Search Results (160)

Many commercial wearable sensor systems typically rely on a single continuous cardiorespiratory sensing modality, photoplethysmography (PPG), which suffers from inherent biases (i.e., differences in skin tone) and noise (e.g., motion and pressure artifacts). In this research, we present a wearable device that provides robust estimates of cardiorespiratory variables by combining three physiological signals from the upper arm: multiwavelength PPG, single-sided electrocardiography (SS-ECG), and bioimpedance plethysmography (BioZ), along with an inertial measurement unit (IMU) providing 3-axis accelerometry and gyroscope information. We evaluated the multimodal device on 16 subjects by its ability to estimate heart rate (HR) and breathing rate (BR) in the presence of various static and dynamic noise sources (e.g., skin tone and motion). We proposed a hierarchical approach that considers the subject’s skin tone and signal quality to select the optimal sensing modality for estimating HR and BR. Our results indicate that, when estimating HR, there is a trade-off between accuracy and robustness, with SS-ECG providing the highest accuracy (low mean absolute error; MAE) but low reliability (higher rates of sensor failure), and PPG/BioZ having lower accuracy but higher reliability. When estimating BR, we find that fusing estimates from multiple modalities via ensemble bagged tree regression outperforms single-modality estimates. These results indicate that multimodal approaches to cardiorespiratory monitoring can overcome the accuracy–robustness trade-off that occurs when using single-modality approaches. Full article

The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical images of diabetic feet, thereby aiding in the prevention and effective treatment of foot ulcers. A comprehensive study was conducted using an annotated dataset of medical images, evaluating the performance of the models in terms of accuracy, precision, recall and F1-score. VGG19 achieved the highest accuracy at 97%, demonstrating superior ability to focus activations on relevant lesion areas in complex images. MobileNetV2, while slightly less accurate, excelled in computational efficiency, making it a suitable choice for mobile devices and environments with hardware constraints. The study also highlights the limitations of each architecture, such as increased risk of overfitting in deeper models and the lower capability of MobileNetV2 to capture fine clinical details. These findings suggest that CNNs hold significant potential in computer-aided clinical diagnosis, particularly in the early and precise detection of diabetic foot ulcers, where timely intervention is crucial to prevent amputations. Full article

Background: Gestational diabetes mellitus (GDM) is a prevalent pregnancy complication globally. Maternal vitamin D deficiency has been linked to the risk of GDM. The aim of this study was to explore and synthesise current evidence on the association between vitamin D deficiency and the development of gestational diabetes in Western countries. Methods: A scoping review was conducted in accordance with the Joanna Briggs Institute (JBI) methodological framework. Relevant studies were identified through a comprehensive search across seven databases: ProQuest Public Health, Google Scholar, PubMed, ScienceDirect, The Lancet, BMC Public Health, the International Journal of Women’s Health, and Scopus. Studies were included based on predefined inclusion and exclusion criteria relevant to the research question. The review followed the JBI protocol, and the PRISMA flowchart was used to guide and visualise the study selection process. Results: Nineteen studies were included in the final analysis, comprising research predominantly from Australia (5), the United States (5), and Canada (4). The findings indicate a notable association between vitamin D deficiency and GDM risk, moderated by factors such as maternal age, ethnicity, seasonal variation, and body mass index (BMI). Older maternal age and higher BMI were linked with lower vitamin D levels and a higher incidence of GDM. Ethnic groups with darker skin tones showed higher rates of vitamin D deficiency, increasing vulnerability to GDM. Seasonal patterns revealed lower vitamin D levels during winter months, correlating with greater GDM risk. These patterns underscore the need for targeted preventive strategies, including the potential role of vitamin D supplementation. Conclusions: This review supports an observed association between maternal vitamin D deficiency and increased GDM risk, influenced by demographic and environmental factors. While the evidence points to a potential preventative role for vitamin D, further high-quality research, including systematic reviews and meta-analyses, is essential to establish causality and inform clinical guidelines. The review identifies knowledge gaps and suggests directions for future research and clinical practice. Full article

Background/Objectives: The accurate identification of insect bites from images of skin is daunting due to the fine gradations among diverse bite types, variability in human skin response, and inconsistencies in image quality. Methods: For this work, we introduce DeepBiteNet, a new ensemble-based deep learning model designed to perform robust multiclass classification of insect bites from RGB images. Our model aggregates three semantically diverse convolutional neural networks—DenseNet121, EfficientNet-B0, and MobileNetV3-Small—using a stacked meta-classifier designed to aggregate their predicted outcomes into an integrated, discriminatively strong output. Our technique balances heterogeneous feature representation with suppression of individual model biases. Our model was trained and evaluated on a hand-collected set of 1932 labeled images representing eight classes, consisting of common bites such as mosquito, flea, and tick bites, and unaffected skin. Our domain-specific augmentation pipeline imputed practical variability in lighting, occlusion, and skin tone, thereby boosting generalizability. Results: Our model, DeepBiteNet, achieved a training accuracy of 89.7%, validation accuracy of 85.1%, and test accuracy of 84.6%, and surpassed fifteen benchmark CNN architectures on all key indicators, viz., precision (0.880), recall (0.870), and F1-score (0.875). Our model, optimized for mobile deployment with quantization and TensorFlow Lite, enables rapid on-client computation and eliminates reliance on cloud-based processing. Conclusions: Our work shows how ensemble learning, when carefully designed and combined with realistic data augmentation, can boost the reliability and usability of automatic insect bite diagnosis. Our model, DeepBiteNet, forms a promising foundation for future integration with mobile health (mHealth) solutions and may complement early diagnosis and triage in dermatologically underserved regions. Full article

Peripheral circulatory failure refers to a condition in which the blood flow through superficial capillaries is markedly reduced or completely occluded. In clinical practice, nurses strictly adhere to regular repositioning protocols to prevent peripheral circulatory failure, during which the skin condition is evaluated visually. In this study, skin colour changes resulting from pressure application were continuously captured using a camera, and supervised machine learning was employed to classify the data into two categories: before and after pressure. The evaluation of practical colour space components revealed that the h component of the JCh colour space demonstrated the highest discriminative performance (Area Under the Curve (AUC) = 0.88), followed by the a* component of the CIELAB colour space (AUC = 0.84) and the H component of the HSV colour space (AUC = 0.83). These findings demonstrate that it is feasible to quantitatively evaluate skin colour changes associated with pressure, suggesting that this approach can serve as a valuable indicator for dimensionality reduction in feature extraction for machine learning and is potentially an effective method for preventing pressure-induced skin injuries. Full article

The absence of skin color information in skin cancer datasets poses a significant challenge for accurate diagnosis using artificial intelligence models, particularly for non-white populations. In this paper, based on the Monk Skin Tone (MST) scale, which is less biased than the Fitzpatrick scale, we propose MST-AI, a novel method for detecting skin color in images of large datasets, such as the International Skin Imaging Collaboration (ISIC) archive. The approach includes automatic frame, lesion removal, and lesion segmentation using convolutional neural networks, and modeling normal skin tones with a Variational Bayesian Gaussian Mixture Model (VB-GMM). The distribution of skin color predictions was compared with MST scale probability distribution functions (PDFs) using the Kullback-Leibler Divergence (KLD) metric. Validation against manual annotations and comparison with K-means clustering of image and skin mean RGBs demonstrated the superior performance of the MST-AI, with Kendall’s Tau, Spearman’s Rho, and Normalized Discounted Cumulative Gain (NDGC) of 0.68, 0.69, and 1.00, respectively. This research lays the groundwork for developing unbiased AI models for early skin cancer diagnosis by addressing skin color imbalances in large datasets. Full article

Medical Infrared Thermography (MIT) is a safe, non-invasive technique for assessing temperature changes on the skin’s surface that may reflect pathological processes in the underlying tissues. In temporomandibular joint disorders (TMDs), which are often associated with reduced mobility and muscle overactivity, tissue metabolism and blood flow may be diminished, resulting in localized hypothermia. Aim: The purpose of this study was to evaluate muscle tone in the masseter, suprahyoid, and sternocleidomastoid muscles following the application of two types of occlusal splints, a Michigan splint and a double repositioning splint, based on temperature changes recorded using a Fluke Ti401 PRO thermal imaging camera. Materials and Methods: Sixty dental students diagnosed with TMDs were enrolled in this study. After applying the inclusion and exclusion criteria, participants were randomly assigned to one of two groups. Group M received a Michigan splint, while group D was treated with a double repositioning splint. Results: The type of occlusal splint influenced both temperature distribution and muscle tone. In the double repositioning splint group, temperature decreased by approximately 0.8 °C between T1 and T3, whereas in the Michigan splint group, temperature increased by approximately 0.7 °C over the same period. Conclusions: Occlusal splint design has a measurable impact on temperature distribution and muscle activity. The double repositioning splint appears to be more effective in promoting short-term muscle relaxation and may provide relief for patients experiencing muscular or myofascial TMD symptoms. Full article

Photoplethysmography (PPG) is a widely adopted optical technique for cardiovascular monitoring, but its accuracy is often compromised by skin pigmentation, which attenuates the signal in individuals with darker skin tones. This research addresses the challenge of skin pigmentation by developing a PPG sensor system with a novel gain calibration strategy. We present a hardware prototype integrating a programmable gain amplifier (PGA), specifically the OPA3S328 operational amplifier, controlled by a microcontroller. The system performs a one-time gain adjustment at initialization based on the user’s skin tone, which is quantified using RGB image analysis. This “set-and-hold” approach normalizes the signal amplitude across various skin tones while effectively preserving the native morphology of the PPG waveform, which is essential for advanced cardiovascular diagnostics. Experimental validation with over 70 human volunteers demonstrated the PGA’s ability to apply calibrated gain levels, derived from a first-degree polynomial relationship between skin pigmentation and red light absorption. This approach significantly improved signal consistency across different skin tones. The findings highlight the efficacy of pre-measurement gain correction for achieving reliable PPG sensing in diverse populations and lay the groundwork for future optimization of PPG sensor designs to improve reliability in wearable health monitoring devices. Full article

Cardiovascular disease (CVD) is the leading cause of global mortality, with hypertension affecting over one billion people. Current noninvasive blood pressure (BP) systems, like cuffs, suffer from discomfort and placement errors and lack continuous monitoring. Wearable solutions promise improvements, but technologies like photoplethysmography (PPG) and bioimpedance (BIOZ) face usability and clinical accuracy limitations. PPG is sensitive to skin tone and body mass index (BMI) variability, while BIOZ struggles with electrode contact and reusability. We present a novel, strain gauge-based wearable BP device that directly quantifies pressure via a dual transducer system, compensating for tissue deformation and external forces to enable continuous, accurate BP measurement. The reusable, energy-efficient, and compact design suits long-term daily use. A novel leg press protocol across 10 subjects (systolic: 71.04–241.42 mmHg, diastolic: 53.46–123.84 mmHg) validated its performance under dynamic conditions, achieving mean absolute errors of 2.45 ± 3.99 mmHg (systolic) and 1.59 ± 2.08 mmHg (diastolic). The device showed enhanced robustness compared to the Finapres, with less motion-induced noise. This technology significantly advances current methods by delivering continuous, real-time BP monitoring without reliance on electrodes, independent of skin tone, while maintaining a high accuracy and user comfort. Full article

Facial aging is a complex process manifesting as skin hyperpigmentation, textural irregularities, and a diminished elasticity, hydration, and evenness of tone. The escalating demand for minimally invasive aesthetic interventions has driven the development of advanced hydrogel-based injectable formulations. This clinical study assessed the safety and efficacy of Hydragel A1, an injectable hydrogel containing hyaluronic acid (HA), niacinamide, and tranexamic acid (TXA), designed to simultaneously address multiple facets of facial skin aging. A cohort of 49 female participants underwent a series of objective and subjective assessments, including the Global Aesthetic Improvement Scale (GAIS), instrumental measurements (Antera 3D, Chromameter, Cutometer, Dermascan, Corneometer), and standardized photographic documentation at baseline (Day 0) and 14, 28, and 70 days post-treatment. The results demonstrated statistically significant improvements in skin hydration, texture, elasticity, and pigmentation following Hydragel A1 administration. Notably, no serious adverse events or significant injection site reactions were observed, confirming the favorable safety profile of the investigated device. Collectively, these findings underscore the potential of a combined HA, niacinamide, and TXA injectable formulation to provide a comprehensive approach to facial skin rejuvenation, effectively targeting multiple aging-related mechanisms. Full article

Background: Uveal melanoma (UM) is a relatively rare malignancy, yet it remains the most common primary intraocular cancer in adults. Several risk factors have been identified, including light iris color, fair skin tone, and cutaneous freckles. Methods: The aim of this article was an overview of the treatment methods for uveal melanoma, with a particular focus on emerging therapies such as tebentafusp and da-rovasertib. The research method was a review of the latest literature. Results: Genetic studies have uncovered key mutations in GNAQ and GNA11, which significantly contribute to UM pathogenesis. Treatment selection depends on tumor location and disease stage. In localized disease, radiotherapy—especially brachytherapy—is commonly used and generally effective. However, the prognosis worsens significantly once distant metastases, most often to the liver, develop, as no standard systemic therapy has demonstrated high efficacy in this setting. Recent years have seen the emergence of promising therapies, including tebentafusp, which stimulates immune responses against gp100-expressing melanoma cells, and darovasertib, a potent PKC inhibitor that targets MAPK pathway activation driven by GNAQ/GNA11 mutations. Both agents have shown encouraging tolerability; tebentafusp has demonstrated clinical benefit in Phase II and III trials, while darovasertib is still under investigation. Additionally, melphalan-based liver-directed therapy, particularly via hepatic arterial infusion (approved by the FDA), has shown potential in controlling liver-dominant disease in metastatic UM. This localized approach may provide significant benefit for patients with limited extrahepatic spread. Conclusions: Future research should focus on optimizing these novel strategies—tebentafusp, darovasertib, melphalan, and combination therapies—and on expanding our understanding of UM’s molecular drivers to enable the development of more effective, personalized treatments. Full article

Lasers are widely employed in the treatment of melanocytic lesions. This scoping review evaluates 77 studies on the efficacy and safety of laser treatments for café-au-lait macules (CALMs), nevus of Ota (NOA), Becker’s nevus (BN), lichen planus pigmentosus (LPP), and other pigmented lesions. The Q-switched neodymium-doped yttrium aluminum garnet (Nd:YAG), particularly the 1064 nm, is the most frequently utilized laser, demonstrating strong efficacy for NOA and other dermal pigmentary disorders. Medium-wavelength lasers, including the Q-switched ruby and Alexandrite lasers, also show promise, though results vary based on lesion depth, skin type, and treatment protocols. Recurrence and adverse effects, including post-inflammatory hyperpigmentation (PIH) and hypopigmentation, are common, particularly in patients with darker skin tones. Future studies should standardize and optimize laser parameters across lesion types and skin tones, improve long-term efficacy, and prioritize inclusion of patients with diverse Fitzpatrick skin types to evaluate differential outcomes and promote equitable treatment efficacy. Full article

Accurate skin color reproduction under varying CCT remains a critical challenge in the graphic arts, impacting applications such as face recognition, portrait photography, and human–computer interaction. Traditional AWB methods like gray-world or max-RGB often rely on statistical assumptions, which limit their accuracy under complex or extreme lighting. We propose SCR-AWB, a novel algorithm that leverages real skin reflectance data to estimate the scene illuminant’s SPD and CCT, enabling accurate skin tone reproduction. The method integrates prior knowledge of human skin reflectance, basis vectors, and camera sensitivity to perform pixel-wise spectral estimation. Experimental results on difficult skin color reproduction task demonstrate that SCR-AWB significantly outperforms traditional AWB algorithms. It achieves lower reproduction angle errors and more accurate CCT predictions, with deviations below 300 K in most cases. These findings validate SCR-AWB as an effective and computationally efficient solution for robust skin color correction. Full article

Fetal malnutrition, characterized by inadequate fat and muscle accretion during intrauterine development, has been linked to adverse outcomes, ranging from neonatal complications to long-term developmental and metabolic disorders. Traditionally, growth curves and birth weight have guided the assessment of newborns’ nutritional status; however, these measures often do not accurately reflect changes in body composition. This review compares several evaluation methods—CAN score (Metcoff methodology), body mass index (BMI), Ponderal Index (PI), McLaren Index, mid–upper arm circumference (MUAC), and plicometry—to provide suggestions on selecting the most appropriate approach, depending on the healthcare setting and population needs. Findings from multiple international studies indicate that the CAN score and BMI are among the most accurate tools, offering better sensitivity and specificity than traditional anthropometric indicators. The CAN score, based on a clinical observation of fat deposits, skin texture, and muscle tone, has been widely used in Latin America and remains a practical and cost-effective option. Nonetheless, recent research suggests that BMI, mainly when used alongside the PI, may outperform the CAN score in certain contexts. Considering the complexity of fetal nutritional assessments, integrating multiple methods enhances the diagnostic accuracy. Early identification of malnourished newborns is essential for timely intervention and improved long-term outcomes. Standardizing these diagnostic tools globally could advance efforts to reduce neonatal morbidity and mortality by 2030. Full article

Commercial progress concerning biobased materials has been slow, with success depending on functionality and emotional responses. Emotional interaction research provides a novel way to shift perceptions of biobased materials. This study proposes a human-centered emotional design framework using biobased tea waste to explore how sensory properties (form, color, odor, surface roughness) shape emotional responses and contribute to sustainable wellbeing. We used a mixed-methods approach combining subjective evaluations (Self-Assessment Manikin scale) with physiological metrics (EEG, skin temperature, pupil dilation) from 24 participants. Results demonstrated that spherical forms and high surface roughness significantly enhanced emotional valence and arousal, while warm-toned yellow samples elicited 23% higher pleasure ratings than dark ones. Neurophysiological data revealed that positive emotions correlated with reduced alpha power in the parietal lobe (αPz, p = 0.03) and a 0.3 °C rise in skin temperature, whereas negative evaluations activated gamma oscillations in central brain regions (γCz, p = 0.02). Mapping these findings to human factors engineering principles, we developed actionable design strategies—such as texture-optimized surfaces and color–emotion pairings—that transform tea waste into emotionally resonant, sustainable products. This work advances emotional design’s role in fostering ecological sustainability and human wellbeing, demonstrating how human-centered engineering can align material functionality with psychological fulfillment. Full article