Journal of Medical Systems最新文献

Motor imagery (MI) is widely used in brain-computer interfaces (BCIs) due to its simplicity and reproducibility, enabling individuals with motor impairments to perform non-muscular limb training for the rehabilitation of motor-related neurons. While MI-based BCIs have shown promise for neurorehabilitation, current 2D paradigms fail to engage critical sensorimotor networks. To address this limitation, we designed an immersive MI paradigm in a virtual reality (VR) environment, where participants imagined limb movements in response to continuous three-dimensional (3D) palm motion stimuli. Furthermore, we proposed a novel decoding algorithm that integrates depthwise separable convolution with multi-head self-attention mechanisms. The proposed method was evaluated against existing approaches, demonstrating superior classification accuracy while reducing the temporal and spatial complexity associated with attention mechanisms. To assess the generalizability and robustness of the algorithm across different scenarios, we conducted experiments on two publicly available datasets: BCI Competition IV-2a and the PhysioNet MI dataset. Results showed that our method achieved an average increase of nearly 8% in kappa score over EEGNet in decoding four-class MI tasks in 2D paradigms. Consistent performance across both VR and 2D paradigms confirmed the algorithm's effectiveness and applicability in multi-scenario MI decoding. This study introduces a novel immersive MI paradigm and decoding framework, offering a promising approach for enhancing user engagement in neurorehabilitation and advancing EEG-based intention recognition in VR environments.

Digital Behaviour Change Interventions (DBCIs) aim at improving individual health by engaging various means of Information and Communication Technology (ICT), including mobile apps and wearables. Participant intervention fatigue may happen when DBCIs become too frequent, repetitive, demanding, or lack perceived relevance, and this may result in participants' reduced motivation and adherence over time. Advancing technology-supported engagement mechanisms is therefore of utmost importance. To address this problem, we present a reference and solution architecture based on open-source technologies and open Application Programming Interfaces (Open APIs). First, we integrated a Large Language Model (LLM) component into the DBCI design. Second, to support context-awareness, we enhanced this integration by adding a Geographic Information Systems (GIS) element. Our pilot implemented AI4Motion platform targets both personalization and contextualization aspects of DBCIs. Our work contributes to the emerging discussion on LLM/GIS-related system design patterns for digital platforms supporting Ecological Momentary Assessment (EMA), Experience Sampling Method (ESM), and Just-in-Time Adaptive Interventions (JITAIs).

AI-based diabetic retinopathy (DR) screening algorithms have been evaluated in many countries and have shown promise in expanding access to screening, especially in low- and middle-income countries (LMICs). However, the literature lacks guidance on which algorithms are best suited for these settings. This umbrella review summarizes current evidence on the performance, infrastructure needs, and global implementation of AI-based DR screening tools. Following the Preferred Reporting Items for Systematic Review (PRISMA) guidelines, systematic reviews were identified through searches in PubMed, Embase, ScienceDirect, Scopus, and Google Scholar up to April 18, 2024. Eligible studies were reviewed, and findings were presented in tables and graphics. Twenty systematic reviews were included. Most algorithms were developed, validated, and used in high-income countries, with none developed or implemented in Africa. More than 400 algorithms were identified, of which 161 had some form of clinical validation, and 31 were validated in real-world settings. Sensitivity ranged from 66.0% to 100.0%, specificity from 59.5% to 98.7%, and AUROC from 87.8% to 99.1%. Only 12 algorithms have received regulatory approval, and 11 of them are currently used in clinical practice. AI-based DR screening models hold promise as diagnostic tools across diverse clinical settings, particularly where ophthalmic resources are limited. However, successful implementation depends on appropriate infrastructure, local validation, and regulatory support. Addressing the significant gaps in algorithm development and validation in Africa is essential to ensure equitable access and effective use of AI in DR screening.

To compare and rank the effects of different mobile health (mHealth) technologies on the physical activity of patients with Chronic Obstructive Pulmonary Disease (COPD). Eight databases from January 2010 to March 2025 were searched. Two researchers independently screened the studies and extracted the data. The quality of the literature was evaluated using the Cochrane Risk of Bias tool. Paired Meta-analysis was conducted using Stata 15.1 software, and network Meta-analysis was performed using RStudio software. A total of 25 studies were included, involving 7 intervention measures, with a sample size of 2093 cases. The results showed that in the paired Meta-analysis, the combination of smart wearable devices and telephone support, as well as smart wearable devices alone, could improve the physical activity level of patients; the WeChat platform, App, and smart wearable devices could effectively improve the patients' exercise endurance. Surface under cumulative rank curve (SUCRA) ranking indicated that, compared with other mHealth technologies, the WeChat platform and the combination of telephone support and smart wearable devices were the top two optimal treatment measures. Current evidence shows that mHealth technologies can promote the physical activity of COPD patients and may improve their exercise endurance. When traditional pulmonary rehabilitation is not feasible, the use of mHealth technologies may be a better intervention option. More high-quality studies with large samples, multiple centers, and long-term follow-up are still needed to further verify this conclusion.

The brain network damage provoked by a neurological disease can be modelled as the result of the action of an operator, K, acting on the brain, inspired by physics. Here, we explore the matrix formulation of K, analysing eigenvalues and eigenvectors, with heuristic considerations on different techniques to approximate it. The primary objective of this paper is to lay the foundational groundwork for an innovative framework aimed at the development of predictive models regarding the progression of neurodegenerative diseases. This endeavour will leverage the potential of integrating these novel representations of brain damage with advanced machine-learning techniques. A case study based on real-world data is presented here to support the proposed modelling.

Atrial fibrillation (AF) significantly contributes to the incidence of strokes. Screening for AF enhances its detection and effective management. However, universal AF screening in rural areas poses a challenge. This study evaluates the cost-effectiveness of artificial intelligence-enabled 12-lead electrocardiography (AI-ECG) model for AF screening in rural communities.This cost-effectiveness analysis targeted individuals aged 65 or older, employing a lifelong decision analytic Markov model. AI-ECG model, trained and validated at three Taiwanese hospitals with 285,108 patients, achieved sensitivities of 97.8% and specificities of 99.1%. The study incorporated costs and efficacy of anticoagulant treatments, health status utilities, and clinical variables, derived from literature and Taiwan's epidemiological data. Outcomes were expressed in US dollars per quality-adjusted life year (QALY). The base-case analysis contrasted AI-ECG screening performed by nurses and physician evaluations using standard 12-lead ECGs against no screening, incorporating uncertainty through probabilistic sensitivity analysis. Results were compared with one GDP per capita in Taiwan (≈$32,327 per QALY), a commonly cited willingness-to-pay (WTP) benchmark.Both AI-ECG and physician-led screenings were costlier yet more effective compared with no screening. Although both methods showed comparable effectiveness in detecting AF and in QALYs gained, AI-ECG screening was less expensive ($141 versus $196). Based on 5,000 Monte Carlo simulations, AI-based screening is more cost-effective at lower thresholds ($4,349 to $6,132 per QALY), while physician-led screening becomes preferable beyond $6,132 per QALY. Both strategies remained cost-effective relative to the WTP benchmark. Sensitivity analyses further identified the referral rate following a positive AI-ECG screening as a critical determinant of its cost-effectiveness.AI-ECG screening for AF is a cost-effective alternative, particularly suitable for areas with limited medical resources.

Ambulatory surgery enhances resource utilization through reduced hospital stays and costs without compromising clinical outcomes. However, existing workflows are labor-intensive and repetitive, necessitating optimization in patient selection, assessment, admission notifications, no-show management, patient education, and postoperative follow-up. Artificial intelligence (AI) offers promising solutions to these challenges. This narrative review aimed to outline current AI applications in ambulatory surgery, appraise limitations, and discuss actionable pathways for future innovation. The PubMed database was systematically searched. Inclusion criteria were original research on AI in ambulatory surgery. Exclusion criteria covered weak thematic connections and unavailable full texts. Two researchers independently conducted the search and data extraction. 50 articles were analyzed in this review. AI technologies, including machine learning, computer vision, and natural language processing, are increasingly used for preoperative patient selection and no-show prediction, intraoperative patient information verification, real-time monitoring and decision support, and postoperative recovery monitoring and health guidance. Nonetheless, AI implementation faces challenges such as data heterogeneity, algorithm interpretability, ethical concerns, and regulatory hurdles. AI demonstrates significant potential to optimize ambulatory surgery procedures, enhance clinical decision-making, and improve patient outcomes. Standardized data collection, collaborative data-sharing, transparency, and model validation with clinically meaningful endpoints are essential for robust and extensive AI application in ambulatory surgery. These elements can ultimately enhance the efficiency and safety of ambulatory surgical procedures.

Preterm birth leads to an increased risk of long-term consequences, with over 50% of children born <30 weeks facing motor, cognitive, or behavioural impairments. Early monitoring of motor developmental trajectories, strongly associated with neurodevelopmental outcome, is crucial for a timely identification of deviations from the reference path and the prediction of possible neurodevelopmental disorders (NDDs). However, the current understanding of the causal pathways through which motor difficulties emerge and evolve is limited by the lack of quantitative, standardised, and interpretative measures for infant motor development, and the need for a complex multidisciplinary examination of medical history. To overcome these limitations, we propose an approach based on Digital Twins (DTs) and innovative technology-based interpretative metrics for motor assessment to support holistic longitudinal evaluations of infant development. The DT enables the integration of multimodal data, including algorithms for data processing and artificial intelligence methods for data analysis, into a unique framework. Details on the DT ecosystem, internal model, and engine are provided. As a first step, a proof-of-concept application was implemented to show the feasibility of the framework, not yet exploring its full longitudinal potential. This initial study was based on already published data (17 full-term children, 21 preterm children born between 29 and 36 gestational weeks, and 8 very preterm children born ≤28 gestational weeks) and illustrates the integration of motor measures with clinical and cognitive information, their standardisation into the DT model, and a first set of advanced analyses. Given the relevance of the problem and the lack of standardised, structured follow-up protocols to monitor motor trajectory in preterm children, the proposed solution has the potential for a significant impact in clinical practice. Moreover, its usable and scalable design allows for easy adaptation to large, multi-center cohort studies targeting various infant clinical populations where motor function monitoring is essential (i.e. from children with rare neurological disorders to all newborns).