Search Results (463)

This paper presents a novel dual-mode memristor-based ring oscillator designed for energy-efficient, wireless biomedical signal conditioning systems. The proposed architecture leverages a compact DTMOS memristor emulator, consisting of only two transistors and one capacitor, to replace the conventional NMOS pull-down devices in a three-stage PMOS ring oscillator. This integration enables two distinct operating modes within a single compact core: a fixed-frequency mode for stable clock generation and carrier synthesis, and a programmable chirp mode for frequency-modulated signal generation. The fixed-frequency mode achieves continuous tuning from 3.142 GHz to 4.017 GHz via varactor control, with an ultra-low power consumption of only 111 µW at 4.017 GHz. The chirp mode generates linear frequency sweeps starting from 0.8 GHz, with the sweep range independently controllable through the state capacitor value and the pulse width of the control signal (SW_Chirp). Designed in a standard 0.18 µm CMOS process, the oscillator exhibits a low phase noise of −87.82 dBc/Hz at a 1 MHz offset for the three-stage configuration, improving to −94.3 dBc/Hz for the five-stage design. The overall frequency coverage spans 0.8–4.017 GHz, representing a 133.6% fractional range. The calculated figure of merit (FoM) is −169.45 dBc/Hz. Experimental validation using a discrete CD4007 prototype confirms the oscillation principle, while comprehensive simulations demonstrate robust performance across process corners and temperature variations. With its zero-static-power memristor core, wide tunability, and dual-mode reconfigurability, the proposed oscillator is ideally suited for multi-standard wireless biomedical applications, including implantable telemetry, neural stimulation, ultra-wideband (UWB) transmitters, and non-contact vital sign monitoring. Full article

Auditory Brain–Computer Interfaces (BCIs) constitute the vital intervention for profound sensorineural hearing loss where the auditory nerve is compromised, yet their clinical efficacy remains restricted by substantial biological bottlenecks and limited spectral resolution. This review critically examines the evolutionary paradigm of auditory restoration, tracing the transition from static physical replacement to dynamic biological symbiosis. We systematically analyze physiological barriers across cochlear, brainstem, and cortical levels, elucidating how rigid interfaces provoke chronic tissue responses and why linear encoding protocols fail in distorted central tonotopy. The article synthesizes emerging methodologies in material science, demonstrating how soft, bio-integrated electronics and biomimetic topologies effectively address mechanical impedance mismatches. Furthermore, the trajectory of neural encoding is evaluated, highlighting the paradigm shift from traditional envelope extraction to deep learning-driven non-linear mapping and adaptive closed-loop neuromodulation. Finally, the potential of high-resolution modulation techniques, including optogenetics and sonogenetics, alongside AI-facilitated intent perception for active listening, is assessed. It is concluded that future neuroprostheses must evolve into symbiotic systems capable of seamlessly integrating with neural plasticity to enable high-fidelity cognitive reconstruction. Full article

Laser powder bed fusion (LPBF) enables controlled gyroid lattices, but mapping both process and design to performance remains challenging when datasets are small and interactions are non-linear. In this study, data-driven models that link energy density and lattice geometry to Young’s modulus and yield strength were established for sheet and network gyroid architectures. To stabilise small-data learning, stacked-autoencoder pre-training was benchmarked against greedy layer-wise pre-training. Compression characterisation data at under-represented energy-density conditions were added to fill data gaps and validate predictions. The models support property-driven design in which given modulus and yield strength targets inform a method that returns feasible combinations of laser powder bed fusion settings and gyroid density and size. Pre-trained models reduced error and captured the relationship between stiffness and density and between strength and density, with yield strength prediction errors of 3.51% for sheet architectures and 8.76% for network architectures. Young’s modulus showed a higher variability that is consistent with sensitivities in LPBF such as surface roughness and thin walls. This work contributes an artificial intelligence method for manufacturing datasets using stacked autoencoder pre-training with fine-tuning, and an inverse-design workflow that maps energy density and gyroid geometry to Young’s modulus and yield strength in titanium lattices. Full article

Objectives: This study aimed to develop and validate a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Vision Transformers (ViT) to automatically classify maxillary sinus membrane morphologies on Cone-Beam Computed Tomography (CBCT) images, distinguishing between Normal, Flat, Polypoid, and Obstruction types. Methods: A dataset of 959 CBCT images was collected and categorized into four morphological classes: Normal, Flat, Polypoid and Obstruction. A custom hybrid model was developed, integrating a lightweight residual CNN for local feature extraction, learnable weighted feature fusion with a bidirectional feature pyramid network and a Transformer encoder for global context modeling. The performance of proposed model was compared against six different architectures, including ResNet50, MobileNetV3L and standard ViT models, using accuracy, precision, recall and F1-score metrics. Results: The proposed hybrid model achieved the highest overall accuracy of 98.44%, outperforming six strong CNN and ViT models including ResNet50 (97.92%) and ViT-B16 (86.46%) models. In class-wise analysis, the model demonstrated superior diagnostic capability, particularly for the “Obstruction” class, achieving 100% accuracy. High discrimination was also observed for “Flat” (98.21%) and “Polypoid” (98.04%) morphologies, confirming the model’s sensitivity to shape-based features. Conclusions: The proposed hybrid CNN-ViT model successfully classifies maxillary sinus membrane morphologies with high accuracy, effectively overcoming the limitations of standard ViT models on limited datasets. Detection of membrane morphology is vital for predicting surgical risks like membrane perforation and post-operative sinusitis. This model serves as a reliable clinical decision support tool, enabling clinicians to objectively assess specific risk factors before implant surgery and sinus floor elevation. Full article

Background/Objectives: A Cochlear implantation (CI) is well-established auditory rehabilitation for severe to profound sensorineural hearing loss (SNHL), yet outcomes vary widely among implantees. Even with advancements in surgical methods and device technology, CI is still commonly applied as a generally uniform procedure, with limited attention to the underlying cause of SNHL. This review aims to summarize current evidence supporting etiology-based personalization of CI and to examine how etiology influences electrode selection, implantation timing, and clinical outcomes. Methods: We reviewed clinical and translational studies focusing on congenital cytomegalovirus infection, genetic hearing loss, cochlear nerve deficiency, and inner-ear malformations, emphasizing how etiology influences cochlear anatomy, neural integrity, and CI outcomes. Results: Etiology significantly affects neural survival, cochlear anatomy, and auditory plasticity, all of which influence optimal electrode design, insertion strategy, and timing of CI. Tailoring CI approaches to specific etiologies may help explain the substantial variability in outcomes observed in both children and adults. Conclusions: CI should be viewed as a precision-based intervention rather than a uniform treatment. Integrating etiology into clinical decision-making is essential for advancing truly personalized CI. Full article

Background/Objectives: Cochlear implants (CIs) constitute a viable method for auditory rehabilitation in patients with profound sensorineural hearing loss after temporal bone fractures (TBFs). These patients comprise a challenging population due to the anatomical deformity and neural injury. Methods: By performing this systematic review, we attempted to evaluate the viability of CIs in the context of TBF. The literature search, across Pubmed/MEDLINE, Scopus and Google Scholar, was performed under the PRISMA guidelines. The selected time period was from December 1995 to September 2025. The final analysis included 11 manuscripts. The majority of the studies were retrospective case series with a moderate risk of bias. Results: The primary outcome was postoperative auditory function, evaluated with speech perception tasks and aided sound-field pure-tone audiometry. The secondary outcomes were the report of radiological and electrophysiologic prognosticators of implants’ viability, timing of surgery, procedural feasibility and complications. Across the studies, CIs conferred meaningful auditory benefit when the cochlear nerve was intact. High-Resolution Computed Tomography (CT) was utilized for TBF classification and cochlear patency, whereas Magnetic Resonance Imaging (MRI) and a promontory test were crucial for the assessment of neural integrity. Prompt placement, optimally within 12 months after trauma, was related to improved outcomes by limiting cochlear fibrosis and ossification. Despite patients’ impedance fluctuation, restricted speech perception in noise and frequent abnormal facial nerve excitation, the overall audiologic and speech discrimination results are comparable to non-trauma recipients. Conclusions: A CI appears to be the choice of treatment over auditory brainstem implants, as long as the cochlear nerve remains intact. Rapid implantation in well-selected patients coupled with ordinal mapping and follow-up can restore dysfunctional hearing and improve patients’ quality of life. Full article

Background/Objectives: It is essential to test the accuracy of artificial intelligence-assisted tools that detect dental pathologies from radiographs. This study aimed to evaluate the test characteristics of an artificial intelligence-assisted convolutional neural network-based prototype used for automated radiographic detection. Methods: A total of 300 panoramic and 100 intraoral periapical radiographs were collected between January 2020 and 2024 and then analyzed by two trained, independent specialist evaluators. The diagnostic consensus, “ground truth”, was labeled as follows: BL: bone loss; C: caries; F: filling; I: implants; IT: impacted teeth; P: prosthesis; PC: post-core; PR: periapical radiolucency; RF: root fillings; and RR: retained roots. The radiographs were uploaded to the prototype, and the results were compared. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated using Stata version 15.0 (StataCorp). Results: Overall, most of the outcomes demonstrated sensitivity greater than 82%, with values ranging from 66.41% (65.47,67.36) for BL to 100% (100.00,100.00) for I. For all outcomes, specificity was greater than 93%, with values ranging from 93.61% (93.12,94.10) for BL to 100% for I. The overall values for all the test characteristics for the periapical radiographs were above 85%. The key errors identified in the qualitative analysis were errors in tooth identification, failure to detect recurrent caries under fillings and crowns, impacted canines, and inaccurate identification of extensive fillings as crowns. Conclusions: The prototype demonstrated high sensitivity and specificity in identifying dental pathologies. Accuracy in identifying bone loss, teeth that have migrated, including impacted canines, secondary caries, and differentiating extensive fillings from crowns requires further improvement. Full article

Background/Objectives: Three types of neuroplasticity that have been reported following hearing aid uptake include: cross-modal reorganization, homologue shifts, and neighbouring region restructuring. However, such evidence primarily stems from cochlear implants and conventional air-conduction hearing aids, leaving a notable gap in research on the neural and neuroplastic consequences of percutaneous bone-anchored hearing aids. The following study aimed to investigate three types of neuroplasticity associated with the integration of bone-conduction hearing aids and resting-state functional connectivity. Methods: Participants (n = 8) came to the lab nine times (i.e., five pre-treatment and four post-treatment), and functional near-infrared spectroscopy (fNIRS) was employed to capture functional brain connectivity between the bilateral superior temporal gyrus (STG), dorsolateral prefrontal cortex (DLPFC), and visual cortex (VC). Results: Across participants, evidence for cross-modal reorganization (between STG and VC) was evident in the left hemisphere. While the presence of homologue shifts and neighbouring region restructuring was detected, these forms of neuroplasticity were much more individualistic. Conclusions: These findings highlight both shared and individualized patterns of neuroplasticity following the uptake of bone-conduction hearing aids, underscoring the importance of considering heterogeneous neural adaptation in auditory rehabilitation research. Full article

Background: Anterior cervical discectomy and fusion (ACDF) is a widely performed procedure for treating degenerative cervical spine conditions. While it effectively addresses neural decompression and restores segmental alignment, the interplay of baseline alignment and implant-specific factors on postoperative segmental alignment remains underexplored. This study evaluates the influence of preoperative cervical alignment and interbody cage design on segmental alignment changes following 1- to 3-level ACDF. Methods: Following institutional review board approval, we identified 258 patients undergoing ACDF for degenerative pathology between 1 January 2010 and 31 December 2023. Preoperative and postoperative radiographs were analyzed for cervical alignment, disc height, and segmental lordosis. Cage dimensions, lordosis, and positioning relative to vertebral landmarks were recorded. Multivariable linear regression models evaluated predictors of postoperative disc height, segmental lordosis, and their respective changes. Results: Postoperative disc height was positively associated with greater cage height (β = 1.13 mm per mm, p < 0.001) and negatively associated with greater cage lordosis (β = −0.10 mm per °, p = 0.001). Segmental lordosis was positively influenced by cage height (β = 0.78° per mm, p = 0.002) and lordosis (β = 0.42° per °, p = 0.002) but was negatively correlated with the distance of the cage from the anterior edge of the cranial vertebra (β = −1.76° per mm, p = 0.004). Greater preoperative segmental kyphosis predicted more significant postoperative lordosis correction (β = −1.07° per °, p < 0.001). Conclusions: This study underscores the importance of preoperative alignment and interbody cage design in achieving optimal segmental correction following ACDF. While cage height primarily drives disc height restoration, surgical technique, particularly anterior placement of the cage, is pivotal for enhancing segmental lordosis. These findings support personalized surgical planning to optimize alignment and patient outcomes. Full article

Introduction: Neurological injuries significantly impair quality of life by disrupting neural transmission. Traditional implantable stimulators often rely on internal batteries, which limit device longevity and necessitate repeated surgical interventions. Objective: This study presents the experimental validation of a battery-free, RFID-powered neural platform for peripheral nerve signal acquisition and stimulation, targeting TRL-6 validation. Methods: The prototype incorporates an adjustable analog front-end with gains up to 93 dB and a biphasic current-controlled stimulator. Validation was performed through benchtop testing, biological tissue assessments using porcine tissue, and functional in vivo trials in adult Wistar rats (n = 3) over a three-month period. Results: Benchtop evaluation confirmed gain accuracy with errors below 2.2 dB and precise stimulation timing. The system maintained a stable 3.3 V wireless power link through 20 mm of biological tissue using RFID. In vivo experiments indicated a 100% functional success rate (51/51 trials) in eliciting gross motor responses via wireless stimulation. Thermal safety was confirmed, with a maximum operating temperature of 28 °C, remaining well below physiological limits. Conclusions: The results demonstrate the functional feasibility of a battery-free, RFID-powered neural interface for wireless signal acquisition and stimulation, supporting system-level validation of this architecture. Full article

The memristor has long been known as a nonvolatile memory technology alternative and has recently been explored for neuromorphic computing, owing to its capability to mimic the synaptic plasticity of the human brain. The architecture of a memristor synapse device allows ultra-high-density integration by internetworking with crossbar arrays, which benefits large-scale training and learning using advanced machine-learning algorithms. In this review, we present a statistical analysis of neuromorphic computing device publications from 2018 to 2025, focusing on various memristive systems. Furthermore, we provide a device-level perspective on biomimetic properties in hardware neural networks such as short-term plasticity (STP), long-term plasticity (LTP), spike timing-dependent plasticity (STDP), and spike rate-dependent plasticity (SRDP). Herein, we highlight the utilization of optoelectronic synapses based on 2D materials driven by a sequence of optical stimuli to mimic the plasticity of the human brain, further broadening the scope of memristor controllability by optical stimulation. We also highlight practical applications ranging from MNIST dataset recognition to hardware-based pattern recognition and explore future directions for memristor synapses in healthcare, including artificial cognitive retinal implants, vital organ interfaces, artificial vision systems, and physiological signal anomaly detection. Full article

Background: Through the direct electrical stimulation of spiral ganglion neurons (SGNs) of the hearing nerve, cochlear implants overcome functionally impaired or missing hair cells in patients with profound to severe hearing loss. In routine clinical fitting, regions with severe local SGN degeneration (modiolar “dead regions”) cannot be identified. As a result, the electrical fields of neighboring electrodes are broadened, which can lead to increased channel interaction and, consequently, poorer speech understanding and hearing. The objective of this study was to ascertain whether neural health status can be evaluated by using cochlear implants’ inbuilt measures. Methods: Electrode impedance (MP1-, MP2-, MP1/MP2-, common ground mode), transimpedance matrix (TIM) and electrically evoked compound action potential (eCAP) measurements were performed before and after laser-induced induction of lesions on the modiolus of the guinea pig. Laser treatment-related shifts in impedance, TIM, and eCAP characteristics (threshold, amplitude, and a modified version of the failure index, referred to as the efficiency index (EI)) were correlated with the histologically assessed damage in three predefined areas of the basal modiolus within the electrode region. Results: Modiolar damage resulted in a significant reduction in the electrode impedance in MP2- and MP1/2-mode, the eCAP amplitude, and the EI. In contrast, TIM values and eCAP thresholds were significantly elevated. MP1, MP1/MP2 electrode impedance, TIM, and the eCAP thresholds were not correlated with the extent of modiolar damage. The shifts in eCAP amplitudes and the EI were significantly correlated with the damage at all regions of the basal modiolus. Conclusions: The eCAP amplitude and the EI are both capable of objectively evaluating the neural health status of the cochlea. Thus, a modiolar dead region could be expected from a local drop in eCAP amplitude values or the modified EI within the electrode array. Full article

Regenerative medicine is increasingly leveraging the synergies between bioinspired conductive biomaterials and 3D/4D bioprinting to replicate the native electroactive and hierarchical microenvironments essential for functional tissue restoration. However, a critical gap remains in the intelligent integration of these technologies to achieve dynamic, responsive tissue regeneration. This review introduces a “bioinspired material–printing–function” triad framework to systematically synthesize recent advances in: (1) tunable conductive materials (polymers, carbon-based systems, metals, MXenes) designed to mimic the electrophysiological properties of native tissues; (2) advanced 3D/4D printing technologies (vat photopolymerization, extrusion, inkjet, and emerging modalities) enabling the fabrication of biomimetic architectures; and (3) functional applications in neural, cardiac, and musculoskeletal tissue engineering. We highlight how bioinspired conductive scaffolds enhance electrophysiological behaviors—emulating natural processes such as promoting axon regeneration cardiomyocyte synchronization, and osteogenic mineralization. Crucially, we identify multi-material 4D bioprinting as a transformative bioinspired approach to overcome conductivity–degradation trade-offs and enable shape-adaptive, smart scaffolds that dynamically respond to physiological cues, mirroring the adaptive nature of living tissues. This work provides the first roadmap toward intelligent electroactive regeneration, shifting the paradigm from static implants to dynamic, biomimetic bioelectronic microenvironments. Future translation will require leveraging AI-driven bioinspired design and organ-on-a-chip validation to address challenges in vascularization, biosafety, and clinical scalability. Full article

Conductive hydrogels have gained considerable interest in the biomedical field because they provide a soft, hydrated, and electrically active microenvironment that closely resembles native tissue. Their unique combination of electrical conductivity and biocompatibility enables monitoring and modulation of biological activities. With the rapid development of conductive hydrogel technologies in recent years, a comprehensive overview is needed to clarify their biological functions and the latest biomedical applications. This review first summarizes the fundamental design strategies, fabrication methods, and conductive mechanisms of conductive hydrogels. We then highlight their applications in wearable device, implanted bioelectronics, wound healing, neural regeneration and cell regulation, accompanied by discussions of the underlying biological and electroactive mechanisms. Potential challenges and future directions, including strategies to optimize fabrication methods, balance key material properties, and tailor conductive hydrogels for diverse biomedical applications, are also highlighted. Finally, we discuss the existing limitations and future perspectives of the biomedical applications of conductive hydrogels. We hope that this article may provide some useful insights to support their further development and potential biomedical applications. Full article

Recent studies show that titanium (Ti)-based alloys combine established mechanical strength, corrosion resistance, and biocompatibility with emerging electrical and electrochemical properties relevant to bioelectronics. The main goal of the present manuscript is to give a wide-ranging overview on the use of Ti-alloys in electronics and biomedicine, focusing on a comprehensive analysis and synthesis of the existing literature to identify gaps and future directions. Concurrently, the identification of possible correlations between the effects of the manufacturing process, alloying elements, and other degrees of freedom influencing the material characteristics are put in evidence, aiming to establish a global view on efficient interdisciplinary efforts to realize high-added-value smart devices useful in the field of biomedicine, such as, for example, implantable apparatuses. This review mostly summarizes advances in surface modification approaches—including anodization, conductive coatings, and nanostructuring that improve conductivity while maintaining biological compatibility. Trends in applications demonstrate how these alloys support smart implants, biosensors, and neural interfaces by enabling reliable signal transmission and long-term integration with tissue. Key challenges remain in balancing electrical performance with biological response and in scaling laboratory modifications for clinical use. Perspectives for future work include optimizing alloy composition, refining surface treatments, and developing multifunctional designs that integrate mechanical, biological, and electronic requirements. Together, these directions highlight the potential of titanium alloys to serve as foundational materials for next-generation bioelectronic medical technologies. Full article