Search Results (305)

The integration of artificial intelligence (AI) and advanced digital workflows is revolutionising full-arch implant dentistry, particularly for geriatric patients with edentulous and atrophic arches, for whom achieving both prosthetic passivity and optimal aesthetic outcomes is critical. This narrative review evaluates current challenges in intraoral scanning accuracy—such as scan distortion, angular deviation, and cross-arch misalignment—and presents how innovations like the Medit SmartX AI-guided workflow and the Scan Ladder system can significantly enhance precision in implant position registration. These technologies mitigate stitching errors by using real-time scan body recognition and auxiliary geometric references, yielding mean RMS trueness values as low as 11–13 µm, comparable to dedicated photogrammetry systems. AI-driven prosthetic design further aligns implant-supported restorations with facial symmetry and smile aesthetics, prioritising predictable midline and occlusal plane control. Early clinical data indicate that such tools can reduce prosthetic misfits to under 20 µm and lower complication rates related to passive fit, while shortening scan times by up to 30% compared to conventional workflows. This is especially valuable for elderly individuals who may not tolerate multiple lengthy adjustments. Additionally, emerging AI applications in design automation, scan validation, and patient-specific workflow adaptation continue to evolve, supporting more efficient and personalised digital prosthodontics. In summary, AI-enhanced scanning and prosthetic workflows do not merely meet functional demands but also elevate aesthetic standards in complex full-arch rehabilitations. The synergy of AI and digital dentistry presents a transformative opportunity to consistently deliver superior precision, passivity, and facial harmony for edentulous implant patients. Full article

Hurricanes are powerful tropical storms that can severely damage mangrove forests through uprooting trees, sediment erosion, and saltwater intrusion, disrupting their critical role in coastal protection and biodiversity. After a hurricane, evaluating mangrove damage helps prioritize rehabilitation efforts, as these ecosystems play a key ecological role in coastal regions. Thus, we analyzed the defoliation of mangrove forest canopies and their early recovery, approximately 2.5 years after the landfall of Category 3 Hurricane Roslyn in October 2002 in Marismas Nacionales, Mexico. The following mangrove traits were analyzed: (1) the yearly time series of the Combined Mangrove Recognition Index (CMRI) standard deviation from 2020 to 2025, (2) the CMRI rate of change (slope) following the hurricane’s impact, and (3) the canopy height model (CHM) before and after the hurricane using satellite and UAV-LiDAR data. Hurricane Roslyn caused a substantial decrease in canopy cover, resulting in a loss of 47,202 ha, which represents 82.8% of the total area of 57,037 ha. The CMRI standard deviation indicated early signs of canopy recovery in one-third of the mangrove-damaged areas 2.5 years post-impact. The CMRI slope indicated that areas near the undammed rivers had a maximum recovery rate of 0.05 CMRI units per month, indicating a predicted canopy recovery of ~2.5 years. However, most mangrove areas exhibited CMRI rates between 0.01 and 0.03 CMRI units per month, anticipating a recovery time between 40 months (approximately 3.4 years) and 122 months (roughly 10 years). Unfortunately, most of the already degraded Laguncularia racemosa forests displayed a negative CMRI slope, suggesting a lack of canopy recovery so far. Additionally, the CHM showed a median significant difference of 3.3 m in the canopy height of fringe-type Rhizophora mangle and Laguncularia racemosa forests after the hurricane’s landfall. Full article

(1) Background: A severe decline in knee joint function significantly affects the mobility of the elderly, making it a key concern in the field of geriatric health. To alleviate the pressure on the knee joints of the elderly during daily movements such as sitting and standing, effective biomechanical solutions are required. (2) Methods: In this study, a biomechanical framework was established based on mechanical analysis to derive the transfer relationship between the ground reaction force and the knee joint moment. Experiments were designed to collect knee joint data on the elderly during the sit-to-stand process. Meanwhile, magnetic resonance imaging (MRI) images were processed through a medical imaging control system to construct a detailed digital 3D knee joint model. A finite element analysis was used to verify the model to ensure the accuracy of its structure and mechanical properties. An improved radial basis function was used to fit the pressure during the entire sit-to-stand conversion process to reduce the computational workload, with an error of less than 5%. In addition, a small-target human key point recognition network was developed to analyze the image sequences captured by the camera. The knee joint angle and the knee joint pressure distribution during the sit-to-stand conversion process were mapped to a three-dimensional interactive platform to form a digital twin system. (3) Results: The system can effectively capture the biomechanical behavior of the knee joint during movement and shows high accuracy in joint angle tracking and structure simulation. (4) Conclusions: This study provides an accurate and comprehensive method for analyzing the biomechanical characteristics of the knee joint during the movement of the elderly, laying a solid foundation for clinical rehabilitation research and the design of assistive devices in the field of rehabilitation medicine. Full article

Human Activity Recognition (HAR) using wearable sensor data is increasingly important in healthcare, rehabilitation, and smart monitoring. This study systematically compared three dynamic neural network architectures—Finite Impulse Response Neural Network (FIRNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—to examine their suitability and specificity for HAR tasks. A controlled experimental setup was applied, training 16,500 models across different delay lengths and hidden neuron counts. The investigation focused on classification accuracy, computational cost, and model interpretability. LSTM achieved the highest classification accuracy (98.76%), followed by GRU (97.33%) and FIRNN (95.74%), with FIRNN offering the lowest computational complexity. To improve model transparency, Layer-wise Relevance Propagation (LRP) was applied to both input and hidden layers. The results showed that gyroscope Y-axis data was consistently the most informative, while accelerometer Y-axis data was the least informative. LRP analysis also revealed that GRU distributed relevance more broadly across hidden units, while FIRNN relied more on a small subset. These findings highlight trade-offs between performance, complexity, and interpretability and provide practical guidance for applying explainable neural wearable sensor-based HAR. Full article

This paper presents a Self-Learning Robotic System (SLRS) for healthcare assistance using Deep Imitation Learning (DIL). The proposed SLRS solution can observe and replicate human demonstrations, thereby acquiring complex skills without the need for explicit task-specific programming. It incorporates modular components for perception (i.e., advanced computer vision methodologies), actuation (i.e., dynamic interaction with patients and healthcare professionals in real time), and learning. The innovative approach of implementing a hybrid model approach (i.e., deep imitation learning and pose estimation algorithms) facilitates autonomous learning and adaptive task execution. The environmental awareness and responsiveness were also enhanced using both a Convolutional Neural Network (CNN)-based object detection mechanism using YOLOv8 (i.e., with 94.3% accuracy and 18.7 ms latency) and pose estimation algorithms, alongside a MediaPipe and Long Short-Term Memory (LSTM) framework for human action recognition. The developed solution was tested and validated in healthcare, with the aim to overcome some of the current challenges, such as workforce shortages, ageing populations, and the rising prevalence of chronic diseases. The CAD simulation, validation, and verification tested functions (i.e., assistive functions, interactive scenarios, and object manipulation) of the system demonstrated the robot’s adaptability and operational efficiency, achieving an 87.3% task completion success rate and over 85% grasp success rate. This approach highlights the potential use of an SLRS for healthcare assistance. Further work will be undertaken in hospitals, care homes, and rehabilitation centre environments to generate complete holistic datasets to confirm the system’s reliability and efficiency. Full article

Background/Objectives: This exploratory study examined the potential effectiveness of cognitive enhancement interventions targeting basic cognitive prerequisites and communicative abilities in girls with Rett syndrome. Special attention was given to evaluating telerehabilitation as a feasible alternative to traditional in-person therapy, particularly for individuals with severe impairments and limited access to care. Methods: Twenty-four girls diagnosed with Rett syndrome (mean age = 13.7 years, SD = 7.1), all meeting the basic cognitive prerequisites defined by the GAIRS scale, were randomly assigned to two groups: a telerehabilitation group (n = 12) and an in-person rehabilitation group (n = 12). Interventions were delivered in school settings and focused on two core areas: basic cognitive skills (e.g., object recognition, spatial and temporal concepts, form and color discrimination, and cause–effect reasoning) and communication skills (e.g., comprehension and expression through gestures, images, or verbal output). Results: Both groups showed significant improvements in the cognitive and communicative domains, with generally comparable outcomes. Notably, the telerehabilitation group demonstrated relatively greater gains in verbal expression and cause–effect understanding. Correlational analyses indicated positive associations between the cognitive and communicative improvements, particularly between spatial understanding and expressive abilities. However, these findings should be interpreted with caution due to the sample size and study design limitations. Conclusions: These preliminary findings suggest that cognitive enhancement programs may support developmental gains in girls with Rett syndrome and that telerehabilitation could represent a viable alternative for those unable to access in-person care. Given the limited sample size and absence of qualitative measures, further research is necessary to validate its effectiveness and understand its role within comprehensive care models. Full article

Background/Objectives: Menière’s disease (MD) is a debilitating disorder with episodic and variable ear symptoms. Diagnosis can be challenging, and evidence for therapeutic approaches is low. Furthermore, patients show a unique and fluctuating configuration of audiovestibular impairment. As a psychometric instrument to assess hearing-specific disability is currently lacking, we evaluated a short form of the Speech, Spatial, and Qualities of Hearing Scale (SSQ) in a cohort of patients with MD. Methods: Data was collected in the context of a multicenter prospective patient registry intended for the long-term follow up of MD patients. Hearing was assessed by pure tone and speech audiometry. The SSQ was applied in the German language version with 17 items. Results: In total, 97 consecutive patients with unilateral MD with a mean age of 56.2 ± 5.0 years were included. A total of 55 individuals (57.3%) were female, and 72 (75.0%) were categorized as having definite MD. The average total score of the SSQ was 6.0 ± 2.1. Cronbach’s alpha for internal consistency was 0.960 for the total score. We did not observe undue floor or ceiling effects. SSQ values showed a statistically negative correlation with hearing thresholds and a statistically positive correlation with speech recognition scores of affected ears. Conclusions: The short form of the SSQ provides insight into hearing-specific disability in patients with MD. Therefore, it may be informative regarding disease stage and rehabilitation needs. Full article

The integration of digital serious games into speech learning (rehabilitation) has demonstrated significant potential in enhancing accessibility and inclusivity for children with speech disabilities. This review of the state of the art examines the role of serious games, Artificial Intelligence (AI), and Natural Language Processing (NLP) in speech rehabilitation, with a particular focus on interaction modalities, engagement autonomy, and motivation. We have reviewed 45 selected studies. Our key findings show how intelligent tutoring systems, adaptive voice-based interfaces, and gamified speech interventions can empower children to engage in self-directed speech learning, reducing dependence on therapists and caregivers. The diversity of interaction modalities, including speech recognition, phoneme-based exercises, and multimodal feedback, demonstrates how AI and Assistive Technology (AT) can personalise learning experiences to accommodate diverse needs. Furthermore, the incorporation of gamification strategies, such as reward systems and adaptive difficulty levels, has been shown to enhance children’s motivation and long-term participation in speech rehabilitation. The gaps identified show that despite advancements, challenges remain in achieving universal accessibility, particularly regarding speech recognition accuracy, multilingual support, and accessibility for users with multiple disabilities. This review advocates for interdisciplinary collaboration across educational technology, special education, cognitive science, and human–computer interaction (HCI). Our work contributes to the ongoing discourse on lifelong inclusive education, reinforcing the potential of AI-driven serious games as transformative tools for bridging learning gaps and promoting speech rehabilitation beyond clinical environments. Full article

Introduction: Methadone, a synthetic opioid used for opioid substitution therapy (OST), is typically associated with arrhythmias rather than direct myocardial depression. Neurological complications, especially with concurrent antipsychotic use, have also been reported. Acute left ventricular failure in young adults is uncommon and often linked to genetic or infectious causes. We present a rare case of reversible cardiogenic shock and cerebellar insult due to methadone toxicity. Case Presentation: A 37-year-old man with a history of drug abuse on OST with methadone (130 mg/day) was admitted to the ICU with hemodynamic instability, seizures, and focal neurological deficits. Diagnostic workup revealed low cardiac output syndrome and a right cerebellar insult, attributed to methadone toxicity. The patient received individualized catecholamine support. After 10 days in the ICU, he was transferred to a general ward for ongoing cardiac and neurological rehabilitation and discharged in stable condition seven days later. Conclusions: Methadone-induced reversible left ventricular failure, particularly when accompanied by cerebellar insult, is rare but potentially life-threatening. Early recognition and multidisciplinary management are essential for full recovery in such complex toxicological presentations. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

Hand motion monitoring plays a vital role in medical rehabilitation, sports training, and human–computer interaction. High-sensitivity wearable biosensors are essential for accurate gesture recognition and precise motion analysis. In this work, we propose a high-sensitivity wearable glove based on a push–pull optical fiber sensor, designed to enhance the sensitivity and accuracy of hand motion biosensing. The sensor employs diagonal core reflectors fabricated at the tip of a four-core fiber, which interconnect symmetric fiber channels to form a push–pull sensing mechanism. This mechanism induces opposite wavelength shifts in fiber Bragg gratings positioned symmetrically under bending, effectively decoupling temperature and strain effects while significantly enhancing bending sensitivity. Experimental results demonstrate superior bending-sensing performance, establishing a solid foundation for high-precision gesture recognition. The integrated wearable glove offers a compact, flexible structure and straightforward fabrication process, with promising applications in precision medicine, intelligent human–machine interaction, virtual reality, and continuous health monitoring. Full article

Introduction: This study aimed to compare different scoring methods, such as phoneme, syllable, and word-based scoring, during word recognition in noise testing and their interaction with hearing loss severity. These scoring methods provided a structured framework for refining clinical audiological diagnosis by revealing underlying auditory processing at multiple linguistic levels. We highlight how scoring differences inform differential diagnosis and guide targeted audiological interventions. Methods: Pure tone audiometry and word-in-noise testing were conducted on 100 subjects with a wide range of hearing loss severity. Speech recognition was scored using phoneme, syllable, and word-based methods. All procedures were designed to reflect standard diagnostic protocols in clinical audiology. Discriminant function analysis examined how these scoring methods differentiate the degree of hearing loss. Results: Results showed that each method provides unique information about auditory processing. Phoneme-based scoring has pointed out basic auditory discrimination; syllable-based scoring can capture temporal and phonological processing, while word-based scoring reflects real-world listening conditions by incorporating contextual knowledge. These findings emphasize the diagnostic value of each scoring approach in clinical settings, aiding differential diagnosis and treatment planning. Conclusions: This study showed the effect of different scoring methods on hearing loss differentiation concerning severity. We recommend the integration of phoneme-based scoring into standard diagnostic batteries to enhance early detection and personalize rehabilitation strategies. Future research must involve studies about integration with other speech perception tests and applicability across different clinical settings. Full article

Bardet–Biedl syndrome (BBS) is a rare multisystem ciliopathy characterized by early-onset retinal degeneration and other vision-threatening ophthalmologic manifestations. This review synthesizes current knowledge on the ocular phenotype of BBS as well as emerging therapeutic approaches aimed at preserving visual function. Retinal degeneration, particularly early macular involvement and rod–cone dystrophy, remains the hallmark of BBS-related vision loss. Additional ocular manifestations, such as refractive errors, nystagmus, optic nerve abnormalities, and cataracts further contribute to visual morbidity. Experimental therapies—including gene-based interventions and pharmacologic strategies such as nonsense suppression and antioxidant approaches—have shown promise in preclinical models but require further validation. Early ophthalmologic care, including routine visual assessments, refractive correction, and low-vision rehabilitation, remains the standard of management. However, there are currently no effective therapies to halt or reverse retinal degeneration, which underscores the importance of emerging molecular and genetic interventions. Timely recognition and comprehensive ophthalmologic evaluation are essential to mitigate visual decline in BBS. Future efforts should focus on translating these approaches into clinical practice, enhancing early diagnosis, and promoting multidisciplinary collaboration to improve long-term outcomes for patients with BBS. Full article

Background/Objectives: This study explores the integration of wearable sensors and artificial intelligence (AI) for Human Activity Recognition (HAR) in the diagnosis and rehabilitation of Parkinson’s disease (PD). The objective was to develop a proof-of-concept model based on internal reproducibility, without external generalization, that is capable of distinguishing pathological movements from healthy ones while ensuring clinical relevance and patient safety. Methods: Nine subjects, including eight patients with Parkinson’s disease and one healthy control, were included. Motion data were collected using the Motigravity platform, which integrates inertial sensors in a controlled environment. The signals were automatically segmented into fixed-length windows, with poor-quality segments excluded through preprocessing. A hybrid CNN-LSTM (Convolutional Neural Networks—Long Short-Term Memory) model was trained to classify motion patterns, leveraging convolutional layers for spatial feature extraction and LSTM layers for temporal dependencies. The Motigravity system provided a controlled hypogravity environment for data collection and rehabilitation exercises. Results: The proposed CNN-LSTM model achieved a validation accuracy of 100%, demonstrating classification potential. The Motigravity system contributed to improved data reliability and ensured patient safety. Despite increasing class imbalance in extended experiments, the model consistently maintained perfect accuracy, suggesting strong generalizability after external validation to overcome the limitations. Conclusions: Integrating AI and wearable sensors has significant potential to improve the HAR-based classification of movement impairments and guide rehabilitation strategies in PD. While challenges such as dataset size remain, expanding real-world validation and enhancing automated segmentation could further improve clinical impact. Future research should explore larger cohorts, extend the model to other neurodegenerative diseases, and evaluate its integration into clinical rehabilitation workflows. Full article

Background/Objectives: Rehabilitation success with a cochlear implant (CI) varies considerably and identifying predictive factors for the reliable prediction of speech understanding with CI remains a challenge. Hoppe and colleagues have recently described a predictive model, which was specifically based on Cochlear™ recipients with a four-frequency pure tone average (4FPTA) ≤ 80 dB HL. The aim of this retrospective study is to test the applicability to an independent patient cohort with extended inclusion criteria. Methods: The Hoppe et al. model was applied to CI recipients with varying degrees of hearing loss. Model performance was analyzed for Cochlear™ recipients with 4FPTA ≤ 80 dB HL and for all recipients regardless of 4FPTA. Subgroup analyses were conducted by WRS_max and CI manufacturer. Results: The model yielded comparable results in our patient cohort when the original inclusion criteria were met (n = 24). Extending the model to patients with profound hearing loss (4FPTA > 80 dB HL; n = 238) resulted in a weaker but significant correlation (r = 0.273; p < 0.0001) between predicted and measured word recognition score at 65 dB with CI (WRS₆₅(CI)). Also, a higher percentage of data points deviated by more than 20 pp, either better or worse. When patients provided with CIs from different manufacturers were enrolled, the prediction error was also higher than in the original cohort. In Cochlear™ recipients with a maximum word recognition score (WRS_max) > 0% (n = 83), we found a moderate correlation between measured and predicted scores (r = 0.3274; p = 0.0025). Conclusions: In conclusion, as long as the same inclusion criteria are used, the Hoppe et al. (2021) prediction model results in similar prediction success in our cohort, and thus seems applicable independently of the cohort used. Nevertheless, it has limitations when applied to a broader and more diverse patient cohort. Our data suggest that the model would benefit from adaptations for broader clinical use, as the model lacks sufficient sensitivity in identifying poor performers. Full article