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.

BACKGROUND: Motor impairment is a common consequence of stroke causing difficulty in independent movement. The first month of post-stroke rehabilitation is the most effective period for recovery. Movement imagination, known as motor imagery, in combination with virtual reality may provide a way for stroke patients with severe motor disabilities to begin rehabilitation. METHODS: The aim of this study is to verify whether motor imagery and virtual reality help to activate stroke patients' motor cortex. 16 acute/subacute (< 6 months) stroke patients participated in this study. All participants performed motor imagery of basketball shooting which involved the following tasks: listening to audio instruction only, watching a basketball shooting animation in 3D with audio, and also performing motor imagery afterwards. Electroencephalogram (EEG) was recorded for analysis of motor-related features of the brain such as power spectral analysis in the [Formula: see text] and [Formula: see text] frequency bands and spectral entropy. 18 EEG channels over the motor cortex were used for all stroke patients. RESULTS: All results are normalised relative to all tasks for each participant. The power spectral densities peak near the [Formula: see text] band for all participants and also the [Formula: see text] band for some participants. Tasks with instructions during motor imagery generally show greater power spectral peaks. The p-values of the Wilcoxon signed-rank test for band power comparison from the 18 EEG channels between different pairs of tasks show a 0.01 significance of rejecting the band powers being the same for most tasks done by stroke subjects. The motor cortex of most stroke patients is more active when virtual reality is involved during motor imagery as indicated by their respective scalp maps of band power and spectral entropy. CONCLUSION: The resulting activation of stroke patient's motor cortices in this study reveals evidence that it is induced by imagination of movement and virtual reality supports motor imagery. The framework of the current study also provides an efficient way to investigate motor imagery and virtual reality during post-stroke rehabilitation.

BACKGROUND: In the future, extended reality technology will be widely used. People will be led to utilize virtual reality (VR) and augmented reality (AR) technologies in their daily lives, hobbies, numerous types of entertainment, and employment. Medical augmented reality has evolved with applications ranging from medical education to picture-guided surgery. Moreover, a bulk of research is focused on clinical applications, with the majority of research devoted to surgery or intervention, followed by rehabilitation and treatment applications. Numerous studies have also looked into the use of augmented reality in medical education and training. METHODS: Using the databases Semantic Scholar, Web of Science, Scopus, IEEE Xplore, and ScienceDirect, a scoping review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. To find other articles, a manual search was also carried out in Google Scholar. This study presents studies carried out over the previous 14 years (from 2009 to 2023) in detail. We classify this area of study into the following categories: (1) AR and VR in surgery, which is presented in the following subsections: subsection A: MR in neurosurgery; subsection B: spine surgery; subsection C: oral and maxillofacial surgery; and subsection D: AR-enhanced human-robot interaction; (2) AR and VR in medical education presented in the following subsections; subsection A: medical training; subsection B: schools and curriculum; subsection C: XR in Biomedicine; (3) AR and VR for rehabilitation presented in the following subsections; subsection A: stroke rehabilitation during COVID-19; subsection B: cancer and VR, and (4) Millimeter-wave and MIMO systems for AR and VR. RESULTS: In total, 77 publications were selected based on the inclusion criteria. Four distinct AR and/or VR applications groups could be differentiated: AR and VR in surgery (N = 21), VR and AR in Medical Education (N = 30), AR and VR for Rehabilitation (N = 15), and Millimeter-Wave and MIMO Systems for AR and VR (N = 7), where N is number of cited studies. We found that the majority of research is devoted to medical training and education, with surgical or interventional applications coming in second. The research is mostly focused on rehabilitation, therapy, and clinical applications. Moreover, the application of XR in MIMO has been the subject of numerous research. CONCLUSION: Examples of these diverse fields of applications are displayed in this review as follows: (1) augmented reality and virtual reality in surgery; (2) augmented reality and virtual reality in medical education; (3) augmented reality and virtual reality for rehabilitation; and (4) millimeter-wave and MIMO systems for augmented reality and virtual reality.

BACKGROUND: Gait is a complex, whole-body movement that requires the coordinated action of multiple joints and muscles of our musculoskeletal system. In the context of Duchenne muscular dystrophy (DMD), a disease characterized by progressive muscle weakness and joint contractures, previous studies have generally assumed symmetrical behavior of the lower limbs during gait. However, such a symmetric gait pattern of DMD was controversial. One aspect of this is criticized, because most of these studies have primarily focused on univariate variables, rather than on the coordination of multiple body segments and even less investigate gait symmetry under a motor synergy of view. METHODS: We investigated the gait pattern of 20 patients with DMD, compared to 18 typical developing children (TD) through 3D Gait Analysis. Kinematic and muscle synergies were extracted with principal component analysis (PCA) and non-negative matrix factorization (NNMF), respectively. The synergies extracted from the left and right sides were compared with each other to obtain a symmetry value. In addition, bilateral spatiotemporal variables of gait, such as stride length, percentage of stance and swing phase, step length, and percentage of double support phase, were used for calculating the symmetry index (SI) to evaluate gait symmetry as well. RESULTS: Compared with the TD group, the DMD group walked with decreased gait velocity (both p < 0.01), stride length (both p < 0.01), and step length (both p < 0.001). No significant difference was found between groups in SI of all spatiotemporal parameters extracted between the left and right lower limbs. In addition, the DMD group exhibited lower kinematic synergy symmetry values compared to the TD group (p < 0.001), while no such significant group difference was observed in symmetry values of muscle synergy. CONCLUSIONS: The findings of this study suggest that DMD influences, to some extent, the symmetry of synergistic movement of multiple segments of lower limbs, and thus kinematic synergy appears capable of discriminating gait asymmetry in children with DMD when conventional spatiotemporal parameters are unchanged.

BACKGROUND: Colorectal cancer is one of the most serious malignant tumors, and lymph node metastasis (LNM) from colorectal cancer is a major factor for patient management and prognosis. Accurate image detection of LNM is an important task to help clinicians diagnose cancer. Recently, the U-Net architecture based on convolutional neural networks (CNNs) has been widely used to segment image to accomplish more precise cancer diagnosis. However, the accurate segmentation of important regions with high diagnostic value is still a great challenge due to the insufficient capability of CNN and codec structure in aggregating the detailed and non-local contextual information. In this work, we propose a high performance and low computation solution. METHODS: Inspired by the working principle of Fovea in visual neuroscience, a novel network framework based on U-Net for cancer segmentation named Fovea-UNet is proposed to adaptively adjust the resolution according to the importance-aware of information and selectively focuses on the region most relevant to colorectal LNM. Specifically, we design an effective adaptively optimized pooling operation called Fovea Pooling (FP), which dynamically aggregate the detailed and non-local contextual information according to the pixel-level feature importance. In addition, the improved lightweight backbone network based on GhostNet is adopted to reduce the computational cost caused by FP. RESULTS: Experimental results show that our proposed framework can achieve higher performance than other state-of-the-art segmentation networks with 79.38% IoU, 88.51% DSC, 92.82% sensitivity and 84.57% precision on the LNM dataset, and the parameter amount is reduced to 23.23 MB. CONCLUSIONS: The proposed framework can provide a valid tool for cancer diagnosis, especially for LNM of colorectal cancer.

BACKGROUND: Bone tissue engineering, based on three-dimensional (3D) printing technology, has emerged as a promising approach to treat bone defects using scaffolds. The objective of this study was to investigate the influence of porosity and internal structure on the mechanical properties of scaffolds. METHODS: We fabricated composite scaffolds (which aimed to replicate trabecular bone) from polycaprolactone (PCL) reinforced with 30% (wt.) nano-hydroxyapatite (nHAp) by extrusion printing. Scaffolds with various porosities were designed and fabricated with and without an interlayer offset, termed as staggered and lattice structure, respectively. Mechanical compressive testing was performed to determine scaffold elastic modulus and yield strength. Linear regression was used to evaluate mechanical properties as a function of scaffold porosity. RESULTS: Different relationships between mechanical properties and porosities were noted for the staggered and lattice structures. For elastic moduli, the two relationships intersected (porosity = 55%) such that the lattice structure exhibited higher moduli with porosity values greater than the intersection point; vice versa for the staggered structure. The lattice structure exhibited higher yield strength at all porosities. Mechanical testing results also indicated elastic moduli and yield strength properties comparable to trabecular bone (elastic moduli: 14-165 MPa; yield strength: 0.9-10 MPa). CONCLUSIONS: Taken together, this study demonstrates that scaffolds printed from PCL/30% (wt.) nHAp with lattice and staggered structure offer promise for treating trabecular bone defects. This study identified the effect of porosity and internal structure on scaffold mechanical properties and provided suggestions for developing scaffolds with mechanical properties for substituting trabecular bone.

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.

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.

BACKGROUND: Osteoporosis is a significant health problem in the skeletal system, associated with bone tissue changes and its strength. Machine Learning (ML), on the other hand, has been accompanied by improvements in recent years and has been in the spotlight. This study is designed to investigate the Diagnostic Test Accuracy (DTA) of ML to detect osteoporosis through the hip dual-energy X-ray absorptiometry (DXA) images. METHODS: The ISI Web of Science, PubMed, Scopus, Cochrane Library, IEEE Xplore Digital Library, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched until June 2023 for studies that tested the diagnostic precision of ML model-assisted for predicting an osteoporosis diagnosis. RESULTS: The pooled sensitivity of univariate analysis of seven studies was 0.844 (95% CI 0.791 to 0.885, I2 = 94% for 7 studies). The pooled specificity of univariate analysis was 0.781 (95% CI 0.732 to 0.824, I2 = 98% for 7 studies). The pooled diagnostic odds ratio (DOR) was 18.91 (95% CI 14.22 to 25.14, I2 = 93% for 7 studies). The pooled mean positive likelihood ratio (LR+) and the negative likelihood ratio (LR-) were 3.7 and 0.22, respectively. Also, the summary receiver operating characteristics (sROC) of the bivariate model has an AUC of 0.878. CONCLUSION: Osteoporosis can be diagnosed by ML with acceptable accuracy, and hip fracture prediction was improved via training in an Architecture Learning Network (ALN).

BACKGROUND: It has been hypothesized that low access to healthy and nutritious food increases health disparities. Low-accessibility areas, called food deserts, are particularly commonplace in lower-income neighborhoods. The metrics for measuring the food environment's health, called food desert indices, are primarily based on decadal census data, limiting their frequency and geographical resolution to that of the census. We aimed to create a food desert index with finer geographic resolution than census data and better responsiveness to environmental changes. MATERIALS AND METHODS: We augmented decadal census data with real-time data from platforms such as Yelp and Google Maps and crowd-sourced answers to questionnaires by the Amazon Mechanical Turks to create a real-time, context-aware, and geographically refined food desert index. Finally, we used this refined index in a concept application that suggests alternative routes with similar ETAs between a source and destination in the Atlanta metropolitan area as an intervention to expose a traveler to better food environments. RESULTS: We made 139,000 pull requests to Yelp, analyzing 15,000 unique food retailers in the metro Atlanta area. In addition, we performed 248,000 walking and driving route analyses on these retailers using Google Maps' API. As a result, we discovered that the metro Atlanta food environment creates a strong bias towards eating out rather than preparing a meal at home when access to vehicles is limited. Contrary to the food desert index that we started with, which changed values only at neighborhood boundaries, the food desert index that we built on top of it captured the changing exposure of a subject as they walked or drove through the city. This model was also sensitive to the changes in the environment that occurred after the census data was collected. CONCLUSIONS: Research on the environmental components of health disparities is flourishing. New machine learning models have the potential to augment various information sources and create fine-tuned models of the environment. This opens the way to better understanding the environment and its effects on health and suggesting better interventions.

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.