Search Results (477)

Background: Central precocious puberty (CPP), defined as the onset of secondary sexual characteristics before age 8 in girls, is increasingly prevalent worldwide. CPP is often caused by early activation of the HPG axis, leading to accelerated growth and bone maturation. However, the diagnostic accuracy of standard bone age (BA) methods remains uncertain in this context. Objective: To compare the diagnostic accuracy of the Greulich–Pyle atlas (GPA) and Tanner–Whitehouse 3 (TW3) methods in estimating skeletal age in girls with CPP and to assess the predictive value of serum hormone levels for estimating chronological age (CA). Methods: An observational, cross-sectional diagnostic study was conducted, involving n = 109 girls aged 6–12 years with confirmed CPP (Ethics Committee approval: CHUC_2023_86; 13 July 2023). Left posteroanterior hand–wrist (PA–HW) radiographs were assessed using the GPA and TW3 methods. Anthropometric measurements were recorded, and serum concentrations of estradiol, LH, FSH, DHEA-S, cortisol, TSH, and free T4 were obtained. Comparisons between CA and BA estimates were conducted using repeated-measures ANOVA, and ANCOVA was applied to examine the hormonal predictors of CA. Results: Both GPA and TW3 overestimated CA between 7 and 12 years, with the GPA showing larger deviations (up to 4.8 months). The TW3 method provided more accurate estimations, particularly at advanced pubertal stages. Estradiol (η²_p = 0.188–0.197), LH (η²_p = 0.061–0.068), and FSH (η²_p = 0.008–0.023) emerged as the strongest endocrine predictors of CA, significantly enhancing the explanatory power of both radiological methods. Conclusions: The TW3 method demonstrated superior diagnostic accuracy over GPA in girls with CPP, especially between 7 and 12 years. Integrating estradiol, LH, and FSH into BA assessment significantly improved the accuracy, supporting a more individualized and physiologically grounded diagnostic approach. Full article

Background/Objectives: Growth prediction may be used by the clinical orthodontist in growing individuals for diagnostic purposes and for treatment planning. This process appraises chronological age and determines the degree of skeletal maturity to calculate residual growth. In developmental deviations, overlooking such diagnostic details might culminate in erroneous conclusions, unstable outcomes, recurrence, and treatment failure. The present review aims to systematically present and explain the available means for predicting growth in humans. Traditional, long-known, popular methods are discussed, and modern digital applications are described. Materials and methods: A search on PubMed and the gray literature up to May 2025 produced 69 eligible studies on future maxillofacial growth prediction without any orthodontic intervention. Results: Substantial variability exists in the studies on growth prediction. In young orthodontic patients, the study of the lateral cephalometric radiography and the subsequent calculation of planes and angles remain questionable for diagnosis and treatment planning. Skeletal age assessment is readily accomplished with X-rays of the cervical vertebrae and the hand–wrist region. Computer software is being implemented to improve the reliability of classic methodologies. Metal implants have been used in seminal growth studies. Biochemical methods and electromyography have been suggested for clinical prediction and for research purposes. Conclusions: In young patients, it would be of importance to reach conclusions on future growth with minimal distress to the individual and, also, reduced exposure to ionizing radiation. Nevertheless, the potential for comprehensive prediction is still largely lacking. It could be accomplished in the future by combining established methods with digital technology. Full article

With the growing use of head-mounted displays (HMDs) such as smart glasses, text input remains a challenge, especially in mobile environments. Conventional methods like physical keyboards, voice recognition, and virtual keyboards each have limitations—physical keyboards lack portability, voice input has privacy concerns, and virtual keyboards struggle with accuracy due to a lack of tactile feedback. FlickPose is a novel text input system designed for smart glasses and mobile HMD users, integrating flick-based input and hand pose recognition. It features two key selection methods: the touch-panel method, where users tap a floating UI panel to select characters, and the raycast method, where users point a virtual ray from their wrist and confirm input via a pinch motion. FlickPose uses five left-hand poses to select characters. A machine learning model trained for hand pose recognition outperforms Random Forest and LightGBM models in accuracy and consistency. FlickPose was tested against the standard virtual keyboard of Meta Quest 3 in three tasks (hiragana, alphanumeric, and kanji input). Results showed that raycast had the lowest error rate, reducing unintended key presses; touch-panel had more deletions, likely due to misjudgments in key selection; and frequent HMD users preferred raycast, as it maintained input accuracy while allowing users to monitor their text. A key feature of FlickPose is adaptive tracking, which ensures the keyboard follows user movement. While further refinements in hand pose recognition are needed, the system provides an efficient, mobile-friendly alternative for HMD text input. Future research will explore real-world application compatibility and improve usability in dynamic environments. Full article

Upper-limb impairments after stroke significantly affect patients’ quality of life and require effective rehabilitation strategies. Rehabilitation devices play a vital role in enhancing motor recovery. This study evaluated the efficacy of the Arm Booster, a bilateral arm-training device, in improving upper-limb impairment in patients with chronic stroke. Eighteen participants were randomly assigned to two groups: a device group (n = 9), using the Arm Booster; and a conventional physiotherapy group (n = 9). Both groups performed six bilateral upper-limb exercises (32 repetitions each) three times per week for eight weeks. Participants were further classified into mild spasticity (n = 5) and moderate-to-severe spasticity (n = 4) subgroups. The primary outcome was motor impairment, assessed using the Fugl-Meyer Assessment of the Upper Extremity (FMA-UE). Secondary outcomes included spasticity, measured by the Modified Ashworth Scale (MAS), and daily functional use of the arm, assessed with the Motor Activity Log (MAL). Both groups showed significant improvements in FMA-UE scores and overall arm movement. The conventional group demonstrated additional gains in hand and wrist function and coordination. Notably, in the moderate-to-severe spasticity subgroup, the device group exhibited improvements in upper-limb movement and a trend toward reduced spasticity. These findings suggest that the Arm Booster may support motor recovery, encourage the use of the affected arm, improve movement control, and provide an efficient means for patients to exercise more frequently on their own. Full article

The complex structure of the wrist joint supports the hand to complete a variety of dexterous and accurate operations in daily living, which in turn makes it vulnerable to motor injury due to stroke, sports, occupational, or traffic accidents. As a supplement to traditional medical treatment, timely and effective rehabilitation training can accelerate the recovery process of wrist motor function. The wearable rehabilitation device in this work exhibits excellent application prospects in the field of human rehabilitation training due to its inherent flexibility and safety. Inspired by the motion principle of tendons and muscles, a modular vacuum-actuated artificial muscle (VAM) is proposed, with the advantages of being lightweight and having a high contraction ratio. The VAMs are applied to the development of a wearable wrist rehabilitation device (WWRD) prototype, which can realize wrist rehabilitation training in the motion directions of extension, flexion, radial deviation, and ulnar deviation. The design concept, structural model, and motion analysis of a WWRD are introduced to provide a reference for the design and analysis of the WWRD prototype. To evaluate the performance of the WWRD, we establish the force and motion parameter models of the WWRD and carry out performance experiments. The process of wrist rehabilitation training is tested and evaluated, which indicates that the WWRD with VAMs will enhance flexibility, comfort, and safety in wrist rehabilitation training. This work is expected to promote the development of high-performance wearable wrist rehabilitation devices based on an understanding of the bionic vacuum-actuated artificial muscles. Full article

Accelerometers are nowadays included in almost any portable or mobile device, including smartphones, smartwatches, wrist-bands, and even smart rings. The data collected from them is therefore an ideal candidate to tackle human motion recognition, as it can easily and unobtrusively be acquired. In this work we analyze the performance of a hand-gesture classification system implemented using LSTM neural networks on a resource-constrained microcontroller platform, which required trade-offs between network accuracy and resource utilization. Using a publicly available dataset, which includes data for 20 different hand gestures recorded from 10 subjects using a wrist-worn device with a 3-axial accelerometer, we achieved nearly 90.25% accuracy while running the model on an STM32L4-series microcontroller, with an inference time of 418 ms for 4 s sequences, corresponding to an average CPU usage of about 10% for the recognition task. Full article

Background/Objective: Parkinson’s disease (PD) is characterized by motor and cognitive impairments that significantly affect quality of life. Robotic-assisted therapies, such as the AMADEO^® system, have shown potential in rehabilitating upper limb function but are underexplored in PD. This study aimed to assess the effects of Robotic-Assisted Therapy (RAT) compared to Conventional Physical Therapy (CPT) on cognitive, motor, and functional outcomes in PD patients. Methods: A single-blind, randomized controlled trial was conducted with PD patients allocated to RAT or CPT. Participants were assessed at baseline (T0) and post-intervention (T1) using measures including MoCA, FAB, UPDRS-III, 9-Hole Peg Test, FMA-UE, FIM, and PDQ-39. Statistical analyses included ANCOVA and regression models. Results: RAT led to significant improvements in global cognition (MoCA, p < 0.001) and executive functioning (FAB, p = 0.0002) compared to CPT. Motor function improved, particularly in wrist and hand control (FMA-UE), whereas changes in fine motor dexterity (9-Hole Peg Test) were less consistent and did not reach robust significance. No significant improvements were observed in broader quality of life domains, depressive symptoms, or memory-related cognitive measures. However, quality of life improved significantly in the stigma subdomain of the PDQ-39 (p = 0.0075). Regression analyses showed that baseline motor impairment predicted cognitive outcomes. Conclusions: RAT demonstrated superior cognitive and motor benefits in PD patients compared to CPT. These results support the integration of robotic rehabilitation into PD management. Further studies with larger sample sizes and long-term follow-up are needed to validate these findings and assess their sustainability. Full article

Background/Aims: The aim of this study was to evaluate the effectiveness of large language model (LLM)-based chatbot systems in predicting bone age and identifying growth stages, and to explore their potential as practical, infrastructure-independent alternatives to conventional methods and convolutional neural network (CNN)-based deep learning models. Methods: This study evaluated the performance of three ChatGPT-based models (GPT-4o, GPT-o4-mini-high, and GPT-o1-pro) in predicting bone age and growth stage using 90 anonymized hand–wrist radiographs (30 from each growth stage—pre-peak, peak, and post-peak—with equal male and female distribution). Reference standards were ensured by expert orthodontists using Fishman’s Skeletal Maturity Indicators (SMI) system and the Greulich–Pyle Atlas, with each radiograph analyzed by three GPT models using standardized prompts. Model performances were evaluated through statistical analyses assessing agreement and prediction accuracy. Results: All models showed significant agreement with the reference values in bone age prediction (p < 0.001), with GPT-o1-pro having the highest concordance (Pearson r = 0.546). No statistically significant difference was observed in the mean absolute error (MAE) among the models (p > 0.05). The GPT-o4-mini-high model achieved an accuracy rate of 72.2% within a ±2 year deviation range for bone age prediction. The GPT-o1-pro and GPT-o4-mini-high models showed bias in the Bland–Altman analysis of bone age predictions; however, GPT-o1-pro yielded more reliable predictions with narrower limits of agreement. In terms of growth stage classification, the GPT-4o model achieved the highest agreement with the reference values (κ = 0.283, p < 0.001). Conclusions: This study shows that general-purpose GPT models can support bone age and growth stages prediction, with each model having distinct strengths. While GPT models do not replace clinical examination, their contextual reasoning and ability to perform preliminary assessments without domain-specific training make them promising tools, though further development is needed. Full article

Background: Humans interacting with virtual objects is becoming common due to the popularity of the devices adopting the mixed reality (MR) techniques. Assessing hand functions using these devices for medical purposes provides alternatives in addition to the traditional hand function assessment techniques. Objectives: The objectives were to compare the movement time (MT) of handing a real and a virtual object between post-surgery wrist fracture patients and healthy adults and to determine the correlation between the MT and commonly adopted hand function indicators. Methods: An experiment was performed. A total of 29 participants, including 17 patients and 12 healthy adults, joined. All the participants moved a real or a virtual tube from an origin to a destination. A set of MR device was adopted to generate the virtual object. The MTs were analyzed to compare differences between the patients and the healthy adults. Regression models were developed to predict the MT under experimental conditions. Results: The MT of the surgical hand was significantly longer than that of the nonsurgical hand of the patients and was significantly longer than that of the left hand of the healthy adults. The MT was negatively correlated with the commonly adopted hand function indicators, including grip strength, range of motion, hand dexterity score, and Modified Mayo Wrist Score. Conclusions: The anticipation that the MT of interacting with virtual objects for patients may reveal hand function characteristics for post-surgery patients was supported. The regression models developed could reveal the progression of hand function recovery for these patients. Having patients interact with virtual objects could be a supplemental approach in assessing their hand functions. Full article

This paper describes ongoing work surrounding the creation of a recognition system for Mexican Sign Language (LSM). We propose a general sign decomposition that is divided into three parts, i.e., hand configuration (HC), arm movement (AM), and non-hand gestures (NHGs). This paper focuses on the AM features and reports the approach created to analyze visual patterns in arm joint movements (wrists, shoulders, and elbows). For this research, a proprietary dataset—one that does not limit the recognition of arm movements—was developed, with active participation from the deaf community and LSM experts. We analyzed two case studies involving three sign subsets. For each sign, the pose was extracted to generate shapes of the joint paths during the arm movements and fed to a CNN classifier. YOLOv8 was used for pose estimation and visual pattern classification purposes. The proposed approach, based on pose estimation, shows promising results for constructing CNN models to classify a wide range of signs. Full article

The aim of the study was to verify the relationship between musculoskeletal pain of elite Polish goalball players and selected physique and posture characteristics. We examined 12 players. The mean age was 21.8 ± 6.0 years, and a mean training experience of 6.3 ± 3.4 years. Physique (body mass, body height, waist circumference, fat tissue, fat-free soft tissue) and posture (thoracic kyphosis and lumbar lordosis) and range of motion (in the thoracic and lumbar regions) were assessed. The incidences and locations of musculoskeletal pain were identified using the Nordic Musculoskeletal Questionnaire, covering the period from the last seven days (NMQ-7) and six months (NMQ-6). Due to the small group size, non-parametric tests (Spearman’s rank correlation) were used. The significance level was set at p < 0.05. Players were more likely to report musculoskeletal pain in the last six months than in the previous week. Pain reported in both NMQ6 and NMQ7 was most common in the wrists/hands and lower back, and, in NMQ6, also in the shoulders and ankles/feet. There were significant negative correlations of total NMQ7 with lumbar lordosis angle in the habitual standing position (R = −0.6; p = 0.04), trunk flexion (R = −0.8, p = 0.002), and trunk extension (R = −0.6; p = 0.03), and a positive correlation with thoracic kyphosis angle in trunk flexion (R = 0.8, p = 0.005). There was a statistically significant, inversely proportional relationship of thoracic kyphosis angle values in the habitual position (R = −0.58; p = 0.049) and thoracic kyphosis angle THA in trunk flexion (R = −0.6; p = 0.038) with time of disability. Relationships between some body posture parameters and musculoskeletal pain in the studied athletes were also noted. Full article

This case series investigates whether traditional machine learning (ML) and convolutional neural network (CNN) models trained on wrist-worn accelerometry data collected in a laboratory setting can accurately predict real-world functional hand use in individuals with chronic stroke. Participants (N = 4) with neuroimaging-confirmed chronic stroke completed matched activity scripts—comprising instrumental and basic activities of daily living—in-lab and at-home. Participants wore ActiGraph CenterPoint Insight watches on the impaired and unimpaired wrists; concurrent video recordings were collected in both environments. Frame-by-frame annotations of the video, guided by the FAABOS scale (functional, non-functional, unknown), served as the ground truth. The results revealed a consistent capacity–performance gap: participants used their impaired hand more in-lab than at-home, with the largest discrepancies in patients with moderate to severe impairment. Random forest ML models trained on in-lab accelerometry accurately classified at-home hand use, with the highest performance in mildly and severely impaired limbs (accuracy = 0.80–0.90) and relatively lower performance (accuracy = 0.62) in moderately impaired limbs. CNN models showed comparable accuracy to random forest classifiers. These pilot findings demonstrate the feasibility of using lab-trained ML models to monitor real-world hand use and identify emerging patterns of learned non-use—enabling timely, targeted interventions to promote recovery in outpatient stroke rehabilitation. Full article

Distal radius fractures (DRFs) constitute one of the most prevalent injuries in adults. This study compares the clinical and radiological outcomes of intra- and extra-articular DRFs treated with percutaneous Kirschner wires (PKW) or volar locking plates (VLP). Materials and Methods: A retrospective analysis was conducted on 42 patients (aged 18 to 85) treated between 2017 and 2019 with a minimum follow-up of five years. Outcomes were assessed using radiographic parameters and validated clinical scoring systems such as Disabilities of the Arm, Shoulder, and Hand (DASH), Patient-Rated Wrist Evaluation (PRWE), and Mayo Wrist Score (MAYO). Results: Clinical outcomes were better in the VLP group but not statistically significant (p > 0.05). For extra-articular fractures, DASH were 1.5 (VLP) vs. 6.4 (PKW) (p = 0.5007), PRWE were 1.3 (VLP) vs. 2.9 (PKW) (p = 0.4049), and MAYO were 95 (VLP) vs. 86.1 (PKW) (p = 0.2406). For intra-articular fractures, DASH were 6.6 (VLP) vs. 19.7 (PKW) (p = 0.0981), PRWE 12.9 (VLP) vs. 21.1 (PKW) (p = 0.3661), and MAYO 78.9 (VLP) vs. 72.2 (PKW) (p = 0.4503). Conclusions: PKW and VLP showed satisfactory long-term outcomes. VLP fixation allowed better short-term recovery and anatomical restoration, but long-term functional outcomes were comparable. Full article

Background: In volleyball, upper limb dimensions, handgrip strength (HGS), and baropodometric parameters are critical for executing offensive and defensive actions during the match. These movements demand not only physical precision but also carry a significant risk of injury, varying by playing position. Objectives: This study aimed to determine the differences in specific upper limb anthropometric characteristics, HGS, and selected baropodometric variables among U-23 male volleyball players concerning playing position. Methods: The sample consisted of 92 U-23 male players who prepared for the U-23 Men’s Volleyball National Championship 2022 (20.39 (1.74) years, 184 (8.46) cm, 75.52 (10.20) kg). Playing positions analyzed were setters (n = 12), outside (n = 18), opposites (n = 19), middle blockers (n = 16), and liberos (n = 12). Results: player position differences in HGS and several anthropometric upper limb variables were observed. Middle blockers, outsides, and opposites exhibited superior anthropometric traits in most of the measurements compared to liberos and setters (p < 0.05). Differences in baropodometric parameters were only found between feet and their zones when the entire sample was evaluated. Finally, regression analysis identified dominant hand breadth (β = 3.42, 95%CI [0.43, 6.40], upper arm muscle area (β = 0.157, 95%CI [0.02, 0.29]), and wrist diameter (β = 3.59, IC 95% [0.49, 6.68]) as associated variables of HGS. Conclusions: The study underscores the importance of positional profiling in volleyball, revealing key physical traits linked to performance. The observed differences are likely attributable to the specific role and physical demands inherent to each playing position. These findings can guide targeted training and injury prevention strategies to enhance performance. Full article

Background/Objectives: Distal radius fractures (DRFs) are among the most common fractures in the elderly, with increasing incidence due to population aging. Recent evidence questions the benefits of operative treatment, particularly in elderly patients. The present study aimed to assess post-traumatic osteoarthritis (OA) and patient-reported outcome measures (PROMs) after a minimum of two years of follow-up of the previously published VOLCON randomized controlled trial (RCT), which compared operative and non-operative treatments of unstable DRFs in patients aged ≥ 65 years. Methods: This study presents a minimum two-year follow-up of a single-center, assessor-blinded RCT. A total of 100 patients with unstable DRFs were randomized to either operative treatment with volar locking plating or non-operative treatment with cast immobilization. The primary outcome was post-traumatic OA, assessed using the Knirk and Jupiter classification. Secondary outcomes included PROMs (Quick Disabilities of the Arm, Shoulder, and Hand (Quick-DASH)) and Patient-Rated Wrist/Hand Evaluation (PRWHE), complications, pain, grip strength, and range of motion (ROM). Statistical analyses were performed using two-way ANOVA. Results: After a median follow-up of 3.0 years, 60 patients (28 non-operative and 32 operative) were available for analysis. There was no significant difference in OA between the groups (p = 0.57). PROMs (Quick-DASH, PRWHE), pain, grip strength, and ROM were time-dependent (p < 0.001) but not treatment-dependent. Complications were more frequent in the operative group, including hardware-related issues requiring reoperation. Conclusions: At a minimum of two years of follow-up, no correlation was found between treatment choice and post-traumatic OA. Functional outcomes were similar between groups, suggesting that non-operative treatment remains a viable option for elderly patients with unstable DRFs. Full article