Progress in biomedical engineering (Bristol, England)最新文献

The longevity and performance of biomedical implants depend strongly on surface properties, motivating coatings that enhance biocompatibility, mechanical resilience, and resistance to wear and infection. This review analyzes state-of-the-art coatings for orthopedic and dental implants, linking material choice, deposition method, and demonstratedin vitroperformance. Bioinert systems (e.g. TiN, diamond-like carbon), bioactive coatings such as hydroxyapatite and bioactive glass, and antibacterial approaches using silver-, zinc-oxide-, and graphene-based layers are compared for their effects on osseointegration, bacterial control, and durability. Deposition routes from plasma spraying to advanced methods including pulsed laser deposition, atomic layer deposition (ALD), and plasma-enhanced chemical vapor deposition are evaluated for adhesion, microstructure control, and clinical practicality. Across recent studies, nanostructured and multifunctional coatings consistently accelerate early osteogenic responses, ion- or carbon-modified hydroxyapatite improves interfacial bonding while adding antibacterial activity, conformal ultrathin films from ALD enhance corrosion resistance on complex geometries without impairing cell viability, and multilayer or hybrid architectures reduce tribocorrosion under cyclic loading. Remaining challenges include maintaining long-term stability and uniform coverage on intricate implant designs and scaling fabrication economically. Emerging directions focus on stimuli-responsive surfaces and biodegradable, drug-eluting coatings aimed at reducing infection risk and speeding integration, with the overall trajectory pointing toward coatings that couple mechanical reliability with targeted biological function.

Brain-controlled powered wheelchairs represent a promising advancement for individuals with neurological conditions that significantly impair motor function. Despite substantial progress, brain-controlled wheelchairs have not been adapted for real-world settings. This article systematically reviews recent trends in brain-computer interface (BCI) technology for wheelchair navigation and control, highlighting the contributions and limitations of various navigation paradigms. This review was conducted in accordance with the PRISMA guidelines, sourcing studies from four databases (PubMed, Scopus, IEEE Xplore, Google Scholar) published between 2000 and April 2025. This review focused on non-invasive BCI paradigms and real-world navigation experiments. The results were narratively synthesized and classified into two primary categories: BCI-based navigation paradigms and wheelchair-based navigation paradigms, along with intersecting concepts such as single-variant BCI, hybrid BCI, control switches, and proportional control. Of the 149 full-text articles reviewed, 47 were included and categorized by navigation paradigm, comprising 20 BCI-based and 27 wheelchair-based studies, with 6 involving participants with motor disabilities. Quality assessment scores ranged from 40% to 95%, with approximately 40% of the studies demonstrating a low risk of bias. The findings indicate that low-level navigation control was predominant in BCI wheelchair studies, with 31 studies employing minimal or no obstacle avoidance. Most studies (57%) integrated sensors for obstacle avoidance, localization, mapping, and autonomous navigation. Twenty-two studies utilized control switches, and five incorporated proportional control for wheelchair navigation. Additionally, motor imagery and steady-state visually evoked potential (SSVEP) paradigms have emerged as the most common approaches for generating control commands, highlighting their potential for effective navigation. Given the potential societal impact on a large number of individuals, future research should prioritize enhancing the reliability and adaptability of BCI wheelchair systems in real-world environments. .

The introduction and development of soft continuum robots (SCRs) for minimally invasive surgery and endoluminal intervention offers a promising option for navigating delicate, convoluted human anatomy across various procedures. However, successful translation of SCRs from research prototypes through to clinically viable tools relies on overcoming the challenge of functionalization for targeted diagnostic and therapeutic intervention. Functionalization demands specialized design and fabrication strategies to ensure practical integration of operational components, such as stimuli-responsive materials and tip-mounted transducers, with soft bioinspired geometry and actuation mechanisms. This review aims to highlight the state of the art in the development of functionalized SCRs for minimally invasive and endoluminal applications. Drawing on advances over the past twenty-five years, we provide a comprehensive discussion of the innovations to date and of the pivotal clinical and developmental challenges to be overcome for the functionalization, therapeutic benefit and therefore, clinical translation of SCRs. Through developing coherence between the fields of bio-inspired soft robotic design, digitally driven fabrication, materials engineering and intra-operative control, further clinically significant advances may be realized in the domain of functionalized SCRs.

Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning (ML) systems into high-stakes domains such as digital health. Key dimensions, including robustness, explainability, fairness, accountability, and privacy, need to be addressed throughout the AI lifecycle, from problem formulation and data collection to model deployment and human interaction. While various contributions address different aspects of trustworthy AI, a focused synthesis on robustness and explainability, especially tailored to the healthcare context, remains limited. This review addresses that need by organizing recent advancements into an accessible framework, highlighting both technical and practical considerations. We present a structured overview of methods, challenges, and solutions, aiming to support researchers and practitioners in developing reliable and explainable AI solutions for digital health. This review article is organized into three main parts. First, we introduce core pillars of trustworthy AI and discuss the technical and ethical challenges they pose, particularly in the context of digital health. Second, we explore application-specific trust considerations across domains such as intensive care, mental health, metabolic disease, and public health surveillance, highlighting how explainability, clinical validation, and human oversight support trust. Lastly, we present recent advancements in techniques aimed at improving robustness under data scarcity and distributional shifts, as well as explainable AI methods ranging from feature attribution to gradient-based interpretations and counterfactual explanations. This paper is further enriched with detailed discussions of the contributions toward robustness and explainability in digital health, the development of trustworthy AI systems in the era of LLMs, and various evaluation metrics for measuring trust and related parameters such as validity, fidelity, and diversity, offering a roadmap for building safer and more reliable AI systems.

Diabetes mellitusis a widespread chronic disease with steadily growing prevalence and associated comorbidities. Current treatment of diabetes can be quite cumbersome for the patients, leading to global efforts to develop a fully-automated artificial pancreas. Such a device will need to employ some form of blood glucose prediction, as well as algorithms to detect meal intake and various physical activities. Many methods were already developed for these tasks, enabling the meta-analysis of the current state of the art. First, an overview of glycemic control strategies and sensors is provided. Then, the relevant studies are introduced and described prior to the meta-analysis. The resulting meta-analysis quantifies the accuracy of prediction models for the various prediction horizons (15, 30, 45, 60, and 120 min) and the performance of meal and physical activity detection models using sensitivity and metrics related to false-positivity. Following the observed patterns across the prediction horizons, a novel approach to evaluating the physiological plausibility of prediction methods is proposed. The baseline state-of-the-art model performance for said tasks is estimated. Finally, a discussion about the current issues in the research of diabetic technologies and their potential solutions is conducted.

One of the most common sites of cancer metastasis is the bone, with a large proportion of both breast cancer and prostate cancer patients who develop metastases having involvement of the skeleton. The prognosis for patients with bone metastases is poor as there are limited effective treatment options. The lack of reliable models to recapitulate the native bone micro-environment during the drug discovery process, has resulted in a poor understanding of the biological processes that enable and drive metastases, and difficulty evaluating potential treatments. Animal models that have been successful in the genesis of cutting-edge treatments for primary cancer have not been able to be used for treatments for metastases, in part due to their inability to accurately recapitulate the native human microenvironment. Consequently, the development and availability of drugs to treat and/or prevent bone metastases are lacking. The last decade has seen an increase in the development and use of three dimensional (3D) scaffolds in cell culture to investigate cancer, as these models have demonstrated similar cancer cellular growth and gene/protein expression to the native human microenvironment. The majority of 3D cell culture systems for studying cancer processes comprise a soft matrix, which fails to accurately replicate the rigidity and structural complexity of bone tissue, which further alters the behaviour of cells. This systematic literature review focuses on the research to date on the development and characterisation of solid scaffolds that have been used for the purpose ofin vitroinvestigation of bone metastases. It highlights the importance of materials testing to characterise the models, ensuring they have a composition, structure and strength similar to bone, to give appropriate mechanical cues to cells, while also highlighting the biological validation completed to ensure the models are an accurate representation of the metastatic niche.

Bioimpedance measurements have gained significant attention due to their ability to assess body composition, muscle health, and internal physiological states without the need for intrusive procedures. This review paper explores the advancements and applications of bioimpedance technology, a non-invasive and cost-effective method for real-time monitoring of physiological parameters and physical activities. It discusses key measurement modalities such as bioelectrical impedance analysis, electrical impedance myography, and electrical impedance tomography, highlighting their unique advantages and applications. It also examines the role of biopotential electrodes, both polarizable and non-polarizable, in ensuring accurate physiological measurements. Despite challenges such as low spatial resolution, motion artifacts and sensitivity to electrode placement, the review highlights promising solutions. These include the integration of hybrid sensor systems, machine learning algorithms for signal interpretation, and the development of wearable and flexible electronics. The paper concludes by emphasizing the growing potential of bioimpedance technology in fields such as sports science, rehabilitation, personalised healthcare, fitness monitoring, and human-machine interaction, suggesting a future where continuous physiological monitoring becomes seamlessly embedded in daily life.

Visual prostheses represent a groundbreaking avenue for restoring vision in individuals with visual impairments. These devices utilize electrode arrays positioned in early visual processing areas, like the retina, thalamus, or primary visual cortex. Connected to a camera, they transform a stream of video to electrical stimulation to present the visual environment through patterned activation of phosphenes. Visual prostheses offer the potential to enhance visual function and thereby quality of life for users, however, understanding and replicating motion perception in a manner akin to natural vision remains a critical challenge for device designers. This review presents studies of motion perception in different visual prosthesis modalities and discusses their advantages and limitations. Retinal and cortical visual prostheses show significant potential in enhancing motion perception, but many implementations have shortcomings. Some challenges which remain for better motion perception in visual prosthesis are gaze contingency, the effective integration of machine vision, and understanding the involvement of higher-order visual areas. Despite these challenges, the current research should be viewed with substantial optimism for the future of restoring functional vision to visually impaired individuals.

Spasticity, a type of hypertonia characterized by a velocity-dependent increase in muscle tone, is associated with damage to the brain and/or spinal cord in different neurological conditions. However, secondary non-neurological factors, such as soft tissue changes, can complicate the assessment and differentiation of the underlying causes. Accurate assessment is crucial for effective treatment planning, with clinicians relying on passive movement to grade the 'feel' of the spastic limb. This review aims to identify and evaluate the feasibility of spasticity models or simulations for clinical teaching. Models based on human spastic limbs were examined, with no restrictions on specific conditions or populations. A comprehensive search of four databases and gray literature was conducted to identify relevant studies. Criteria for inclusion focused on model development, data, and evaluation processes. Study selection and data extraction were carried out by independent reviewers, and data synthesis was performed by systematically mapping model properties, methods, and utility. The quality of the studies was assessed using an adapted framework for health technology assessments. The findings highlight opportunities for the development of simulation models to support training. However, significant limitations to the existing evidence base limit the feasibility of developing spasticity models based on existing literature.

Computational modeling and simulation (CM&S) is a powerful tool that can be used to support the development, evaluation, and regulatory authorization of medical devices. CM&S can provide valuable insights into device performance, safety, and effectiveness, as well as reduce the need for animal or human testing. Computational models are, however, idealized digital representations that often have many assumptions and need to be credible before they are used in decision making that could incur patient harm. While the medical device community has made great strides to advance the use of CM&S, a number of challenges remain. To begin addressing these challenges, the US Food and Drug Administration (FDA) and the Medical Device Innovation Consortium (MDIC) co-sponsored theFDA/MDIC Symposium on Computational Modeling and Simulationon April 16-17, 2024 in College Park, Maryland, USA, where attendees from around the world convened to hear from leaders in the field through a unique blend of invited presentations and interactive panel discussions. The symposium agenda covered several major themes, including credibility considerations for CM&S used across the medical device total product life cycle, practical examples of performing model credibility assessment, and the use of CM&S for clinical decision making and the emerging areas ofin silicoclinical trials and digital twins. The objective of this article is to summarize the major takeaways of the symposium. We first provide an overview of the invited presentations followed by summaries of the topics covered during the interactive panel discussions. In doing so, we highlight the main takeaways and identify areas in which panelists had shared perspectives or differences of opinion. Next, we present the results of a survey conducted at the symposium that sought attendees' perspectives on different aspects of medical device CM&S. Finally, we conclude by summarizing the major outcomes of the symposium, including areas where more work and investment are needed to advance the field.