optical coherence tomography (OCT) images, clinical parameters, echocardiography and FFR measurements collected from 80 patients with 102 lesions, with stable multivessel coronary artery disease. Having the ground truth given by the invasive FFR measurement, the dataset is challenging because almost 40% of the lesions are in the gray zone, having an FFR value between 0.75 and 0.85. Twenty-six features are extracted from OCT images, clinical characteristics, and echocardiography and the most relevant are identified by examining the models' accuracy. An ensembled learning is performed for solving the binary classification problem of lesion significance considering the leave-one-out cross-validation approach. Results: Ensemble models are designed from the multi-features voting from 5 features models by prediction aggregation with a maximum accuracy of 81.37% and a maximum area under the curve score (AUC) of 0.856. Conclusions: The proposed explainable supervised learning-based lesion classification is a new method that can be improved by training with a larger multicenter dataset for further designing a tool for guiding the decision making of the clinician for the cases outside the gray zone and for the other situation extra clinical information about the lesion is needed. [ABSTRACT FROM AUTHOR]

Background: This systematic review and meta-analysis were conducted to objectively evaluate the evidence of machine learning (ML) in the patient diagnosis of Intracranial Hemorrhage (ICH) on computed tomography (CT) scans. Methods: Until May 2023, systematic searches were conducted in ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE for studies that evaluated the diagnostic precision of ML model-assisted ICH detection. Patients with and without ICH as the target condition who were receiving CT-Scan were eligible for the research, which used ML algorithms based on radiologists' reports as the gold reference standard. For meta-analysis, pooled sensitivities, specificities, and a summary receiver operating characteristics curve (SROC) were used. Results: At last, after screening the title, abstract, and full paper, twenty-six retrospective and three prospective, and two retrospective/prospective studies were included. The overall (Diagnostic Test Accuracy) DTA of retrospective studies with a pooled sensitivity was 0.917 (95% CI 0.88–0.943, I2 = 99%). The pooled specificity was 0.945 (95% CI 0.918–0.964, I2 = 100%). The pooled diagnostic odds ratio (DOR) was 219.47 (95% CI 104.78–459.66, I2 = 100%). These results were significant for the specificity of the different network architecture models (p-value = 0.0289). However, the results for sensitivity (p-value = 0.6417) and DOR (p-value = 0.2187) were not significant. The ResNet algorithm has higher pooled specificity than other algorithms with 0.935 (95% CI 0.854–0.973, I2 = 93%). Conclusion: This meta-analysis on DTA of ML algorithms for detecting ICH by assessing non-contrast CT-Scans shows the ML has an acceptable performance in diagnosing ICH. Using ResNet in ICH detection remains promising prediction was improved via training in an Architecture Learning Network (ALN). [ABSTRACT FROM AUTHOR]

Background: Infertility is a significant problem of humanity. In vitro fertilisation is one of the most effective and frequently applied ART methods. The effectiveness IVF depends on the assessment and selection of gametes and embryo with the highest developmental potential. The subjective nature of morphological assessment of oocytes and embryos is still one of the main reasons for seeking effective and objective methods for assessing quality in automatic manner. The most promising methods to automatic classification of oocytes and embryos are based on image analysis aided by machine learning techniques. The special attention is paid on deep neural networks that can be used as classifiers solving the problem of automatic assessment of the oocytes/embryos.Methods: This paper deals with semantic segmentation of human oocyte images using deep neural networks in order to develop new version of the predefined neural networks. Deep semantic oocyte segmentation networks can be seen as medically oriented predefined networks understanding the content of the image. The research presented in the paper is focused on the performance comparison of different types of convolutional neural networks for semantic oocyte segmentation. In the case study, the merits and limitations of the selected deep neural networks are analysed.Results: 71 deep neural models were analysed. The best score was obtained for one of the variants of DeepLab-v3-ResNet-18 model, when the training accuracy (Acc) reached about 85% for training patterns and 79% for validation ones. The weighted intersection over union (wIoU) and global accuracy (gAcc) for test patterns were calculated, as well. The obtained values of these quality measures were 0,897 and 0.93, respectively.Conclusion: The obtained results prove that the proposed approach can be applied to create deep neural models for semantic oocyte segmentation with the high accuracy guaranteeing their usage as the predefined networks in other tasks. [ABSTRACT FROM AUTHOR]

Background: Three-dimensional (3D) printing has numerous applications and has gained much interest in the medical world. The constantly improving quality of 3D-printing applications has contributed to their increased use on patients. This paper summarizes the literature on surgical 3D-printing applications used on patients, with a focus on reported clinical and economic outcomes. Methods: Three major literature databases were screened for case series (more than three cases described in the same study) and trials of surgical applications of 3D printing in humans. Results: 227 surgical papers were analyzed and summarized using an evidence table. The papers described the use of 3D printing for surgical guides, anatomical models, and custom implants. 3D printing is used in multiple surgical domains, such as orthopedics, maxillofacial surgery, cranial surgery, and spinal surgery. In general, the advantages of 3D-printed parts are said to include reduced surgical time, improved medical outcome, and decreased radiation exposure. The costs of printing and additional scans generally increase the overall cost of the procedure. Conclusion: 3D printing is well integrated in surgical practice and research. Applications vary from anatomical models mainly intended for surgical planning to surgical guides and implants. Our research suggests that there are several advantages to 3D-printed applications, but that further research is needed to determine whether the increased intervention costs can be balanced with the observable advantages of this new technology. There is a need for a formal cost-effectiveness analysis. [ABSTRACT FROM AUTHOR]

Background: Optical coherence tomography (OCT) is widely used in ophthalmology for viewing the morphology of the retina, which is important for disease detection and assessing therapeutic effect. The diagnosis of retinal diseases is based primarily on the subjective analysis of OCT images by trained ophthalmologists. This paper describes an OCT images automatic analysis method for computer-aided disease diagnosis and it is a critical part of the eye fundus diagnosis. Methods: This study analyzed 300 OCT images acquired by Optovue Avanti RTVue XR (Optovue Corp., Fremont, CA). Firstly, the normal retinal reference model based on retinal boundaries was presented. Subsequently, two kinds of quantitative methods based on geometric features and morphological features were proposed. This paper put forward a retinal abnormal grading decision-making method which was used in actual analysis and evaluation of multiple OCT images. Results: This paper showed detailed analysis process by four retinal OCT images with different abnormal degrees. The final grading results verified that the analysis method can distinguish abnormal severity and lesion regions. This paper presented the simulation of the 150 test images, where the results of analysis of retinal status showed that the sensitivity was 0.94 and specificity was 0.92.The proposed method can speed up diagnostic process and objectively evaluate the retinal status. Conclusions: This paper aims on studies of retinal status automatic analysis method based on feature extraction and quantitative grading in OCT images. The proposed method can obtain the parameters and the features that are associated with retinal morphology. Quantitative analysis and evaluation of these features are combined with reference model which can realize the target image abnormal judgment and provide a reference for disease diagnosis. [ABSTRACT FROM AUTHOR]

While lumbar spinal fusion using rigid rods is a prevalent surgical technique, it can lead to complications such as adjacent segment disease (ASDis). Dynamic stabilization devices serve to maintain physiological spinal motion and alleviate painful stress, yet they are accompanied by a substantial incidence of construct failure and subsequent reoperation. Compared to traditional rigid devices, Isobar TTL semi-rigid stabilization devices demonstrate equivalent stiffness and effective stabilization capabilities. Furthermore, when contrasted with dynamic stabilization techniques, semi-rigid stabilization offers improved load distribution, a broader range of motion within the fixed segment, and reduced mechanical failure rates. This paper will review and evaluate the clinical and biomechanical performance of Isobar TTL semi-rigid stabilization devices. A literature search using the PubMed, EMBASE, CNKI, Wanfang, VIP, and Cochrane Library databases identified studies that met the eligibility criteria. Twenty-eight clinical studies and nine biomechanical studies were included in this systematic review. The VAS, the ODI, and Japanese Orthopedic Association scoring improved significantly in most studies. UCLA grading scale, Pfirrmann grading, and modified Pfirrmann grading of the upper adjacent segments improved significantly in most studies. The occurrence rate of ASD was low. In biomechanical studies, Isobar TTL demonstrated a superior load sharing distribution, a larger fixed segment range of motion, and reduced stress at the rod–screw/screw–bone interfaces compared with titanium rods. While findings from mechanical studies provided promising results, the clinical studies exhibited low methodological quality. As a result, the available evidence does not possess sufficient strength to substantiate superior outcomes with Isobar semi-rigid system in comparison to titanium rods. To establish more conclusive conclusions, further investigations incorporating improved protocols, larger sample sizes, and extended follow-up durations are warranted. [ABSTRACT FROM AUTHOR]

Background: Numerous sensing techniques have been investigated in an effort to monitor the main parameters influencing the residual limb/prosthesis interface, fundamental to the optimum design of prosthetic socket solutions. Sensing integration within sockets is notoriously complex and can cause user discomfort. A personalised prosthetic liner with embedded sensors could offer a solution. However, to allow for a functional and comfortable instrumented liner, highly customised designs are needed. The aim of this paper is to presents a novel approach to manufacture fully personalised liners using scanned three-dimensional image data of the patient's residual limb, combined with designs that allow for sensor integration. To demonstrate the feasibility of the proposed approach, a personalised liner with embedded temperature and humidity sensors was realised and tested on a transtibial amputee, presented here as a case study. Methods: The residual limb of a below knee amputee was first scanned and a three-dimensional digital image created. The output was used to produce a personalised prosthesis. The liner was manufactured using a cryogenic Computer Numeric Control (CNC) machining approach. This method enables fast, direct and precise manufacture of soft elastomer products. Twelve Hygrochron Data Loggers, able to measure both temperature and humidity, were embedded in specific liner locations, ensuring direct sensor-skin contact. The sensor locations were machined directly into the liner, during the manufacturing process. The sensors outputs were assessed on the below amputee who took part in the study, during resting (50 min) and walking activities (30 min). To better describe the relative thermal properties of new liner, the same tests were repeated with the amputee wearing his existing liner. Quantitative comparisons of the thermal properties of the new liner solution with that currently used in clinical practice are, therefore, reported. Results: The liner machining process took approximately 4 h. Fifteen minutes after donning the prosthesis, the skin temperature reached a plateau. Physical activity rapidly increased residuum skin temperatures, while cessation of activity caused a moderate decrease. Humidity increased throughout the observation period. In addition, the new liner showed better thermal properties with respect to the current liner solution (4% reduction in skin temperature). Conclusions: This work describes a personalised liner solution, with embedded temperature and humidity sensors, developed through an innovative approach. This new method allows for a range of sensors to be smoothly embedded into a liner, which is capable of measuring changes in intra-socket microclimate conditions, resulting in the design of advanced socket solutions personalised specifically for individual requirements. In future, this method will not only provide a personalised liner but will also enable dynamic assessment of how a residual limb behaves within the socket during daily activities. [ABSTRACT FROM AUTHOR]

Background: There are a number of clinical disorders that require mandibular reconstruction (MR). Novel three-dimensional (3D) printing technology enables reconstructions to be more accurate and beneficial to the patient. However, there is currently no evidence identifying which techniques are better suited for MR, based on the type of clinical disorder the patient has. In this study, we aim to compare 3D techniques with conventional techniques to identify how best to reconstruct the mandible based on the clinical cause that necessitates the reconstructive procedure: cancerous or benign tumours, clinical disorders, infection or disease and trauma or injury.Methods: PubMed, Scopus, Embase and Medline were searched to identify relevant papers that outline the clinical differences between 3D and conventional techniques in MR. Data were evaluated to provide a clear outline of suitable techniques for surgery.Results: 20 of 2749 papers met inclusion criteria. These papers were grouped based on the clinical causes that required MR into four categories: malignant or benign tumour resection; mandibular trauma/injury and other clinical disorders.Conclusions: The majority of researchers favoured 3D techniques in MR. However, due to a lack of standardised reporting in these studies it was not possible to determine which specific techniques were better for which clinical presentations. [ABSTRACT FROM AUTHOR]

Background: Neural stimulation is an important method used to activate or inhibit action potentials of the neuronal anatomical targets found in the brain, central nerve and peripheral nerve. The neural stimulator system produces biphasic pulses that deliver balanced charge into tissue from single or multichannel electrodes. The timing and amplitude of these biphasic pulses are precisely controlled by the neural stimulator software or imbedded algorithms. Amplitude mismatch between the anodic current and cathodic current of the biphasic pulse will cause permanently damage for the neural tissues. The main goal of our circuit and layout design is to implement a 16-channel biphasic current mode programmable neural stimulator with calibration to minimize the current mismatch caused by inherent complementary metal oxide semiconductor (CMOS) manufacturing processes.Methods: This paper presents a 16-channel constant current mode neural stimulator chip. Each channel consists of a 7-bit controllable current DAC used as sink and source current driver. To reduce the LSB quantization error and the current mismatch, an automatic calibration circuit and flow diagram is presented in this paper. There are two modes of operation of the stimulator chip-namely, stimulation mode and calibration mode. The chip also includes a digital interface used to control the stimulator parameters and calibration levels specific for each individual channel.Results: This stimulator Application Specific Integrated Circuit (ASIC) is designed and fabricated in a 0.18 μm High-Voltage CMOS technology that allows for ±20 V power supply. The full-scale stimulation current was designed to be at 1 mA per channel. The output current was shown to be constant throughout the timing cycles over a wide range of electrode load impedances. The calibration circuit was also designed to reduce the effect of CMOS process variation of the P-channel metal oxide semiconductor (PMOS) and N-channel metal oxide semiconductor (NMOS) devices that will result in charge delivery to have less than 0.13% error.Conclusions: A 16-channel integrated biphasic neural stimulator chip with calibration is presented in this paper. The stimulator circuit design was simulated and the chip layout was completed. The chip layout was verified using design rules check (DRC) and layout versus schematic (LVS) design check using computer aided design (CAD) software. The test results we presented show constant current stimulation with charge balance error within 0.13% least-significant-bit (LSB). This LSB error was consistent throughout a variety stimulation patterns and electrode load impedances. [ABSTRACT FROM AUTHOR]

Background: Systems aiding in selecting the correct settings for mechanical ventilation should visualize patient information at an appropriate level of complexity, so as to reduce information overload and to make reasoning behind advice transparent. Metaphor graphics have been applied to this effect, but these have largely been used to display diagnostic and physiologic information, rather than the clinical decision at hand. This paper describes how the conflicting goals of mechanical ventilation can be visualized and applied in making decisions. Data from previous studies are analyzed to assess whether visual patterns exist which may be of use to the clinical decision maker.Materials and Methods: The structure and screen visualizations of a commercial clinical decision support system (CDSS) are described, including the visualization of the conflicting goals of mechanical ventilation represented as a hexagon. Retrospective analysis is performed on 95 patients from 2 previous clinical studies applying the CDSS, to identify repeated patterns of hexagon symbols.Results: Visual patterns were identified describing optimal ventilation, over and under ventilation and pressure support, and over oxygenation, with these patterns identified for both control and support modes of mechanical ventilation. Numerous clinical examples are presented for these patterns illustrating their potential interpretation at the bedside.Conclusions: Visual patterns can be identified which describe the trade-offs required in mechanical ventilation. These may have potential to reduce information overload and help in simple and rapid identification of sub-optimal settings. [ABSTRACT FROM AUTHOR]

Randomized Controlled Trials (RCTs) are the best method to determine causal effects for treatments if they are well done and well reported. Good evidence about proposed treatments for obesity is needed, and Hsieh et al. (Biomed Eng Online 17:149, 2018) are to be commended for putting moxibustion to the test. However, careful evaluation of the paper reveals inconsistencies and apparent reporting errors, which raise doubts about conclusions from the study. [ABSTRACT FROM AUTHOR]

Gastric cancer is a deadly disease and gastric polyps are at high risk of becoming cancerous. Therefore, the timely detection of gastric polyp is of great importance which can reduce the incidence of gastric cancer effectively. At present, the object detection method based on deep learning is widely used in medical images. However, as the contrast between the background and the polyps is not strong in gastroscopic image, it is difficult to distinguish various sizes of polyps from the background. In this paper, to improve the detection performance metrics of endoscopic gastric polyps, we propose an improved attentional feature fusion module. First, in order to enhance the contrast between the background and the polyps, we propose an attention module that enables the network to make full use of the target location information, it can suppress the interference of the background information and highlight the effective features. Therefore, on the basis of accurate positioning, it can focus on detecting whether the current location is the gastric polyp or background. Then, it is combined with our feature fusion module to form a new attentional feature fusion model that can mitigate the effects caused by semantic differences in the processing of feature fusion, using multi-scale fusion information to obtain more accurate attention weights and improve the detection performance of polyps of different sizes. In this work, we conduct experiments on our own dataset of gastric polyps. Experimental results show that the proposed attentional feature fusion module is better than the common feature fusion module and can improve the situation where polyps are missed or misdetected. [ABSTRACT FROM AUTHOR]

Interest in home-based stroke rehabilitation mechatronics, which includes both robots and sensor mechanisms, has increased over the past 12 years. The COVID-19 pandemic has exacerbated the existing lack of access to rehabilitation for stroke survivors post-discharge. Home-based stroke rehabilitation devices could improve access to rehabilitation for stroke survivors, but the home environment presents unique challenges compared to clinics. The present study undertakes a scoping review of designs for at-home upper limb stroke rehabilitation mechatronic devices to identify important design principles and areas for improvement. Online databases were used to identify papers published 2010–2021 describing novel rehabilitation device designs, from which 59 publications were selected describing 38 unique designs. The devices were categorized and listed according to their target anatomy, possible therapy tasks, structure, and features. Twenty-two devices targeted proximal (shoulder and elbow) anatomy, 13 targeted distal (wrist and hand) anatomy, and three targeted the whole arm and hand. Devices with a greater number of actuators in the design were more expensive, with a small number of devices using a mix of actuated and unactuated degrees of freedom to target more complex anatomy while reducing the cost. Twenty-six of the device designs did not specify their target users' function or impairment, nor did they specify a target therapy activity, task, or exercise. Twenty-three of the devices were capable of reaching tasks, 6 of which included grasping capabilities. Compliant structures were the most common approach of including safety features in the design. Only three devices were designed to detect compensation, or undesirable posture, during therapy activities. Six of the 38 device designs mention consulting stakeholders during the design process, only two of which consulted patients specifically. Without stakeholder involvement, these designs risk being disconnected from user needs and rehabilitation best practices. Devices that combine actuated and unactuated degrees of freedom allow a greater variety and complexity of tasks while not significantly increasing their cost. Future home-based upper limb stroke rehabilitation mechatronic designs should provide information on patient posture during task execution, design with specific patient capabilities and needs in mind, and clearly link the features of the design to users' needs. [ABSTRACT FROM AUTHOR]

Background: Current research related to electroencephalogram (EEG)-based driver's emergency braking intention detection focuses on recognizing emergency braking from normal driving, with little attention to differentiating emergency braking from normal braking. Moreover, the classification algorithms used are mainly traditional machine learning methods, and the inputs to the algorithms are manually extracted features. Methods: To this end, a novel EEG-based driver's emergency braking intention detection strategy is proposed in this paper. The experiment was conducted on a simulated driving platform with three different scenarios: normal driving, normal braking and emergency braking. We compared and analyzed the EEG feature maps of the two braking modes, and explored the use of traditional methods, Riemannian geometry-based methods, and deep learning-based methods to predict the emergency braking intention, all using the raw EEG signals rather than manually extracted features as input. Results: We recruited 10 subjects for the experiment and used the area under the receiver operating characteristic curve (AUC) and F1 score as evaluation metrics. The results showed that both the Riemannian geometry-based method and the deep learning-based method outperform the traditional method. At 200 ms before the start of real braking, the AUC and F1 score of the deep learning-based EEGNet algorithm were 0.94 and 0.65 for emergency braking vs. normal driving, and 0.91 and 0.85 for emergency braking vs. normal braking, respectively. The EEG feature maps also showed a significant difference between emergency braking and normal braking. Overall, based on EEG signals, it was feasible to detect emergency braking from normal driving and normal braking. Conclusions: The study provides a user-centered framework for human–vehicle co-driving. If the driver's intention to brake in an emergency can be accurately identified, the vehicle's automatic braking system can be activated hundreds of milliseconds earlier than the driver's real braking action, potentially avoiding some serious collisions. [ABSTRACT FROM AUTHOR]

Background: Embolization is a common treatment method for tumor-targeting, anti-organ hyper-function, and hemostasis. However, the injection of embolic agents largely depends on the experiences of doctors, and doctors need to work in an X-ray environment that hurts their health. Even for a well-trained doctor, complications such as ectopic embolism caused by excessive embolic agents are always inevitable. Results: This paper established a flow control curve model for embolic injection based on local arterial pressure. The end-vessel network was simplified as a porous media. The hemodynamic changes at different injection velocities and embolization degrees were simulated and analyzed. Sponge, a typical porous medium, was used to simulate the blocking and accumulation of embolic agents by capillary networks in the in vitro experimental platform. Conclusions: The simulation and experimental results show that the local arterial pressure is closely related to the critical injection velocity of the embolic agent reflux at a certain degree of embolization. The feasibility of this method for an automatic embolic injection system is discussed. It is concluded that the model of the flow control curve of embolic injection can effectively reduce the risk of ectopic embolism and shorten the time of embolic injection. The clinical application of this model is of great value in reducing radiation exposure and improving the success rate of interventional embolization. [ABSTRACT FROM AUTHOR]

Tracking points in ultrasound (US) videos can be especially useful to characterize tissues in motion. Tracking algorithms that analyze successive video frames, such as variations of Optical Flow and Lucas–Kanade (LK), exploit frame-to-frame temporal information to track regions of interest. In contrast, convolutional neural-network (CNN) models process each video frame independently of neighboring frames. In this paper, we show that frame-to-frame trackers accumulate error over time. We propose three interpolation-like methods to combat error accumulation and show that all three methods reduce tracking errors in frame-to-frame trackers. On the neural-network end, we show that a CNN-based tracker, DeepLabCut (DLC), outperforms all four frame-to-frame trackers when tracking tissues in motion. DLC is more accurate than the frame-to-frame trackers and less sensitive to variations in types of tissue movement. The only caveat found with DLC comes from its non-temporal tracking strategy, leading to jitter between consecutive frames. Overall, when tracking points in videos of moving tissue, we recommend using DLC when prioritizing accuracy and robustness across movements in videos, and using LK with the proposed error-correction methods for small movements when tracking jitter is unacceptable. [ABSTRACT FROM AUTHOR]

Recent advances in deep learning have shown great potential for the automatic generation of medical imaging reports. Deep learning techniques, inspired by image captioning, have made significant progress in the field of diagnostic report generation. This paper provides a comprehensive overview of recent research efforts in deep learning-based medical imaging report generation and proposes future directions in this field. First, we summarize and analyze the data set, architecture, application, and evaluation of deep learning-based medical imaging report generation. Specially, we survey the deep learning architectures used in diagnostic report generation, including hierarchical RNN-based frameworks, attention-based frameworks, and reinforcement learning-based frameworks. In addition, we identify potential challenges and suggest future research directions to support clinical applications and decision-making using medical imaging report generation systems. [ABSTRACT FROM AUTHOR]

Background: Motor imagery is a cognitive process of imagining a performance of a motor task without employing the actual movement of muscles. It is often used in rehabilitation and utilized in assistive technologies to control a brain–computer interface (BCI). This paper provides a comparison of different time–frequency representations (TFR) and their Rényi and Shannon entropies for sensorimotor rhythm (SMR) based motor imagery control signals in electroencephalographic (EEG) data. The motor imagery task was guided by visual guidance, visual and vibrotactile (somatosensory) guidance or visual cue only. Results: When using TFR-based entropy features as an input for classification of different interaction intentions, higher accuracies were achieved (up to 99.87%) in comparison to regular time-series amplitude features (for which accuracy was up to 85.91%), which is an increase when compared to existing methods. In particular, the highest accuracy was achieved for the classification of the motor imagery versus the baseline (rest state) when using Shannon entropy with Reassigned Pseudo Wigner–Ville time–frequency representation. Conclusions: Our findings suggest that the quantity of useful classifiable motor imagery information (entropy output) changes during the period of motor imagery in comparison to baseline period; as a result, there is an increase in the accuracy and F1 score of classification when using entropy features in comparison to the accuracy and the F1 of classification when using amplitude features, hence, it is manifested as an improvement of the ability to detect motor imagery. [ABSTRACT FROM AUTHOR]

Background Wireless energy transfer system is an effective way to solve the visual prosthesis energy supply problems, theoretical modeling of the system is the prerequisite to do optimal energy transfer system design. Methods On the basis of the ideal model of the wireless energy transfer system, according to visual prosthesis application condition, the system modeling is optimized. During the optimal modeling, taking planar spiral coils as the coupling devices between energy transmitter and receiver, the effect of the parasitic capacitance of the transfer coil is considered, and especially the concept of biological capacitance is proposed to consider the influence of biological tissue on the energy transfer efficiency, resulting in the optimal modeling's more accuracy for the actual application. Results The simulation data of the optimal model in this paper is compared with that of the previous ideal model, the results show that under high frequency condition, the parasitic capacitance of inductance and biological capacitance considered in the optimal model could have great impact on the wireless energy transfer system. The further comparison with the experimental data verifies the validity and accuracy of the optimal model proposed in this paper. Conclusions The optimal model proposed in this paper has a higher theoretical guiding significance for the wireless energy transfer system's further research, and provide a more precise model reference for solving the power supply problem in visual prosthesis clinical application. [ABSTRACT FROM AUTHOR]

Background: Amyloid β-protein (Aβ) plaque deposition is an important prevention and treatment target for Alzheimer's disease (AD). As a noninvasive, nonradioactive and highly cost-effective clinical imaging method, magnetic resonance imaging (MRI) is the perfect imaging technology for the clinical diagnosis of AD, but it cannot display the plaque deposition directly. This paper resolves this problem based on pixel feature selection algorithms at the image level. Methods and Results: Firstly, the brain region was segmented from mouse model brain MR images. Secondly, the pixels in the segmented brain region were extracted as a feature vector (features). Thirdly, feature selection was conducted on the extracted features, and the optimal feature subset was obtained. Fourthly, the various optimal feature subsets were obtained by repeating the same processing above. Fifthly, based on the optimal feature subsets, the final optimal feature subset was obtained by voting mechanism. Finally, using the final optimal selected features, the corresponding pixels on the MR images could be found and marked to show the information about Aβ plaque deposition. The MR images and brain histological image slices of twenty-two model mice were used in the experiments. Four feature selection algorithms were used on the MR images and six kinds of classification experiments are conducted, thereby choosing a pixel feature selection algorithm for further study. The experimental results showed that by using the pixel features selected by the algorithms in this paper, the best classification accuracy between early AD and control slides could be as high as 80 %. The selected and marked MR pixels could show information of Aβ plaque deposition without missing most of the Aβ plaque deposition compared with brain histological slice images. The hit rate is over than 90 %. Conclusions: According to the experimental results, the proposed detection algorithm of the Aβ plaque deposition based on MR pixel feature selection algorithm is effective. The proposed algorithm can detect the information of the Aβ plaque deposition on MR images and the information can be useful for improving the classification accuracy as assistant MR biomarker. Besides, these findings firstly show the feasibility of detection of the Aβ plaque deposition on MR images and provide reference method for interested relevant researchers in public. [ABSTRACT FROM AUTHOR]

Background and Objective: Moderate exercise contributes to good health. However, excessive exercise may lead to cardiac fatigue, myocardial damage and even exercise sudden death. Monitoring the heart health has important implication to prevent exercise sudden death. Diagnosis methods such as electrocardiogram, echocardiogram, blood pressure and histological analysis have shown that arrhythmia and left ventricular fibrosis are early warning symptoms of exercise sudden death. Heart sounds (HS) can reflect the changes of cardiac valve, cardiac blood flow and myocardial function. Deep learning has drawn wide attention because of its ability to recognize disease. Therefore, a deep learning method combined with HS was proposed to predict exercise sudden death in New Zealand rabbits. The objective is to develop a method to predict exercise sudden death in New Zealand rabbits.Methods: This paper proposed a method to predict exercise sudden death in New Zealand rabbits based on convolutional neural network (CNN) and gated recurrent unit (GRU). The weight-bearing exhaustive swimming experiment was conducted to obtain the HS of exercise sudden death and surviving New Zealand rabbits (n = 11/10) at four different time points. Then, the improved Viola integral method and double threshold method were employed to segment HS signals. The segmented HS frames at different time points were taken as the input of a combined CNN and GRU called CNN-GRU network to complete the prediction of exercise sudden death.Results: In order to evaluate the performance of proposed network, CNN and GRU were used for comparison. When the fourth time point segmented HS frames were taken as input, the result shows that the proposed network has better performance with an accuracy of 89.57%, a sensitivity of 89.38% and a specificity of 92.20%. In addition, the segmented HS frames at different time points were input into CNN-GRU network, and the result shows that with the progress of the experiment, the prediction accuracy of exercise sudden death in New Zealand rabbits increased from 50.98 to 89.57%.Conclusion: The proposed network shows good performance in classifying HS, which proves the feasibility of deep learning in exploring exercise sudden death. Further, it may have important implications in helping humans explore exercise sudden death. [ABSTRACT FROM AUTHOR]

Background: In order to use the microwave to measure the dielectric constant of the human body and improve the measurement resolution, a small near-field probe working at 915 MHz is designed in this paper.Method: Based on the electric small loop antenna model loaded by the spiral resonator (SR), a small near-field probe was designed. The probe model is designed and optimized by the HFSS (high frequency structure simulator) software. The human tissues were tested by the manufactured probe and the relationship between the S11 parameters of the probe and the human tissues was analyzed.Results and Conclusions: A probe with small size was designed and fabricated, with the overall size of 10.0 mm × 12.0 mm × 0.8 mm. The probe has a good performance with a 30.7 dB return loss, a 20 MHz bandwidth at the resonance point, and a distance resolution of 10 mm. Due to the small size and good resolution of the probe, it can be used in the measurement of human tissues. [ABSTRACT FROM AUTHOR]

Background: Pericardial access is necessary for the application of epicardial cardiac therapies including ablation catheters, pacing and defibrillation leads, and left atrial appendage closure systems. Pericardial access under fluoroscopic guidance is difficult in patients without pericardial effusions and may result in coronary artery damage, ventricular injury, or perforation with potentially life-threatening pericardial bleeding in up to 10% of cases. There is a clinical need for a pericardial access technique to safely deliver epicardial cardiac therapies. Methods: In this paper, we describe the design and evaluation of a novel videoscope and tool kit to percutaneously access the pericardial space under direct visualization. Imaging is performed by a micro-CMOS camera with an automatic gain adjustment software to prevent image saturation. Imaging quality is quantified using known optical targets, while tool performance is evaluated in pediatric insufflation and pericardial access simulators. Device safety and efficacy is demonstrated by infant porcine preclinical studies (N = 6). Results: The videoscope has a resolution of 400 × 400 pixels, imaging rate of 30 frames per second, and fits within the lumen of a 14G needle. The tool can resolve features smaller than 39.4 µm, achieves a magnification of 24x, and has a maximum of 3.5% distortion within the field of view. Successful pericardial access was achieved in pediatric simulators and acute in vivo animal studies. During in vivo testing, it took the electrophysiologist an average of 66.83 ± 32.86 s to insert the pericardial access tool into the thoracic space and visualize the heart. After visualizing the heart, it took an average of 136.67 ± 80.63 s to access the pericardial space under direct visualization. The total time to pericardial access measured from needle insertion was 6.7 × quicker than pericardial access using alternative direct visualization techniques. There was no incidence of ventricular perforation. Conclusions: Percutaneous pericardial access under direct visualization is a promising technique to access the pericardial space without complications in simulated and in vivo animal models. [ABSTRACT FROM AUTHOR]