RESUMEN
Assessing sleep posture, a critical component in sleep tests, is crucial for understanding an individual's sleep quality and identifying potential sleep disorders. However, monitoring sleep posture has traditionally posed significant challenges due to factors such as low light conditions and obstructions like blankets. The use of radar technolsogy could be a potential solution. The objective of this study is to identify the optimal quantity and placement of radar sensors to achieve accurate sleep posture estimation. We invited 70 participants to assume nine different sleep postures under blankets of varying thicknesses. This was conducted in a setting equipped with a baseline of eight radars-three positioned at the headboard and five along the side. We proposed a novel technique for generating radar maps, Spatial Radio Echo Map (SREM), designed specifically for data fusion across multiple radars. Sleep posture estimation was conducted using a Multiview Convolutional Neural Network (MVCNN), which serves as the overarching framework for the comparative evaluation of various deep feature extractors, including ResNet-50, EfficientNet-50, DenseNet-121, PHResNet-50, Attention-50, and Swin Transformer. Among these, DenseNet-121 achieved the highest accuracy, scoring 0.534 and 0.804 for nine-class coarse- and four-class fine-grained classification, respectively. This led to further analysis on the optimal ensemble of radars. For the radars positioned at the head, a single left-located radar proved both essential and sufficient, achieving an accuracy of 0.809. When only one central head radar was used, omitting the central side radar and retaining only the three upper-body radars resulted in accuracies of 0.779 and 0.753, respectively. This study established the foundation for determining the optimal sensor configuration in this application, while also exploring the trade-offs between accuracy and the use of fewer sensors.
Asunto(s)
Redes Neurales de la Computación , Postura , Radar , Sueño , Humanos , Postura/fisiología , Sueño/fisiología , Masculino , Femenino , Adulto , Algoritmos , Adulto JovenRESUMEN
Surface-enhanced Raman scattering (SERS) imaging has emerged as a promising tool for guided cancer diagnosis and synergistic therapies, such as combined chemotherapy and photothermal therapy (chemo-PTT). Yet, existing therapeutic agents often suffer from low SERS sensitivity, insufficient photothermal conversion, or/and limited drug loading capacity. Herein, a multifunctional theragnostic nanoplatform consisting of mesoporous silica-coated gold nanostar with a cyclic Arg-Gly-Asp (RGD)-coated gold nanocluster shell (named RGD-pAS@AuNC) is reported that exhibits multiple "hot spots" for pronouncedly enhanced SERS signals and improved near-infrared (NIR)-induced photothermal conversion efficiency (85.5%), with a large capacity for high doxorubicin (DOX) loading efficiency (34.1%, named RGD/DOX-pAS@AuNC) and effective NIR-triggered DOX release. This nanoplatform shows excellent performance in xenograft tumor model of HeLa cell targeting, negligible cytotoxicity, and good stability both in vitro and in vivo. By SERS imaging, the optimal temporal distribution of injected RGD/DOX-pAS@AuNCs at the tumor site is identified for NIR-triggered local chemo-PTT toward the tumor, achieving ultraeffective therapy in tumor cells and tumor-bearing mouse model with 5 min of NIR irradiation (0.5 W cm-2 ). This work offers a promising approach to employing SERS imaging for effective noninvasive tumor treatment by on-site triggered chemo-PTT.
Asunto(s)
Nanopartículas , Neoplasias , Humanos , Animales , Ratones , Células HeLa , Oro/farmacología , Terapia Fototérmica , Fototerapia/métodos , Doxorrubicina/farmacología , OligopéptidosRESUMEN
Sleep posture has a crucial impact on the incidence and severity of obstructive sleep apnea (OSA). Therefore, the surveillance and recognition of sleep postures could facilitate the assessment of OSA. The existing contact-based systems might interfere with sleeping, while camera-based systems introduce privacy concerns. Radar-based systems might overcome these challenges, especially when individuals are covered with blankets. The aim of this research is to develop a nonobstructive multiple ultra-wideband radar sleep posture recognition system based on machine learning models. We evaluated three single-radar configurations (top, side, and head), three dual-radar configurations (top + side, top + head, and side + head), and one tri-radar configuration (top + side + head), in addition to machine learning models, including CNN-based networks (ResNet50, DenseNet121, and EfficientNetV2) and vision transformer-based networks (traditional vision transformer and Swin Transformer V2). Thirty participants (n = 30) were invited to perform four recumbent postures (supine, left side-lying, right side-lying, and prone). Data from eighteen participants were randomly chosen for model training, another six participants' data (n = 6) for model validation, and the remaining six participants' data (n = 6) for model testing. The Swin Transformer with side and head radar configuration achieved the highest prediction accuracy (0.808). Future research may consider the application of the synthetic aperture radar technique.
Asunto(s)
Radar , Apnea Obstructiva del Sueño , Humanos , Postura , Aprendizaje Automático , SueñoRESUMEN
Surveillance of sleeping posture is essential for bed-ridden patients or individuals at-risk of falling out of bed. Existing sleep posture monitoring and classification systems may not be able to accommodate the covering of a blanket, which represents a barrier to conducting pragmatic studies. The objective of this study was to develop an unobtrusive sleep posture classification that could accommodate the use of a blanket. The system uses an infrared depth camera for data acquisition and a convolutional neural network to classify sleeping postures. We recruited 66 participants (40 men and 26 women) to perform seven major sleeping postures (supine, prone (head left and right), log (left and right) and fetal (left and right)) under four blanket conditions (thick, medium, thin, and no blanket). Data augmentation was conducted by affine transformation and data fusion, generating additional blanket conditions with the original dataset. Coarse-grained (four-posture) and fine-grained (seven-posture) classifiers were trained using two fully connected network layers. For the coarse classification, the log and fetal postures were merged into a side-lying class and the prone class (head left and right) was pooled. The results show a drop of overall F1-score by 8.2% when switching to the fine-grained classifier. In addition, compared to no blanket, a thick blanket reduced the overall F1-scores by 3.5% and 8.9% for the coarse- and fine-grained classifiers, respectively; meanwhile, the lowest performance was seen in classifying the log (right) posture under a thick blanket, with an F1-score of 72.0%. In conclusion, we developed a system that can classify seven types of common sleeping postures under blankets and achieved an F1-score of 88.9%.
Asunto(s)
Aprendizaje Profundo , Ropa de Cama y Ropa Blanca , Femenino , Humanos , Masculino , Redes Neurales de la Computación , Postura , SueñoRESUMEN
Wandering is a common behavioral disorder in the community-dwelling elderly. More than two-thirds of caregivers believe that wandering would cause falls. While physical restraint is a common measure to address wandering, it could trigger challenging behavior in approximately 80% of the elderly with dementia. This study aims to develop a virtual restraint using a night monitoring system (eNightLog) to provide a safe environment for the elderly and mitigate the caregiver burden. The eNightLog system consisted of remote sensors, including a near infra-red 3D time-of-flight sensor and ultrawideband sensors. An alarm system was controlled by customized software and algorithm based on the respiration rate and body posture of the elderly. The performance of the eNightLog system was evaluated in both single and double bed settings by comparing to that of a pressure mat and an infrared fence system, under simulated bed-exiting scenarios. The accuracy and precision for the three systems were 99.0%, 98.8%, 85.9% and 99.2%, 97.8%, 78.6%, respectively. With higher accuracy, precision, and a lower false alarm rate, eNightLog demonstrated its potential as an alternative to physical restraint to remedy the workload of the caregivers and the psychological impact of the elderly.
Asunto(s)
Monitoreo Fisiológico , Conducta Errante , Accidentes por Caídas , Anciano , Cuidadores , Humanos , Vida IndependienteRESUMEN
BACKGROUND: The purpose of this study was to compare the biomechanical stress and stability of calcaneal fixations with and without bone defect, before and after bone grafting, through a computational approach. METHODS: A finite element model of foot-ankle complex was reconstructed, impoverished with a Sanders III calcaneal fracture without bone defect and with moderate and severe bone defects. Plate fixations with and without bone grafting were introduced with walking stance simulated. The stress and fragment displacement of the calcaneus were evaluated. FINDINGS: Moderate and severe defect increased the calcaneus stress by 16.11% and 32.51%, respectively and subsequently decreased by 10.76% and 20.78% after bone grafting. The total displacement was increased by 3.99% and 24.26%, respectively by moderate and severe defect, while that of posterior joint facet displacement was 86.66% and 104.44%. The former was decreased by 25.73% and 35.96% after grafting, while that of the latter was reduced by 88.09% and 84.78% for moderate and severe defect, respectively. INTERPRETATION: Our finite element prediction supported that bone grafting for fixation could enhance the stability and reduce the risk of secondary stress fracture in cases of bone defect in calcaneal fracture.
Asunto(s)
Trasplante Óseo , Calcáneo , Análisis de Elementos Finitos , Fracturas Óseas , Calcáneo/cirugía , Calcáneo/lesiones , Calcáneo/fisiopatología , Humanos , Trasplante Óseo/métodos , Fracturas Óseas/cirugía , Fracturas Óseas/fisiopatología , Estrés Mecánico , Simulación por Computador , Fenómenos Biomecánicos , Fijación Interna de Fracturas/métodos , Modelos BiológicosRESUMEN
The accuracy of sleep posture assessment in standard polysomnography might be compromised by the unfamiliar sleep lab environment. In this work, we aim to develop a depth camera-based sleep posture monitoring and classification system for home or community usage and tailor a deep learning model that can account for blanket interference. Our model included a joint coordinate estimation network (JCE) and sleep posture classification network (SPC). SaccpaNet (Separable Atrous Convolution-based Cascade Pyramid Attention Network) was developed using a combination of pyramidal structure of residual separable atrous convolution unit to reduce computational cost and enlarge receptive field. The Saccpa attention unit served as the core of JCE and SPC, while different backbones for SPC were also evaluated. The model was cross-modally pretrained by RGB images from the COCO whole body dataset and then trained/tested using dept image data collected from 150 participants performing seven sleep postures across four blanket conditions. Besides, we applied a data augmentation technique that used intra-class mix-up to synthesize blanket conditions; and an overlaid flip-cut to synthesize partially covered blanket conditions for a robustness that we referred to as the Post-hoc Data Augmentation Robustness Test (PhD-ART). Our model achieved an average precision of estimated joint coordinate (in terms of PCK@0.1) of 0.652 and demonstrated adequate robustness. The overall classification accuracy of sleep postures (F1-score) was 0.885 and 0.940, for 7- and 6-class classification, respectively. Our system was resistant to the interference of blanket, with a spread difference of 2.5%.
RESUMEN
Dementia is a long-term and progressive syndrome that not only influences the person with dementia (PWD) but also the caregiver. However, informal caregivers are not always empathic and understand the symptoms of dementia, leading to destructive caregiving relationships and poor quality of caregiving. VR-based simulation interventions can provide a more realistic and memorable learning experience for caregivers to walk in PWDs' shoes. This review aimed to provide practitioners and researchers with insights on developing and/or adopting an effective VR-based simulation intervention for enhancing the empathy of informal caregivers of PWD. A mixed-methods systematic review was conducted. Quantitative, qualitative, and mixed-methods studies were searched from MEDLINE, PsycINFO, CINAHL, Scopus, Embase, and Cochrane Library updating. Standard JBI critical appraisal instruments were used for the quality appraisal. A convergent segregated approach was used to synthesize and integrate the data. A total of seven studies were included. Inconsistent quantitative results were reported on the effects of VR-based simulation on empathy enhancement. Significant effects were reported on knowledge of dementia and emotion-focused coping strategies. Two themes were generated from the qualitative studies, including "Informal caregivers gained better insight into problems encountered by older people with dementia" and "Thinking from the perspective of older people with dementia, leading to changes in attitudes and behaviours towards dementia". The qualitative synthesized evidence showed that informal caregivers gained better insight into problems encountered by PWD, but the quantitative synthesized results are inconsistent. Yet, informal caregivers experienced a change in attitude by thinking from the perspective of PWD.
Asunto(s)
Demencia , Realidad Virtual , Humanos , Anciano , Cuidadores , Empatía , Demencia/terapia , Aprendizaje , Calidad de VidaRESUMEN
While hallux valgus (HV) surgeries are useful for correcting skeletal alignment problems, their effects on plantar load, which reflects forefoot functions, are less understood. The objective of this study is to conduct a systematic review and meta-analysis on the plantar load change after HV surgeries. A systematic search of Web of Science, Scopus, PubMed, CENTRAL, EMBASE, and CINAHL was performed. Studies that assessed the pre- and post-operative plantar pressure of HV patients undergoing surgeries and reported load-related parameters over the hallux, medial metatarsal, and/or central metatarsal regions were included. Studies were appraised by using the modified NIH quality assessment tool for before-after study. Studies suitable for meta-analysis were pooled with the random-effects model, using the standardized mean difference of the before-after parameters as an effect measure. Twenty-six studies containing 857 HV patients and 973 feet were included for the systematic review. Meta-analysis was conducted on 20 of them, and most studies did not favor HV surgeries. Overall, HV surgeries reduced the plantar load over the hallux region (SMD -0.71, 95% CI, -1.15 to -0.26), indicating that forefoot function worsened after surgeries. For the other five outcomes, the overall estimates were not statistically significant, indicating that surgeries did not improve them either. There was substantial heterogeneity among the studies, which in most cases could not be resolved by pre-planned subgroup analyses by surgical classification, year of publication, median age of patients, and length of follow-up. Sensitivity analysis removing lower-quality studies showed that the load integrals (impulse) over the central metatarsal region significantly increased (SMD 0.27, 95% CI, 0 to 0.53), indicating that surgeries increased the risk of transfer metatarsalgia. There is no solid evidence that HV surgeries could improve forefoot functions from a biomechanical point perspective. Currently available evidence even suggests that surgeries might reduce the plantar load over the hallux and adversely affect push-off function. The reasons behind and the effectiveness of alternative surgical methods warrant further investigation.
RESUMEN
This study examined the influence of set-interval and repetition-interval rest time of virtual reality (VR) boxing game in supine-lying posture. Fifty healthy middle-aged adults were randomly assigned into VR and non-VR groups to perform six different exercise protocols with varying set-interval and repetition-interval rest times (S0R0, S0R1/3, S0R2/3, S40R0, S40R1/3, and S40R2/3). Analysis on the non-VR group showed significant differences between exercise protocols for average heart rate (p < 0.001), maximum ventilation volume (p < 0.001), respiratory quotient (p < 0.001), oxygen pulse (p < 0.001), and excess post-exercise oxygen consumption (EPOC) (p = 0.003). VR appeared to have no further improvement on physical training effects in middle-aged adults, while the participants reported negative experience that might be associated with the over-exertion. Future study might need to explore game design elements that can accommodate high-exertion exercises.
RESUMEN
Early stage oxidative stress, inflammatory response, and infection after tendon surgery are highly associated with the subsequent peritendinous adhesion formation, which may diminish the quality and function of the repaired tendon. Although various anti-inflammatory and/or antibacterial grafts have been proposed to turn the scale, most of them suffer from the uncertainty of drug-induced adverse effects, low mechanical strength, and tissue adhesiveness. Here, inspired by the tendon anatomy and pathophysiology of adhesion development, an adhesive and robust dual-layer Janus patch is developed, whose inner layer facing the operated tendon is a multifunctional electrospun hydrogel patch (MEHP), encircled further by a poly-l-lactic acid (PLLA) fibrous outer layer facing the surrounding tissue. Specifically, MEHP is prepared by gelatin methacryloyl (GelMA) and zinc oxide (ZnO) nanoparticles, which are co-electrospun first and then treated by tannic acid (TA). The inner MEHP exhibits superior mechanical performance, adhesion strength, and outstanding antioxidation, anti-inflammation, and antibacterial properties, and it can adhere to the injury site offering a favorable microenvironment for tendon regeneration. Meanwhile, the outer PLLA acts as a physical barrier that prevents extrinsic cells and tissues from invading the defect site, reducing peritendinous adhesion formation. This work presents a proof-of-concept of a drug-free graft with anisotropic adhesive and biological functions to concert the healing phases of injured tendon by alleviating incipient inflammation and oxidative damage but supporting tissue regeneration and reducing tendon adhesion in the later phase of repair and remodeling. It is envisioned that this Janus patch could offer a promising strategy for safe and efficient tendon therapy.
Asunto(s)
Adhesivos , Biomimética , Antiinflamatorios/farmacología , Antibacterianos/farmacologíaRESUMEN
Elastography complements traditional medical imaging modalities by mapping tissue stiffness to identify tumors in the endocrine system, and machine learning models can further improve diagnostic accuracy and reliability. Our objective in this review was to summarize the applications and performance of machine-learning-based elastography on the classification of endocrine tumors. Two authors independently searched electronic databases, including PubMed, Scopus, Web of Science, IEEEXpress, CINAHL, and EMBASE. Eleven (n = 11) articles were eligible for the review, of which eight (n = 8) focused on thyroid tumors and three (n = 3) considered pancreatic tumors. In all thyroid studies, the researchers used shear-wave ultrasound elastography, whereas the pancreas researchers applied strain elastography with endoscopy. Traditional machine learning approaches or the deep feature extractors were used to extract the predetermined features, followed by classifiers. The applied deep learning approaches included the convolutional neural network (CNN) and multilayer perceptron (MLP). Some researchers considered the mixed or sequential training of B-mode and elastographic ultrasound data or fusing data from different image segmentation techniques in machine learning models. All reviewed methods achieved an accuracy of ≥80%, but only three were ≥90% accurate. The most accurate thyroid classification (94.70%) was achieved by applying sequential training CNN; the most accurate pancreas classification (98.26%) was achieved using a CNN-long short-term memory (LSTM) model integrating elastography with B-mode and Doppler images.
RESUMEN
Dysphagia is one of the most common problems among older adults, which might lead to aspiration pneumonia and eventual death. It calls for a feasible, reliable, and standardized screening or assessment method to prompt rehabilitation measures and mitigate the risks of dysphagia complications. Computer-aided screening using wearable technology could be the solution to the problem but is not clinically applicable because of the heterogeneity of assessment protocols. The aim of this paper is to formulate and unify a swallowing assessment protocol, named the Comprehensive Assessment Protocol for Swallowing (CAPS), by integrating existing protocols and standards. The protocol consists of two phases: the pre-test phase and the assessment phase. The pre-testing phase involves applying different texture or thickness levels of food/liquid and determining the required bolus volume for the subsequent assessment. The assessment phase involves dry (saliva) swallowing, wet swallowing of different food/liquid consistencies, and non-swallowing (e.g., yawning, coughing, speaking, etc.). The protocol is designed to train the swallowing/non-swallowing event classification that facilitates future long-term continuous monitoring and paves the way towards continuous dysphagia screening.
Asunto(s)
Trastornos de Deglución , Neumonía por Aspiración , Humanos , Anciano , Trastornos de Deglución/etiología , Deglución , Tamizaje Masivo/métodos , Alimentos , Neumonía por Aspiración/etiologíaRESUMEN
Aspiration caused by dysphagia is a prevalent problem that causes serious health consequences and even death. Traditional diagnostic instruments could induce pain, discomfort, nausea, and radiation exposure. The emergence of wearable technology with computer-aided screening might facilitate continuous or frequent assessments to prompt early and effective management. The objectives of this review are to summarize these systems to identify aspiration risks in dysphagic individuals and inquire about their accuracy. Two authors independently searched electronic databases, including CINAHL, Embase, IEEE Xplore® Digital Library, PubMed, Scopus, and Web of Science (PROSPERO reference number: CRD42023408960). The risk of bias and applicability were assessed using QUADAS-2. Nine (n = 9) articles applied accelerometers and/or acoustic devices to identify aspiration risks in patients with neurodegenerative problems (e.g., dementia, Alzheimer's disease), neurogenic problems (e.g., stroke, brain injury), in addition to some children with congenital abnormalities, using videofluoroscopic swallowing study (VFSS) or fiberoptic endoscopic evaluation of swallowing (FEES) as the reference standard. All studies employed a traditional machine learning approach with a feature extraction process. Support vector machine (SVM) was the most famous machine learning model used. A meta-analysis was conducted to evaluate the classification accuracy and identify risky swallows. Nevertheless, we decided not to conclude the meta-analysis findings (pooled diagnostic odds ratio: 21.5, 95% CI, 2.7-173.6) because studies had unique methodological characteristics and major differences in the set of parameters/thresholds, in addition to the substantial heterogeneity and variations, with sensitivity levels ranging from 21.7% to 90.0% between studies. Small sample sizes could be a critical problem in existing studies (median = 34.5, range 18-449), especially for machine learning models. Only two out of the nine studies had an optimized model with sensitivity over 90%. There is a need to enlarge the sample size for better generalizability and optimize signal processing, segmentation, feature extraction, classifiers, and their combinations to improve the assessment performance. Systematic Review Registration: (https://www.crd.york.ac.uk/prospero/), identifier (CRD42023408960).
RESUMEN
Biomechanical studies play an important role in understanding the pathophysiology of sleep disorders and providing insights to maintain sleep health. Computational methods facilitate a versatile platform to analyze various biomechanical factors in silico, which would otherwise be difficult through in vivo experiments. The objective of this review is to examine and map the applications of computational biomechanics to sleep-related research topics, including sleep medicine and sleep ergonomics. A systematic search was conducted on PubMed, Scopus, and Web of Science. Research gaps were identified through data synthesis on variants, outcomes, and highlighted features, as well as evidence maps on basic modeling considerations and modeling components of the eligible studies. Twenty-seven studies (n = 27) were categorized into sleep ergonomics (n = 2 on pillow; n = 3 on mattress), sleep-related breathing disorders (n = 19 on obstructive sleep apnea), and sleep-related movement disorders (n = 3 on sleep bruxism). The effects of pillow height and mattress stiffness on spinal curvature were explored. Stress on the temporomandibular joint, and therefore its disorder, was the primary focus of investigations on sleep bruxism. Using finite element morphometry and fluid-structure interaction, studies on obstructive sleep apnea investigated the effects of anatomical variations, muscle activation of the tongue and soft palate, and gravitational direction on the collapse and blockade of the upper airway, in addition to the airflow pressure distribution. Model validation has been one of the greatest hurdles, while single-subject design and surrogate techniques have led to concerns about external validity. Future research might endeavor to reconstruct patient-specific models with patient-specific loading profiles in a larger cohort. Studies on sleep ergonomics research may pave the way for determining ideal spine curvature, in addition to simulating side-lying sleep postures. Sleep bruxism studies may analyze the accumulated dental damage and wear. Research on OSA treatments using computational approaches warrants further investigation.
RESUMEN
The objective of this review was to summarize the applications of sonoelastography in testicular tumor identification and inquire about their test performances. Two authors independently searched English journal articles and full conference papers from CINAHL, Embase, IEEE Xplore®, PubMed, Scopus, and Web of Science from inception and organized them into a PIRO (patient, index test, reference test, outcome) framework. Eleven studies (n = 11) were eligible for data synthesis, nine of which (n = 9) utilized strain elastography and two (n = 2) employed shear-wave elastography. Meta-analyses were performed on the distinction between neoplasm (tumor) and non-neoplasm (non-tumor) from four study arms and between malignancy and benignity from seven study arms. The pooled sensitivity of classifying malignancy and benignity was 86.0% (95%CI, 79.7% to 90.6%). There was substantial heterogeneity in the classification of neoplasm and non-neoplasm and in the specificity of classifying malignancy and benignity, which could not be addressed by the subgroup analysis of sonoelastography techniques. Heterogeneity might be associated with the high risk of bias and applicability concern, including a wide spectrum of testicular pathologies and verification bias in the reference tests. Key technical obstacles in the index test were manual compression in strain elastography, qualitative observation of non-standardized color codes, and locating the Regions of Interest (ROI), in addition to decisions in feature extractions. Future research may focus on multiparametric sonoelastography using deep learning models and ensemble learning. A decision model on the benefits-risks of surgical exploration (reference test) could also be developed to direct the test-and-treat strategy for testicular tumors.
RESUMEN
Professional esports athletes spend a long time in the same sitting posture during training and competition. Mobile esports may exacerbate potential postural problems because of the closer and unsupported arms and because athletes spend more time in a forward-/flexed-head posture. Prolonged sitting in these postures carries significant health risks and may lead to musculoskeletal problems and injuries. The objective of this retrospective study is to assess the posture, mobility, and stability of the spine for professional mobile esports athletes. We collected spine-assessment data from 48 athletes participating in a top-tier league on a real-time-strategy battle-arena online game. The spinal assessment was conducted using the SpinalMouse® under upright standing and trunk flexion in addition to the Matthiass test. Measurements were converted into Idiag Scores by the SpinalMouse® software. The Idiag Posture, Idiag Mobility, and Idiag Stability scores were 62.50 (IQR: 21), 63.50 (IQR: 19.5), and 54.50 (IQR: 14.5), respectively, and were significantly lower (p < 0.001) than the reference normative value (100). Age was found to have a weak positive correlation with the posture score (ρ = 0.29, p = 0.048). Although career duration appeared to lower the scores, the association was insignificant (p > 0.05). The scores also had no significant association with body height, body mass, body mass index, and esports team (p > 0.05). It was anticipated that mobile-based esports would attenuate the biomechanics of the spine and increase the likelihood of musculoskeletal problems, such as neck and back pain.
RESUMEN
Ultrasound elastography can quantify stiffness distribution of tissue lesions and complements conventional B-mode ultrasound for breast cancer screening. Recently, the development of computer-aided diagnosis has improved the reliability of the system, whilst the inception of machine learning, such as deep learning, has further extended its power by facilitating automated segmentation and tumour classification. The objective of this review was to summarize application of the machine learning model to ultrasound elastography systems for breast tumour classification. Review databases included PubMed, Web of Science, CINAHL, and EMBASE. Thirteen (n = 13) articles were eligible for review. Shear-wave elastography was investigated in six articles, whereas seven studies focused on strain elastography (5 freehand and 2 Acoustic Radiation Force). Traditional computer vision workflow was common in strain elastography with separated image segmentation, feature extraction, and classifier functions using different algorithm-based methods, neural networks or support vector machines (SVM). Shear-wave elastography often adopts the deep learning model, convolutional neural network (CNN), that integrates functional tasks. All of the reviewed articles achieved sensitivity ³ 80%, while only half of them attained acceptable specificity ³ 95%. Deep learning models did not necessarily perform better than traditional computer vision workflow. Nevertheless, there were inconsistencies and insufficiencies in reporting and calculation, such as the testing dataset, cross-validation, and methods to avoid overfitting. Most of the studies did not report loss or hyperparameters. Future studies may consider using the deep network with an attention layer to locate the targeted object automatically and online training to facilitate efficient re-training for sequential data.
RESUMEN
Older people are increasingly dependent on others to support their daily activities due to geriatric symptoms such as dementia. Some of them stay in long-term care facilities. Elderly people with night wandering behaviour may lose their way, leading to a significant risk of injuries. The eNightLog system was developed to monitor the night-time bedside activities of older people in order to help them cope with this issue. It comprises a 3D time-of-flight near-infrared sensor and an ultra-wideband sensor for detecting human presence and to determine postures without a video camera. A threshold-based algorithm was developed to classify different activities, such as leaving the bed. The system is able to send alarm messages to caregivers if an elderly user performs undesirable activities. In this study, 17 sets of eNightLog systems were installed in an elderly hostel with 17 beds in 9 bedrooms. During the three-month field test, 26 older people with different periods of stay were included in the study. The accuracy, sensitivity and specificity of detecting non-assisted bed-leaving events was 99.8%, 100%, and 99.6%, respectively. There were only three false alarms out of 2762 bed-exiting events. Our results demonstrated that the eNightLog system is sufficiently accurate to be applied in the hostel environment. Machine learning with instance segmentation and online learning will enable the system to be used for widely different environments and people, with improvements to be made in future studies.
Asunto(s)
Lechos , Cuidadores , Anciano , Algoritmos , Humanos , Aprendizaje Automático , Monitoreo FisiológicoRESUMEN
OBJECTIVES: We have proposed a novel intramedullary nail (Ni-Nail) by incorporating a sustentaculum tali screw to improve the fixation stability of minimally invasive treatment for calcaneal fractures. This study aimed to evaluate the biomechanical characters of the Ni-Nail system and compare it with traditional C-Nail system. METHODS: A finite element model of a Sanders type-IIIAB calcaneal fracture was reconstructed and fixed using two intramedullary nail systems, which was validated by a cadaver study. A vertical loading of 700 N was applied to the subtalar joint surfaces, and 525 N Achilles tendon tension was applied to the superior border of the Achilles tuberosity. The von Mises stresses and fracture displacements of both fixation models were evaluated. RESULTS: The maximum von Mises stress of the screws of Ni-Nail and C-Nail were 27.92 MPa and 57.42 MPa, respectively, while that of the main nail were 67.44 MPa and 53.01 MPa. In addition, the maximum fracture displacement of the Ni-Nail was larger than that of C-Nail by 15.6% (0.37 mm vs.0.32 mm). CONCLUSIONS: Our static simulation analysis showed that both Ni-Nail and C-Nail demonstrated similar biomechanical stability for calcaneal fixation. The Ni-Nail features a simple structure that is easier to operate and less traumatizing. Future studies may consider to further evaluate the clinical effectiveness by clinical trials and follow-ups.