Biomedical Engineering Letters最新文献

This study proposes a visualization and analysis method for eye blinking pattern using high-frame-rate videos. The high-frame-rate video clips for visualization are taken using a camera without additional equipment. The partial video clips of eye blinking except for eyelid flutters and microsleeps are extracted from the entire video clip. The changes in shapes and positions of the upper eyelid during the eye blinking sequences are evaluated, and each eye blinking is visualized as a single image. The various parameters regarding eye blinking are calculated to analyze blinking patterns. The single eye blinking sequence is divided into phases to analyze and classify eye blinking patterns in more detail. In this experiment conducted on 80 volunteers, the proposed method was able to quantitatively analyze eyelid movements, and various parameters related to eye blinking were calculated. Additionally, different types of eye blinking patterns were visualized as graph images, and incomplete eye blinking and consecutive eye blinking were defined and detected. The proposed method can overcome the spatial and situational limitations of conventional bio-signal analysis methods, as it allows non-contact measurement in ordinary environments. In addition, since quantitative eye blink data obtained from high-frame-rate video contain more information than data obtained from bio-signals, it is expected that analysis methods using videos can be easily applied to a wider range of fields.

As artificial intelligence (AI) becomes increasingly central to modern healthcare, medical education must move beyond passive knowledge transfer and adopt a system-wide approach to convergence training. This narrative review shares a 5-year case study from Seoul National University College of Medicine (SNU Medicine), which developed a comprehensive, multi-level model for integrating AI into medical education. Instead of relying on pilot programs or piecemeal curriculum updates, SNU Medicine established a governance-driven, modular framework that includes institutional infrastructure, interdisciplinary teaching strategies, cross-campus credit integration, and alignment with national digital health policies. Based on this long-term case, we propose four key design principles-modularity, transdisciplinary alignment, infrastructure-curriculum coupling, and policy embeddedness-as a framework for creating scalable and sustainable convergence education in medical AI. While rooted in Korea's unique policy environment, this model provides transferable insights for medical institutions worldwide, particularly those operating within public or policy-constrained environments.

This review article focuses on recent advancements and persistent challenges in artificial vision prostheses designed to restore sight for patients affected by retinal diseases. It comprehensively examines various approaches, including epiretinal, subretinal, and suprachoroidal implants, as well as optic nerve and visual cortex stimulation strategies. The critical role of the retina in visual perception is explored, emphasizing how retinal degeneration affects the transmission of visual information and how artificial devices aim to replicate this function. The review also discusses the technological complexities of artificial retina development, particularly challenges associated with enhancing resolution, minimizing the spread of electrical stimulation, and achieving reliable long-term device functionality within the biological environment. Practical clinical outcomes, such as surgical feasibility, device durability, and biocompatibility, are analyzed in light of these innovations. Furthermore, emerging trends are highlighted, including the adoption of flexible materials, photovoltaic structures, and 3D electrode architectures to improve the performance and longevity of implants. Ultimately, future advancements in artificial vision systems will depend on integrated approaches that combine cutting-edge engineering with a deep understanding of biological systems to achieve meaningful and lasting visual restoration.

China's healthcare infrastructure faces growing population pressure and resource gaps. This review explores how AI applications, regulatory frameworks, and commercialization pathways are reshaping China's healthcare delivery system and global innovation standards. China's AI healthcare market is expected to grow from $900 million in 2020 to $1.59 billion in 2023, and is expected to reach $18.88 billion by 2030, at a Compound Annual Growth Rate (CAGR) of 42.5%. The National Medical Products Administration (NMPA) expects to approve 59 Class III AI devices by 2023, compared with just nine in 2020. Key applications include the widespread use of AI technology in lesion identification; a telemedicine platform serving 13 million users; and AI drug development that shortens the development cycle from 4 to 18 months. Regulatory pillars include the Personal Information Protection Law, which requires explicit consent before processing health data, and NMPA guidelines, which require all AI medical software to undergo three types of review. China's unique combination of centralized health data, policy incentives, and rapid commercialization has created a globally competitive AI medical ecosystem. Continued development requires addressing issues such as algorithm transparency, cross-border data governance, and international regulatory coordination.

The function of our brain is the result of the balanced interplay between billions of neurons forming a network of enormous complexity. However, the neurons themselves are also immensely complex entities, with many specialized macromolecular structures orchestrating signal processing and propagation. The postsynaptic density is an elaborate network of interconnected proteins, a dynamic yet highly organized molecular assembly beneath the dendritic membrane, and plays a pivotal role in learning, memory formation, and the development of a number of cognitive disorders. In this review, we argue that with the recent blooming of AI-assisted computational tools in structural biology, we might be able to get closer to understanding the molecular-level mechanistic aspects of this machinery. Nevertheless, we have to use these methods with caution as they are not yet capable of solving all the questions that arise for such a complex macromolecular system. First, we focus on the unique features of the postsynaptic protein network, highlighting those that pose particular challenges for such a modeling task, and put these in the light of the currently available deep learning-based approaches. We highlight the aspects that need specific attention and the areas where future developments could facilitate the detailed description of neural function at the molecular level.

Microplastics have become ubiquitous in modern environments, entering the human body through multiple pathways, including air, water, and food. Recent evidence shows that microplastics penetrate deep into the human body and accumulate in tissues. Despite escalating exposure to microplastics and growing concerns about potential toxicity, strategies for microplastic clearance from the body have yet to be explored. This review summarizes current knowledge on exposure pathways, distribution, accumulation mechanisms, and health risks of microplastics and critically evaluates natural clearance mechanisms in human and their limitations. Further, we investigate potential biomedical strategies for microplastic clearance and detoxification and synthesize considerations for clinical translation.

The integration of smartphones, wearable devices, and artificial intelligence (AI) has revolutionized mental health diagnostics, particularly for depression and anxiety, by enabling real-time data collection and early intervention. This review synthesizes the findings from recent studies on the use of these technologies for diagnostic precision and predictive modeling. Following the for Systematic Reviews and Preferred Reporting Items Meta-Analyses guidelines, a systematic search of PubMed, Scopus, and Web of Science was conducted for publications up to April 2025, resulting in the inclusion of 62 relevant studies. Our critical analysis revealed that, while artificial intelligence demonstrates high accuracy in detecting mental health symptoms, its performance is highly context-dependent. We examined significant challenges, including the lack of generalizability owing to disparate datasets, the critical yet often unstandardized role of feature engineering, and the "black box" nature of complex algorithms that hinder clinical trust. Addressing these limitations requires interdisciplinary collaboration, robust ethical and regulatory frameworks (e.g., GDPR and HIPAA), and scalable interpretable solutions. Future research must prioritize long-term validation, inclusivity across diverse populations, and development of explainable AI to bridge the gap between technological potential and clinical reality.

Osteoarthritis is the most common degenerative joint disease and a major cause of reduced physical function and the quality of life. Proper application of ultrasound has been proven to be effective for non-invasive osteoarthritis treatment. With a 2-D array transducer, spatial focusing of treatment pulses and three-dimensional (3-D) imaging of cartilage structures and intra-articular soft tissue are feasible for more effective treatment and diagnosis. However, supporting both imaging and therapy with a single 2-D ultrasound transducer is challenging due to the physical limitations caused by the array geometry. Given the number of active channels, increasing the element pitch can improve the lateral or elevational resolution and treatment efficacy, but introduces the grating lobe artifacts, degrading the overall image quality. To utilize a 2-D array configured with relatively large pitch elements for both 3-D imaging and low-frequency treatment, this study proposes a 3-D sub-pitch plane-wave imaging method. This method acquires channel RF data by physically translating the 2-D array transducer in the elevational and lateral directions and synthesizes all acquired RF data to reconstruct the single image, effectively maintaining the resolution while reducing grating lobe artifacts. We have demonstrated effective reduction in grating lobes through beam pattern analysis and quantitatively evaluated the imaging capabilities by Field II simulations and in-vitro experiments using a 2-D array with 8 × 8 elements centered at 2 MHz with 55% fractional bandwidth. These results could suggest that our approach may be useful in a theranostic ultrasound system supporting both treatment and diagnosis of osteoarthritic diseases.

Augmented reality (AR) has emerged as a powerful tool for enhancing human spatial awareness by overlaying digital information onto the physical world. This paper presents a review of the methodologies that enable AR-based spatial perception, with a focus on challenging environments such as underwater and disaster scenarios. We review state-of-the-art deep learning approaches for 3D data interpretation and completion, including voxel-based, point-based, and view-based methods. As part of this review, we implement an AR-enabled spatial awareness system, where the investigated deep learning solutions can be tested directly. In our approach, a robotic arm with an ultrasound sensor performs 2D scans underwater, from which a 3D point cloud of the scene is reconstructed. Using the reviewed deep learning networks, the point cloud is segmented in order to identify objects of interest, and point cloud completion is performed to infer missing structure. We report experimental results from synthetic data and underwater scanning trials, demonstrating that the system can recover and augment unseen spatial information for the user. We discuss the outcomes, including segmentation accuracy and completeness of reconstructions, as well as challenges such as data scarcity, noise, and real-time constraints. The paper concludes that, when combined with robust sensing and 3D deep learning techniques, AR enhances human spatial awareness in environments where direct perception is limited. The need for more adequate metrics to describe point clouds and for more labeled sonar datasets is discussed.

Accurate, non-invasive prediction of muscle fatigue and coordination is essential for improving exercise performance and rehabilitation strategies. This study proposed a deep learning-based algorithm that integrates surface electromyography (EMG) and markerless motion analysis to estimate muscle fatigue and intermuscular coordination during dynamic upper-limb movement. Five healthy male participants (age: 26 ± 1.73 years) performed one-arm dumbbell curls at 50% of their one-repetition maximum (1RM), during which EMG signals were collected from the biceps brachii and lateral deltoid. Muscle fatigue was evaluated using median frequency (MDF) separately for each muscle, while intermuscular coordination was quantified via the Synergy Activation Ratio (SAR), derived from non-negative matrix factorization (NMF). Markerless motion data were captured using a Kinect V2 sensor, and both EMG and motion data were used to train an LSTM model. The model demonstrated high prediction accuracy (MDF: MSE 0.0081, MAE 0.0664 for biceps; MSE 0.0102, MAE 0.0728 for deltoid; SAR: MSE 0.0366, MAE 0.1230). Results showed a decline in biceps MDF across sets, indicating localized fatigue, while the deltoid exhibited increased MDF, possibly reflecting compensatory or inefficient activation. SAR values decreased over time, suggesting fatigue-induced reorganization of muscle synergy and increased reliance on stabilizer muscles. These findings demonstrate the feasibility of using LSTM models with synchronized EMG and motion data to detect both localized fatigue and coordination changes in real-time. The proposed framework may support future applications in personalized training, fatigue monitoring, and ergonomic assessment.