Training Paradigm Research Articles

Gamified Web-Delivered Attentional Bias Modification Training for Adults With Chronic Pain: Randomized, Double-Blind, Placebo-Controlled Trial.

Attentional bias to pain-related information has been implicated in pain chronicity. To date, research investigating attentional bias modification training (ABMT) procedures in people with chronic pain has found variable success, perhaps because training paradigms are typically repetitive and monotonous, which could negatively affect engagement and adherence. Increasing engagement through the gamification (ie, the use of game elements) of ABMT may provide the opportunity to overcome some of these barriers. However, ABMT studies applied to the chronic pain field have not yet incorporated gamification elements. This study aimed to investigate the effects of a gamified web-delivered ABMT intervention in a sample of adults with chronic pain via a randomized, double-blind, placebo-controlled trial. A final sample of 129 adults with chronic musculoskeletal pain, recruited from clinical (hospital outpatient waiting list) and nonclinical (wider community) settings, were included in this randomized, double-blind, placebo-controlled, 3-arm trial. Participants were randomly assigned to complete 6 web-based sessions of nongamified standard ABMT (n=43), gamified ABMT (n=41), or a control condition (nongamified sham ABMT; n=45) over a period of 3 weeks. Active ABMT conditions trained attention away from pain-related words. The gamified task included a combination of 5 game elements. Participant outcomes were assessed before training, during training, immediately after training, and at 1-month follow-up. Primary outcomes included self-reported and behavioral engagement, pain intensity, and pain interference. Secondary outcomes included anxiety, depression, cognitive biases, and perceived improvement. Results of the linear mixed model analyses suggest that across all conditions, there was an overall small to medium decline in self-reported task-related engagement between sessions 1 and 2 (P<.001; Cohen d=0.257; 95% CI 0.13-0.39), sessions 1 and 3 (P<.001; Cohen d=0.368; 95% CI 0.23-0.50), sessions 1 and 4 (P<.001; Cohen d=0.473; 95% CI 0.34-0.61), sessions 1 and 5 (P<.001; Cohen d=0.488; 95% CI 0.35-0.63), and sessions 1 and 6 (P<.001; Cohen d=0.596; 95% CI 0.46-0.73). There was also an overall small decrease in depressive symptoms from baseline to posttraining assessment (P=.007; Cohen d=0.180; 95% CI 0.05-0.31) and in pain intensity (P=.008; Cohen d=0.180; 95% CI 0.05-0.31) and pain interference (P<.001; Cohen d=0.237; 95% CI 0.10-0.37) from baseline to follow-up assessment. However, no differential effects were observed over time between the 3 conditions on measures of engagement, pain intensity, pain interference, attentional bias, anxiety, depression, interpretation bias, or perceived improvement (all P values>.05). These findings suggest that gamification, in this context, was not effective at enhancing engagement, and they do not support the widespread clinical use of web-delivered ABMT in treating individuals with chronic musculoskeletal pain. The implications of these findings are discussed, and future directions for research are suggested. Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12620000803998; https://anzctr.org.au/ACTRN12620000803998.aspx. RR2-10.2196/32359.

Effects of chemoradiation and tongue exercise on swallow biomechanics and bolus kinematics.

Common treatments for head and neck cancer (radiation and chemotherapy) can lead to dysphagia; tongue exercise is a common intervention. This study aimed to assess swallow biomechanics and bolus kinematics using a well-established rat model of radiation or chemoradiation treatment to the tongue base, with or without tongue exercise intervention. Pre- and post-treatment videofluoroscopy was conducted on 32 male Sprague-Dawley rats treated with radiation/chemoradiation and exercise/no exercise. Rats in the exercise groups completed a progressive resistance tongue training paradigm. Swallow biomechanics, bolus kinematics, jaw opening, and post-swallow respiration were assessed. Both treatments impacted outcome measures; the addition of exercise intervention showed benefit for some measures, particularly in rats treated with radiation, vs. chemoradiation. Radiation and chemoradiation can significantly affect aspects of deglutition; combined treatment may result in worse outcomes. Tongue exercise intervention can mitigate deficits; more intensive intervention may be warranted in proportion to combined treatment.

A novel augmented reality-based simulator for enhancing orthopedic surgical training.

Total Hip Arthroplasty (THA) is a well-established and common orthopedic surgery. Due to the complexity involved in THA, orthopedic surgeons require rigorous training. However, the current gold standard, the tutor-guided and -evaluated apprenticeship model is time-consuming, costly, and poses risks to patients. There is a pressing need for additional training resources to enhance the efficiency and safety of the training process. In this work, we present a novel Augmented Reality (AR)-based simulator designed for THA that helps enable a new self-paced training and learning paradigm without the need for instructors. The simulator reduces the need for instructors by integrating an AR guidance module and an automated performance evaluation module. Three types of AR guidance were developed: Overlay, Virtual Twin, and Sectional Views. A feasibility study was conducted with five resident surgeons and two senior surgeons to compare these guidance methods quantitatively and qualitatively. The automated performance evaluation module was assessed against manual performance evaluation using Bland-Altman analysis with limits of agreement (LoA) and Mann-Whitney U tests. The quantitative feasibility results indicate the efficacy of the developed AR guidance, characterized by mean transitional and rotational deviation errors below 3 mm and 3 degrees. Based on the qualitative results, we provide recommendations for efficient AR guidance designs. The Bland-Altman analysis results (0.22±1.32mm with LoA -2.37 to 2.81 mm for distance deviation, 0.94±2.41 degrees with LoA -3.78 to 5.66 degrees for yaw deviation, -0.34±1.30 degrees with LoA -2.90 to 2.22 degrees for pitch deviation) and p-values of Mann-Whitney U tests (0.64 for distance deviation, 0.12 for yaw deviation, 0.11 for pitch deviation) indicate no statistically significant differences between the automated and manual performance evaluation at a significance level of 0.05. This work shows the potential of AR-based simulators in introducing a novel, data-driven approach to open surgery training in orthopedics, enabling surgeons to individually assess and improve their progress.

High-fidelity surgical simulator for the performance of craniofacial osteotomies.

The oral and maxillofacial (OMF) surgical community is making an active effort to develop new approaches for surgical training in order to compensate for work-hour restrictions, mitigate differences between training standards, and improve the efficiency of learning while minimizing the risks for the patients. Simulation-based learning, a technology adopted in other training paradigms, has the potential to enhance surgeons' knowledge and psychomotor skills. We developed a fully immersive, high-fidelity virtual simulation trainer system based on Kitware's open-source visualization and interactive simulation libraries: the Interactive Medical Simulation Toolkit (iMSTK) and the Visualization Toolkit (VTK). This system allows surgeons to train for the crucial osteotomy step in bilateral sagittal split osteotomy (BSSO) using a pen-grasp oscillating saw that is controlled in the virtual environment using a 3D Systems Geomagic Touch haptic device. The simulator incorporates a proficiency-based progression evaluation system to assess the correctness of the cut and provide user feedback. Three expert clinicians and two senior residents tested our pilot simulator to evaluate how the developed system compares to the performance of real-life surgery. The outcomes of the face and content validation study showed promising results with respect to the quality of the simulated images and the force feedback response they obtained from the device matched what they expected to feel. The developed trainer has the potential to contribute to a reduction in the prevalence of adverse surgical outcomes after OMF surgeries involving osteotomies. Observing the clinicians and talking through some of the difficulties helped us identify key areas for improvement. Future work will focus on further clinical evaluation for the BSSO surgical scenario and extension of the trainer to include other craniofacial osteotomy procedures.

Bifrontal Craniectomy: A High-Yield Surgical Training Tool.

Bifrontal decompressive craniectomy (DC), which was once a popular technique for treating midline mass lesions, has seen a notable decline in its therapeutic use within modern neurosurgery. Despite its diminished clinical use, the procedure offers considerable value as an educational tool for surgical training. This study used a Thiel-embalmed cadaver to demonstrate the bifrontal DC procedure, including a Souttar incision, strategic (MacCarty, zygomatic, and apical) keyhole/burr hole placement, superior sagittal sinus suturing, left frontal lobe decortication, and microscopic visualization of the anterior cranial fossa. The procedure demonstrated educational value in three ways: first, wide anatomical exposure enables a detailed discussion of tissue handling. Second, an efficient training paradigm that allows multiple surgical techniques to be taught within a limited timeframe. Third, it offers risk management training focusing on superior sagittal sinus protection. While bifrontal DC has selective therapeutic applications, its potential as a teaching tool is undervalued. The procedure's wide exposure creates an ideal platform for surgical education, allowing residents to develop skills in a structured environment. We advocate its use in training programs by focusing on its educational benefits rather than its limited therapeutic role.

Activity-related dyspnea and exercise intolerance in chronic obstructive pulmonary disease: recent insights.

Exertional dyspnea and exercise intolerance remain key patient-related outcomes in chronic obstructive pulmonary disease (COPD). Improvement in treatment strategies is pendant further understand of their underpinnings across the spectrum of disease severity. Emerging literature has been reviewed based on a conceptual framework that relates ventilatory demand to capacity under the modulating influence of sub-cortical and cortical centers (symptom perception and affective interpretation). Evidence supporting these fundamental tenets is critically appraised, focusing on mechanistic and interventional studies that shed novel light on the sources of heightened and/or mechanically constrained ventilation. Mechanistic studies using proxies of the inspiratory neural drive (e.g., diaphragm electromyography) were particularly informative, as well as interventional trials aimed at decreasing afferent stimulation and/or symptom perception via pharmacological (e.g., low-dose opiates in selected patients, high flow oxygen, oral nitrate) and nonpharmacological (e.g., novel exercise training paradigms, inspiratory muscle training, breathing techniques) interventions. Therapeutic and rehabilitative strategies to lessen dyspnea's devastating impact on quality of life should minimize demand in the setting of reduced capacity and increased sensation awareness in COPD. The most successful attempts so far have amalgamated pharmacological and nonpharmacological approaches tailored to the main underlying mechanisms on an individual basis.

SiCRNN: A Siamese Approach for Sleep Apnea Identification via Tracheal Microphone Signals.

Sleep apnea syndrome (SAS) affects about 3-7% of the global population, but is often undiagnosed. It involves pauses in breathing during sleep, for at least 10 s, due to partial or total airway blockage. The current gold standard for diagnosing SAS is polysomnography (PSG), an intrusive procedure that depends on subjective assessment by expert clinicians. To address the limitations of PSG, we propose a decision support system, which uses a tracheal microphone for data collection and a deep learning (DL) approach-namely SiCRNN-to detect apnea events during overnight sleep recordings. Our proposed SiCRNN processes Mel spectrograms using a Siamese approach, integrating a convolutional neural network (CNN) backbone and a bidirectional gated recurrent unit (GRU). The final detection of apnea events is performed using an unsupervised clustering algorithm, specifically k-means. Multiple experimental runs were carried out to determine the optimal network configuration and the most suitable type and frequency range for the input data. Tests with data from eight patients showed that our method can achieve a Recall score of up to 95% for apnea events. We also compared the proposed approach to a fully convolutional baseline, recently introduced in the literature, highlighting the effectiveness of the Siamese training paradigm in improving the identification of SAS.

Fedeval: Defending Against Lazybone Attack via Multi-dimension Evaluation in Federated Learning

Federated learning (FL) has become a prominent paradigm for collaborative model training while ensuring data privacy. However, in resource-constrained environments, such as the Internet of Things (IoT), FL faces a distinct challenge from Lazybone attackers, who compromise system performance by providing low-quality data or conducting minimal local training to reduce their computational burden. In this paper, we propose Fedeval, a novel multi-dimensional evaluation framework designed to defend against Lazybone attacks. Fedeval leverages a server-side base validation dataset and a base model to assess the quality and relevance of client contributions through gradient inversion, and it compares client-uploaded gradients with an honest baseline to detect training inconsistencies. By assigning adaptive importance scores based on client contributions, Fedeval enhances the robustness of FL by mitigating the impact of non-contributing participants. We also provide a theoretical analysis of Fedeval’s convergence properties and validate its effectiveness through extensive experiments on four datasets and two attack scenarios. Our results demonstrate that Fedeval significantly accelerates convergence and improves accuracy by up to 13% compared to traditional methods.

Semi-supervised learning network for deep-sea nodule mineral image segmentation

The accurate segmentation of deep-sea nodule mineral images is crucial for scientific mining. However, due to the low contrast of deep-sea images and the varying sizes of nodule minerals, existing methods are not effective in segmenting these images. Furthermore, fully supervised deep learning methods require numerous labelled images for training, and labelling deep-sea nodule mineral images is highly difficult, resulting in a scarcity of available labeled images, which limits the model generalization ability. To address these challenges, a semi-supervised learning network for deep-sea nodule image segmentation (NmiNet) was proposed. In this method, a semi-supervised training paradigm based on underwater image enhancement perturbation and uncertainty weighted optimization (UEUO) was designed. This paradigm enabled the model to fully mine the features in many unlabeled nodule mineral images under the condition of few labelled nodule mineral images, improving the model generalization ability. Moreover, a lightweight global and local feature extraction (GLFE) module was designed to enhance the attention of the module to small nodules, and its ability to locate nodules of different scales by fusing local and global features without considerably increasing model complexity. Experimental results on deep-sea nodule mineral images reveal that this method outperforms existing approaches.

Theta Rhythm-Based Attention Switch Training Effectively Modified Negative Attentional Bias.

Attentional bias modification training (ABMT) is commonly employed to regulate negative attentional bias (NAB) and, in turn, to prevent or alleviate depressive symptoms. Recent advancements in attention switch theory have facilitated the development of a novel training paradigm that may enhance the efficacy of such interventions. A total of fifty-seven college students were assigned to two groups: one exhibiting NAB and the other without. Both groups underwent training with a novel paradigm integrating theta rhythm with the traditional dot-probe task (DPT). The DPT was also administered as a pre- and post-test measure. For individuals with NAB, rhythmic DPT effectively alleviates their NAB. Additionally, within the training procedure's DPT, flashing negative stimuli elicits faster responses when the probe appears at the positive stimulus' location. Baseline attention scores can negatively predict changes in subsequent corresponding attentional performance. This study presents a novel training paradigm-the theta rhythm-based DPT-that effectively modifies NAB. The mechanism underlying this intervention may be driven by positive salient stimuli at the critical trough, facilitating the switch of attention from negative to positive stimuli.

Exploratory study on the enhancement of O-RADS application effectiveness for novice ultrasonographers via deep learning.

The study aimed to create a deep convolutional neural network (DCNN) model based on ConvNeXt-Tiny to identify classic benign lesions (CBL) from other lesions (OL) within the Ovarian-Adnexal Reporting and Data System (O-RADS), enhancing the system's utility for novice ultrasonographers. Two sets of sonographic images of pathologically confirmed adnexal lesions were retrospectively collected [development dataset (DD) and independent test dataset (ITD)]. The ConvNeXt-Tiny model, optimized through transfer learning, was trained on the DD using the original images directly and after automatic lesion segmentation by a U-Net model. Models derived from both training paradigms were validated on the ITD for sensitivity, specificity, accuracy, and area under the curve (AUC). Two novice ultrasonographers were assessed in O-RADS with and without assistance from the model for Application Effectiveness. The ConvNeXt-Tiny model trained on original images scored AUCs of 0.978 for DD and 0.955 for ITD, while the U-Net segmented image model achieved 0.967 for DD and 0.923 for ITD; neither showed significant differences. When assessing the malignancy of lesions using O-RADS 4 and 5, the diagnostic performances of two novice ultrasonographers and senior ultrasonographer, as well as model-assisted classifications, showed no significant differences, except for one novice's low accuracy. This approach reduced classification time by 62 and 64 min. The kappa values with senior doctors' classifications rose from 0.776 and 0.761 to 0.914 and 0.903, respectively. The ConvNeXt-Tiny model demonstrated excellent and stable performance in distinguishing CBL from OL within O-RADS. The diagnostic performance of novice ultrasonographers using O-RADS is essentially equivalent to that of senior ultrasonographer, and the assistance of the model can enhance their classification efficiency and consistency with the results of senior ultrasonographer.

Identification of perceptive users based on the graph convolutional network

In this paper, we introduce a pioneering approach titled Identifying Perceptive Users based on Graph Convolutional Networks (IPGCN), tailored specifically for user-object bipartite networks. Our methodology incorporates rating behavior into the graph convolutional network framework, crafting a feature matrix that encapsulates the intricacies of user preferences. This feature matrix is meticulously constructed using a breadth-first search algorithm, grounded in user rating attributes, effectively transforming the bipartite network into a compact, low-dimensional vector space. This transformation not only serves as the input for our graph convolutional network but also meticulously preserves the topological structure and interconnectivity of the nodes. Furthermore, we introduce a novel training paradigm by leveraging the classification outcomes from a perceptive user quantification model. This model discriminates between perceptive users, defined as the top q% (with 0 <q< 1) most insightful users based on their ratings, and non-perceptive users. This binary classification serves as the ground truth for training our IPGCN model. Through rigorous experimentation on three real-world datasets, we demonstrate that our IPGCN method outperforms traditional machine learning algorithms like SVM, XGBoost, and Random Forest, as well as state-of-the-art graph neural network approaches including GCN and GAT. Specifically, when q = 5%, our method achieves remarkable improvements of 25.28% in Accuracy, 23.34% in F1 Score, and 25.30% in AUC, highlighting the efficacy and superiority of our proposed IPGCN approach in identifying perceptive users within complex bipartite networks.

It is a "small world": Relations between performance on five spatial tasks and five mathematical tasks in undergraduate students.

One of the most robust relations in cognition is that between spatial and mathematical reasoning. One important question is whether this relation is domain general or if specific relations exist between performance on different types of spatial tasks and performance on different types of mathematical tasks. In this study, we explore unique relations between performance on five spatial tasks and five mathematical tasks. An exploratory factor analysis conducted on Data Set 1 (N = 391) yielded a two-factor model, one spatial factor and one mathematical factor with significant cross-domain factor loadings. The general two-factor model structure was replicated in a confirmatory factor analysis conducted in a separate data set (N = 364) but the strength of the factor loadings differed by task. Multidimensional scaling and network-based analyses conducted on the combined data sets reveal one spatial cluster, with a central node and one more tightly interconnected mathematical cluster. Both clusters were interconnected via the math task assessing geometry and spatial sense. The unique links identified with the network-based analysis are representative of a "small-world network." These results have theoretical implications for our understanding of the spatial-mathematical relation and practical implications for our understanding of the limitations of transfer between spatial training paradigms and mathematical tasks. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Accurate neural control of a hand prosthesis by posture-related activity in the primate grasping circuit.

Brain-computer interfaces (BCIs) have the potential to restore hand movement for people with paralysis, but current devices still lack the fine control required to interact with objects of daily living. Following our understanding of cortical activity during arm reaches, hand BCI studies have focused primarily on velocity control. However, mounting evidence suggests that posture, and not velocity, dominates in hand-related areas. To explore whether this signal can causally control a prosthesis, we developed a BCI training paradigm centered on the reproduction of posture transitions. Monkeys trained with this protocol were able to control a multidimensional hand prosthesis with high accuracy, including execution of the very intricate precision grip. Analysis revealed that the posture signal in the target grasping areas was the main contributor to control. We present, for the first time, neural posture control of a multidimensional hand prosthesis, opening the door for future interfaces to leverage this additional information channel.

Yes, we can! Ensuring that our graduating resident’s procedural skills meet the needs of their patients

There is a burgeoning discrepancy between the procedural competency of graduating diagnostic radiology residents and the needs of our patient population. The causes of this mismatch and opportunities for improvement are explored by the APDR Procedural Competency of Graduating DR Residents Task Force. The APDR convened a task force consisting of diverse broad stakeholder viewpoints, drawing from organized radiology, academic and private practices. The task force conducted structured analyses of the drivers contributing to the current state and reviewed relevant resources, conducted membership surveys, and developed consensus statements regarding solutions to the identified problem. A defined list of procedures a graduating resident is expected to competently perform is established. Key domain-based drivers of the currents state were identified including the ABR initial certification exam structure and content, ACGME practices, creation of the IR-DR residency and ESIR tracks, residency and fellowship training paradigms, and secular trends. The task force offers several best practice recommendations for improving procedural training in DR residency to better meet the needs of the marketplace and our patients. Armed with a defined list of procedures expected of a general radiologist and best practices for enhancing procedural training in diagnostic residencies, the task force presents a national game-plan for improving our ability to deliver high value diagnostic and interventional services to the communities that need it most.

Effect of Deep Cervical Flexors Training Paradigm on neck pain and functional disability in Patients with Cervicogenic Headache

Effect of Deep Cervical Flexors Training Paradigm on neck pain and functional disability in Patients with Cervicogenic Headache

Effects of Stroboscopic Goggles on Standing Balance in the Spatiotemporal and Frequency Domains: An Exploratory Study.

Balance training paradigms have been shown to effectively reduce fall risk. Visual feedback is an important sensory mechanism for regulating postural control, promoting visual perturbations for balance training paradigms. Stroboscopic goggles, which oscillate from transparent to opaque, are a form of visual perturbation, but their effect on standing balance has not been assessed. In this study, 29 participants stood in bilateral and tandem stances as the center of pressure was recorded for 6 consecutive minutes wherein there were no stroboscopic perturbations in the first and last minutes. Spatial-temporal, frequency domain, and nonlinear standing balance parameters were calculated for each period. More differences in spatial-temporal parameters due to the strobe were found in the medial-lateral direction than the anterior-posterior direction. More differences in frequency domain parameters were observed in the anterior-posterior direction than the medial-lateral direction, but this did not occur for each variable. The nonlinear parameters were strongly affected by the strobe. Stroboscopic perturbations did not affect the bilateral and tandem stances equally. Spatial-temporal parameters for the tandem stance were greater in magnitude during the strobe period than the no strobe periods. This effect was not seen with the bilateral stance. This indicates that the efficacy of stroboscopic perturbations for challenging standing balance depends on task difficulty. Balance training paradigms that utilize stroboscopic perturbations will need to harmonize these perturbations with task difficulty.

Enhancing neuroprosthesis calibration: the advantage of integrating prior training over exclusive use of new data

Objective.Neuroprostheses typically operate under supervised learning, in which a machine-learning algorithm is trained to correlate neural or myoelectric activity with an individual's motor intent. Due to the stochastic nature of neuromyoelectric signals, algorithm performance decays over time. This decay is accelerated when attempting to regress proportional control of multiple joints in parallel, compared with the more typical classification-based pattern recognition control. To overcome this degradation, neuroprostheses and commercial myoelectric prostheses are often recalibrated and retrained frequently so that only the most recent, up-to-date data influences the algorithm performance. Here, we introduce and validate an alternative training paradigm in which training data from past calibrations is aggregated and reused in future calibrations for regression control.Approach.Using a cohort of four transradial amputees implanted with intramuscular electromyographic recording leads, we demonstrate that aggregating prior datasets improves prosthetic regression-based control in offline analyses and an online human-in-the-loop task. In offline analyses, we compared the performance of a convolutional neural network (CNN) and a modified Kalman filter (MKF) to simultaneously regress the kinematics of an eight-degree-of-freedom prosthesis. Both algorithms were trained under the traditional paradigm using a single dataset, as well as under the new paradigm using aggregated datasets from the past five or ten trainings.Main results.Dataset aggregation reduced the root-mean-squared error (RMSE) of algorithm estimates for both the CNN and MKF, although the CNN saw a greater reduction in error. Further offline analyses revealed that dataset aggregation improved CNN robustness when reusing the same algorithm on subsequent test days, as indicated by a smaller increase in RMSE per day. Finally, data from an online virtual-target-touching task with one amputee showed significantly better real-time prosthetic control when using aggregated training data from just two prior datasets.Significance.Altogether, these results demonstrate that training data from past calibrations should not be discarded but, rather, should be reused in an aggregated training dataset such that the increased amount and diversity of data improve algorithm performance. More broadly, this work supports a paradigm shift for the field of neuroprostheses away from daily data recalibration for linear classification models and towards daily data aggregation for non-linear regression models.

Virtual reality in surgical training: Trends and prospects

Virtual Reality (VR) is revolutionizing medical education and surgical training by offering immersive, risk-free environments for trainees. With advancements in technology, VR has moved from simple visualization tools to interactive platforms that provide real-time feedback, improving the precision and dexterity required for complex surgical procedures. This review aims to explore the current trends in VR-based surgical training, highlighting key technologies, their applications, and future prospects. While traditional surgical training relies heavily on cadaveric dissections and apprenticeship models, VR provides a standardized, cost-effective, and replicable learning experience. The review also delves into the technical challenges and ethical considerations in integrating VR into medical curricula. Moreover, we evaluate empirical evidence that supports VR’s efficacy in enhancing surgical skills and patient outcomes. The prospects of augmented reality (AR) and mixed reality (MR) in hybrid approaches are also discussed, which could complement existing training paradigms. In conclusion, VR shows promise not only in the training of novice surgeons but also as a continuous learning tool for experienced professionals, fostering the evolution of surgical education.

Meta-learning-based fault diagnosis method for rolling bearings under cross-working conditions

Abstract Accurate prediction of bearing failures is crucial for reducing maintenance costs and enhancing production efficiency in rotating machinery. However, the variable speed conditions and complex working environments encountered during operation pose significant challenges to fault diagnosis. Problems such as domain shift and insufficient sample quantity may occur during fault diagnosis under cross-working conditions, which can decrease the accuracy and generalization of deep learning algorithms. In this paper, we introduce a fault diagnosis framework grounded in meta-learning. Centered on a dual-channel feature fusion network and employing a meta-learning training paradigm, the framework not only performs well in cross-condition fault diagnosis tasks but also demonstrates superior performance in few-shot learning scenarios. Firstly, dual-channel network is used to extract the classification features of different domains, and the features are fused. Next, training is conducted using a meta-learning strategy to acquire prior knowledge, enabling rapid model adaptation to cross-working conditions and addressing the challenge of limited training samples. Finally, two public rolling bearing data sets are used to demonstrate the efficacy of the proposed method across different operational conditions. Prior to this, we selected the appropriate sample length and fusion domain through experimental validation. The proposed method also has good fault diagnosis accuracy in cross-device tasks. The experimental results verify the effective classification capability and robustness of the proposed method. Furthermore, comparisons with other meta-learning approaches confirm the superior performance of our method. The ablation experiments validated the importance and irreplaceability of each component of the proposed method.