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Recently commercialized powered prosthetic arm systems hold great potential in restoring function for people with upper-limb loss. However, effective use of such devices remains limited by conventional (direct) control methods, which rely on electromyographic signals produced from a limited set of muscles. Targeted Muscle Reinnervation (TMR) is a nerve transfer procedure that creates additional recording sites for myoelectric prosthesis control. The effects of TMR may be enhanced when paired with pattern recognition technology. We sought to compare pattern recognition and direct control in eight transhumeral amputees who had TMR in a balanced randomized cross-over study. Subjects performed a 6–8 week home trial using direct and pattern recognition control with a custom prostheses made from commercially available parts. Subjects showed statistically better performance in the Southampton Hand Assessment Procedure (p = 0.04) and the Clothespin relocation task (p = 0.02). Notably, these tests required movements along 3 degrees of freedom. Seven of 8 subjects preferred pattern recognition control over direct control. This study was the first home trial large enough to establish clinical and statistical significance in comparing pattern recognition with direct control. Results demonstrate that pattern recognition is a viable option and has functional advantages over direct control.

Background Although electromyogram (EMG) pattern recognition (PR) for multifunctional upper limb prosthesis control has been reported for decades, the clinical benefits have rarely been examined. The study purposes were to: 1) compare self-report and performance outcomes of a transradial amputee immediately after training and one week after training of direct myoelectric control and EMG pattern recognition (PR) for a two-degree-of-freedom (DOF) prosthesis, and 2) examine the change in outcomes one week after pattern recognition training and the rate of skill acquisition in two subjects with transradial amputations. Methods In this cross-over study, participants were randomized to receive either PR control or direct control (DC) training of a 2 DOF myoelectric prosthesis first. Participants were 2 persons with traumatic transradial (TR) amputations who were 1 DOF myoelectric users. Outcomes, including measures of dexterity with and without cognitive load, activity performance, self-reported function, and prosthetic satisfaction were administered immediately and 1 week after training. Speed of skill acquisition was assessed hourly. One subject completed training under both PR control and DC conditions. Both subjects completed PR training and testing. Outcomes of test metrics were analyzed descriptively. Results Comparison of the two control strategies in one subject who completed training in both conditions showed better scores in 2 (18%) dexterity measures, 1 (50%) dexterity measure with cognitive load, and 1 (50%) self-report functional measure using DC, as compared to PR. Scores of all other metrics were comparable. Both subjects showed decline in dexterity after training. Findings related to rate of skill acquisition varied considerably by subject. Conclusions Outcomes of PR and DC for operating a 2-DOF prosthesis in a single subject cross-over study were similar for 74% of metrics, and favored DC in 26% of metrics. The two subjects who completed PR training showed decline in dexterity one week after training ended. Findings related to rate of skill acquisition varied considerably by subject. This study, despite its small sample size, highlights a need for additional research quantifying the functional and clinical benefits of PR control for upper limb prostheses.

Sensorimotor impairments, resulting from conditions like stroke and amputations, can profoundly impact an individual’s functional abilities and overall quality of life. Assistive and rehabilitation devices such as prostheses, exo-skeletons, and serious gaming in virtual environments can help to restore some degree of function and alleviate pain after sensorimotor impairments. Myoelectric pattern recognition (MPR) has gained popularity in the past decades as it provides superior control over said devices, and therefore efforts to facilitate and improve performance in MPR can result in better rehabilitation outcomes. One possibility to enhance MPR is to employ transcranial direct current stimulation (tDCS) to facilitate motor learning. Twelve healthy able-bodied individuals participated in this crossover study to determine the effect of tDCS on MPR performance. Baseline training was followed by two sessions of either sham or anodal tDCS using the dominant and non-dominant arms. Assignments were randomized, and the MPR task consisted of 11 different hand/wrist movements, including rest or no movement. Surface electrodes were used to record EMG and the MPR open-source platform, BioPatRec, was used for decoding motor volition in real-time. The motion test was used to evaluate performance. We hypothesized that using anodal tDCS to increase the excitability of the primary motor cortex associated with non-dominant side in able-bodied individuals, will improve motor learning and thus MPR performance. Overall, we found that tDCS enhanced MPR performance, particularly in the non-dominant side. We were able to reject the null hypothesis and improvements in the motion test’s completion rate during tDCS (28% change, p-value: 0.023) indicate its potential as an adjunctive tool to enhance MPR and motor learning. tDCS appears promising as a tool to enhance the learning phase of using assistive devices using MPR, such as myoelectric prostheses.

Background Pattern recognition technology allows for more intuitive control of myoelectric prostheses. However, the need to collect electromyographic data to initially train the pattern recognition system, and to re-train it during prosthesis use, adds complexity that can make using such a system difficult. Although experienced clinicians may be able to guide users to ensure successful data collection methods, they may not always be available when a user needs to (re)train their device. Methods Here we present an engaging and interactive virtual reality environment for optimal training of a myoelectric controller. Using this tool, we evaluated the importance of training a classifier actively (i.e., moving the residual limb during data collection) compared to passively (i.e., maintaining the limb in a single, neutral orientation), and whether computational adaptation through serious gaming can improve performance. Results We found that actively trained classifiers performed significantly better than passively trained classifiers for non-amputees ( P  < 0.05). Furthermore, collecting data passively with minimal instruction, paired with computational adaptation in a virtual environment, significantly improved real-time performance of myoelectric controllers. Conclusion These results further support previous work which suggested active movements during data collection can improve pattern recognition systems. Furthermore, adaptation within a virtual guided serious game environment can improve real-time performance of myoelectric controllers.

The Hough transform has been widely used in image analysis and digital image processing due to its capability of transforming image space detection to parameter space accumulation. In this paper, we propose a novel Angle-Aided Circle Detection (AACD) algorithm based on the randomized Hough transform to reduce the computational complexity of the traditional Randomized Hough transform. The algorithm ameliorates the sampling method of random sampling points to reduce the invalid accumulation by using region proposals method, and thus significantly reduces the amount of computation. Compared with the traditional Hough transform, the proposed algorithm is robust and suitable for multiple circles detection under complex conditions with strong anti-interference capacity. Moreover, the algorithm has been successfully applied to the welding spot detection on automobile body, and the experimental results verifies the validity and accuracy of the algorithm.

Current methods for thresholding functional magnetic resonance imaging (fMRI) maps are based on the well-known hypothesis-test framework, optimal for addressing novel theoretical claims. However, these methods as typically practiced have a strong bias toward protecting the null hypothesis, and thus may not provide an optimal balance between specificity and sensitivity in forming activation maps for surgical planning. Maps based on hypothesis-test thresholds are also highly sensitive to sample size and signal-to-noise ratio, whereas many clinical applications require methods that are robust to these effects. We propose a new thresholding method, optimized for surgical planning, based on normalized amplitude thresholding. We show that this method produces activation maps that are more reproducible and more predictive of postoperative cognitive outcome than maps produced with current standard thresholding methods. •Compared p-value based thresholding of fMRI data to two alternative methods•Compared methods on reproducibility and accuracy at predicting postsurgical outcome•Normalized signal thresholding was more reliable and predictive than other methods

Introduction Haematoma and oedema size determines outcome after intracerebral haemorrhage (ICH), with each added 10 % volume increasing mortality by 5 %. We assessed the reliability of semi-automated computed tomography planimetry using Analyze and Osirix softwares. Methods We randomly selected 100 scans from 1329 ICH patients from two centres. We used Hounsfield Unit thresholds of 5–33 for oedema and 44–100 for ICH. Three raters segmented all scans using both softwares and 20 scans repeated for intra-rater reliability and segmentation timing. Volumes reported by Analyze and Osirix were compared to volume estimates calculated using the best practice method, taking effective individual slice thickness, i.e. voxel depth, into account. Results There was excellent overall inter-rater, intra-rater and inter-software reliability, all intraclass correlation coefficients >0.918. Analyze and Osirix produced similar haematoma (mean difference: Analyze − Osirix = 1.5 ± 5.2 mL, 6 %, p  ≤ 0.001) and oedema volumes (−0.6 ± 12.6 mL, −3 %, p  = 0.377). Compared to a best practice approach to volume calculation, the automated haematoma volume output was 2.6 mL (−11 %) too small with Analyze and 4.0 mL (−18 %) too small with Osirix, whilst the oedema volumes were 2.5 mL (−12 %) and 5.5 mL (−25 %) too small, correspondingly. In scans with variable slice thickness, the volume underestimations were larger, −29%/−36 % for ICH and −29 %/−41 % for oedema. Mean segmentation times were 6:53 ± 4:02 min with Analyze and 9:06 ± 5:24 min with Osirix ( p  < 0.001). Conclusion Our results demonstrate that the method used to determine voxel depth can influence the final volume output markedly. Results of clinical and collaborative studies need to be considered in the context of these methodological differences.

Monitors evaluating the electroencephalogram (EEG) to determine depth of anaesthesia use spectral analysis approaches for analysis windows up to 61.5 s as well as additional smoothing algorithms. Stationary EEG is required to reliably apply the index algorithms. Because of rapid physiological changes, artefacts, etc., the EEG may not always fulfil this requirement. EEG analysis using permutation entropy (PeEn) may overcome this issue, since PeEn can also be applied to practically nonstationary EEG. One objective was to determine the duration of EEG sequences that can be considered stationary at different anaesthetic levels. The second, more important objective was to test the reliability of PeEn to reflect the anaesthetic levels for short EEG segments. EEG was recorded from 15 volunteers undergoing sevoflurane and propofol anaesthesia at different anaesthetic levels and for each group 10 data sets were included. EEG stationarity was evaluated for EEG sample lengths from 4 to 116 s for each level. PeEn was calculated for these sequences using different parameter settings and analysis windows from 2 to 60 s. During wakefulness EEG can only be considered stationary for sequences up to 12 s. With increasing anaesthetic level the probability and duration of stationary EEG increases. PeEn is able to reliably separate consciousness from unconsciousness for EEG segments as short as 2 s. Especially during wakefulness a conflict between stationary EEG sequence durations and methods used for monitoring may exist. PeEn does not require stationarity and functions for EEG sequences as short as 2 s. These promising results seem to support the application of non-linear parameters, such as PeEn, to depth of anaesthesia monitoring.

This exploration primarily aims to jointly apply the local FCN (fully convolution neural network) and YOLO-v5 (You Only Look Once-v5) to the detection of small targets in remote sensing images. Firstly, the application effects of R-CNN (Region-Convolutional Neural Network), FRCN (Fast Region-Convolutional Neural Network), and R-FCN (Region-Based-Fully Convolutional Network) in image feature extraction are analyzed after introducing the relevant region proposal network. Secondly, YOLO-v5 algorithm is established on the basis of YOLO algorithm. Besides, the multi-scale anchor mechanism of Faster R-CNN is utilized to improve the detection ability of YOLO-v5 algorithm for small targets in the image in the process of image detection, and realize the high adaptability of YOLO-v5 algorithm to different sizes of images. Finally, the proposed detection method YOLO-v5 algorithm + R-FCN is compared with other algorithms in NWPU VHR-10 data set and Vaihingen data set. The experimental results show that the YOLO-v5 + R-FCN detection method has the optimal detection ability among many algorithms, especially for small targets in remote sensing images such as tennis courts, vehicles, and storage tanks. Moreover, the YOLO-v5 + R-FCN detection method can achieve high recall rates for different types of small targets. Furthermore, due to the deeper network architecture, the YOL v5 + R-FCN detection method has a stronger ability to extract the characteristics of image targets in the detection of remote sensing images. Meanwhile, it can achieve more accurate feature recognition and detection performance for the densely arranged target images in remote sensing images. This research can provide reference for the application of remote sensing technology in China, and promote the application of satellites for target detection tasks in related fields.

Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task 1 , 2 . Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations 3 . Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer. A study describes the development of a generalizable foundation machine learning framework to extract pathology imaging features for cancer diagnosis and prognosis prediction.