Technology and Health Care最新文献

BackgroundMenopause and periodontitis can lead to changes in mandibular bone structure. Fractal dimension (FD) and radiomorphometric indices, which are widely are used to assess such changes.ObjectiveThis study aimed to evaluate mandibular trabecular bone using fractal analysis and cortical bone using radiomorphometric indices on panoramic radiographs of individuals with and without periodontitis during the perimenopausal and postmenopausal periods.MethodsThis retrospective study used panoramic radiographs from 60 females, categorized into four groups: perimenopausal and periodontally healthy (PERI-H); perimenopausal with periodontitis (PERI-P); postmenopausal and periodontally healthy (POST-H); postmenopausal with periodontitis (POST-P). Radiomorphometric indices and FD were measured bilaterally on selected condylar (F1, F6) and gonial regions (F2, F5), as well as between the first molar and second premolar teeth (F3, F4) bilaterally.ResultsIn the F3 and F4 regions, the POST-P group exhibited lower FD values compared to the PERI-H group (p = 0.035, p = 0.001, respectively). In the F1 region, significantly lower FD values were observed in the POST-P group versus the PERI-H, PERI-P and POST- H groups (p = 0.017, p = 0.011 and p = 0.017, respectively), and the POST-H group showed significantly lower FD values than the PERI-H group (p = 0.011). Cortical bone classification showed that C1 was most common in the PERI-H group (66.7%), C2 in the POST-H and POST-P groups (60.0%, 66.7%, respectively), and C3 in the POST-P group (26.7%) (p = 0.004).ConclusionsPostmenopausal females exhibited greater bone resorption in the alveolar region and the right condyle, and also showed lower FD values compared to perimenopausal females. Additionally, females with periodontitis exhibited lower fractal dimension values and increased bone porosity compared to the healthy group.

BackgroundVibration applied to the chest wall can activate the tonic vibration reflex (TVR), potentially increasing respiratory muscle activity and ventilation. While single-site intercostal stimulation has shown such effects, it is unclear whether multi-site stimulation provides greater benefits.ObjectiveTo investigate whether synchronised multi-site intercostal vibration enhances ventilation through increased rib cage expansion in healthy adults.MethodsThirty healthy adults underwent chest wall vibration under three randomised conditions: 4-point stimulation, 8-point stimulation, and sham control. Vibration was synchronised with resting breathing. Tidal volume (Vt), minute ventilation (Ve), and thoracoabdominal displacement were measured. A linear mixed-effects model was used to compare outcomes across conditions.ResultsThe 8-point stimulation significantly increased Vt and Ve compared to the 4-point and control conditions (p < 0.01). Rib cage displacement also increased, while abdominal motion remained unchanged. These findings suggest that enhanced ventilation was primarily due to rib cage expansion.ConclusionSynchronized multi-site intercostal vibration improves ventilation by increasing rib cage expansion in healthy adults and may offer a novel non-invasive respiratory facilitation strategy.

ObjectiveTo evaluate the predictive value of LA strain parameters and LASI for AF recurrence following electrical CV, and to compare them to conventional echocardiographic, biochemical, and clinical markers.MethodsIn this prospective, observational pilot study, 31 patients with persistent AF underwent electrical CV and were followed for six months. Echocardiographic evaluation included LA reservoir, conduit, and contractile strain, left atrial stiffness index, left atrial volume index (LAVI), left atrial appendage (LAA) morphology, left ventricular ejection fraction (LVEF), right atrial (RA) area, and right ventricular systolic pressure (RVSP). AF recurrence was assessed at three and six months.ResultsAt three months post-CV, LA reservoir, conduit, and contractile strain values were significantly negatively associated with AF recurrence (p < 0.001), while LASI and E/E' ratios were positively associated (p < 0.001). At six months, only contractile strain retained prognostic significance (p = 0.008). LVEF showed a positive correlation with recurrence at six months (p = 0.003), potentially reflecting the role of diastolic dysfunction.ConclusionLA strain parameters and LASI are valuable tools for predicting AF recurrence after CV, particularly in the early post-procedural period. Contractile strain may serve as a more reliable long-term predictor, emphasizing the importance of longitudinal atrial function assessment in rhythm outcome prediction. However, given the small sample size and single-center design, these results should be considered hypothesis-generating, requiring validation in larger studies.

ObjectiveThe present study aims to establish normative data for the Turkish mobile digit-in-noise test. The main objectives are to provide a reliable hearing screening tool for clinical, educational, and research purposes and to investigate its relationship with socio-demographic factors.MethodsThe study included 353 participants with normal hearing, aged 12 to 60 years. The mobile Turkish Digit-in-Noise (T-DIN) test, developed for the Android operating system, was administered using a Samsung Galaxy S10 smartphone paired with the original earbuds. To assess the reliability of the mobile T-DIN test application, it was re-administered to 172 participants under similar test conditions after a 15-day interval.ResultsThe Spearman correlation analysis yielded a coefficient of 0.754, while the intraclass correlation coefficient was calculated as 0.431. Normalization values for the assay were set at a signal-to-noise ratio of -7.05 ± 0.84. A statistically significant difference in mobile T-DIN SNR values was observed based on the age of the participants.ConclusionThe mobile T-DIN test is a suitable tool for hearing screening in individuals aged 12-60 years and provides a practical and reliable method for assessing auditory function.

BackgroundWater quality is a critical component of environmental and public health, as polluted water bodies can lead to serious health outcomes. The Miljacka river, flowing through Sarajevo, has been significantly impacted by industrial and urban wastewater discharges.ObjectiveThis study aims to develop a 3D digital model of the Miljacka river, built on topographic and satellite data, to support improved pollution assessment and inform water quality management strategies.MethodA 3D model of the river was created using integrated hydrological, topographic and satellite data. Initial 2D schematics were developed in AutoCAD and visualizations were produced in SketchUp, Revit and Twinmotion to simulate river flow dynamics and pollutant dispersion. Areas of interest were identified to assess the spatial distribution of contaminants.ResultsThe model enabled the visualization of pollutant movement and the identification of potentially high-risk zones along the river's course. Analysis of the available data suggests possible impacts of pollution on public health, particularly in relation to chemical contaminants and microbial loads, although further studies are needed for a more precise assessment of health risks.ConclusionThis research highlights the importance of integrating advanced digital technologies in environmental health assessment.

Background: Stroke, medically known as the brain attack, refers to the stoppage or stoppage of blood from flowing into a particular region of the brain, or even from the breaking of a vessel, causing injury to and death of areas of the brain. It presents a medical emergency, with the potential of severe long-term neurological impairment, disability, and even death; thus, urgent detection and treatment are needed.

Objective: The study aims to develop a novel Multilayer Perceptron of Convolutional Neural Network-based Residual Network (MLPCNNbRN) for early brain stroke detection, focusing on improving the accuracy and reliability of detecting subtle stroke patterns in medical images.

Methods: The MLPCNNbRN provided resented in the context of residual connections within an architecture designed for deep network training in medical images. This allowed the overall model to learn complex relations very effectively. The system was implemented in the Python framework. Its performance was compared with other methods. The key metrics used in the evaluation were accuracy, precision, recall, and F-score.

Results: The MLPCNNbRN model demonstrated superior performance compared to existing methods, achieving higher levels of accuracy in stroke detection. Specifically, the model improved overall accuracy, precision, recall, and F-score, showcasing its robustness in identifying subtle stroke patterns.

Conclusion: The proposed MLPCNNbRN system enhances early brain stroke detection by extracting hierarchical features and residual network learning, offering a more accurate and reliable approach than previous methods. This system has the potential to aid medical professionals in timely diagnosis and treatment, ultimately improving patient outcomes.

BackgroundTinnitus, a common auditory disorder, significantly impacts patient quality of life and lacks universally effective treatments. The integration of advanced imaging technology like ultrasound in therapeutic interventions offers new possibilities in healthcare.ObjectiveThis study evaluated the efficacy of ultrasound-guided stellate ganglion block as an innovative approach to managing tinnitus.MethodsEighty patients with tinnitus were randomly assigned to either a control group receiving standard drug therapy or an observation group treated with ultrasound-guided stellate ganglion block in addition to standard therapy. Key metrics, including clinical effectiveness rates, anxiety scores, and tinnitus disability index scores, were assessed pre- and post-treatment.ResultsPost-treatment outcomes revealed that the observation group exhibited significantly improved anxiety scores (38.74 ± 4.05 vs. 50.45 ± 4.86; P < 0.05) and tinnitus disability index scores (37.8 ± 17.56 vs. 50.4 ± 21.26; P < 0.05) compared to the control group. Additionally, the observation group achieved a 100% clinical efficacy rate, outperforming the control group's 84% (P < 0.05).ConclusionUltrasound-guided stellate ganglion block demonstrates superior efficacy in managing tinnitus compared to conventional drug therapy. This study underscores the potential of integrating advanced ultrasound technology into healthcare to optimize treatment outcomes for auditory disorders.

BackgroundCervical cancer is the fourth most common cause of women cancer deaths worldwide. The primary etiology of cervical cancer is the persistent infection of specific high-risk strains of the human papillomavirus. Liquid-based cytology is the established method for early detection of cervical cancer. The evaluation of cellular abnormalities at a microscopic level allows for the identification of malignant or precancerous features in liquid-based cytology pap smears. This technique is characterized by its time-consuming nature and susceptibility to both inter- and intra-observer variability. Hence, the utilization of Artificial Intelligence in computer-assisted diagnosis can reduce the duration needed for diagnosing this ailment, thereby eliminating delayed diagnosis and facilitating the implementation of an efficient treatment.ObjectiveThis research presents a new deep learning-based cervical cancer identification decision support system in liquid-based cytology smear images.MethodsThe proposed diagnosis support system incorporates a novel hybrid feature reduction and optimization module, which integrates a sparse Autoencoder with the Binary Harris Hawk metaheuristic optimization algorithm to select the most informative features from a supplemented feature set of the input images. The supplemented feature set is retrieved by three pretrained Convolutional Neural Networks. The module utilizes an improved feature set to conduct a Bayesian-optimized K Nearest Neighbors machine learning classification of cervical cancer in input Pap smears.ResultsThe introduced approach achieves a classification accuracy of 99.9% and demonstrates an improved ability to detect the stages of cervical cancer, with a sensitivity of 99.8%. In addition, the system has the ability to identify the lack of cervical cancer stages with a specificity rate of 99.9%.ConclusionThe proposed system outpaces recent deep learning-based cervical cancer identification systems.

BackgroundBrain tumors pose a significant healthcare challenge, necessitating early detection and precise monitoring to ensure effective treatment.ObjectivesThe study proposes an innovative technique with the integration of hybrid transfer learning with improved microwave imaging. The integration of special feature extraction abilities of pre-trained deep learning methods along with the high-resolution imaging capabilities of the patch antenna.MethodsIt was primarily composed of two phases. The initial stage involves the development of a patch antenna and head phantom model, which are then subjected to SAR analysis to extract pertinent features from transmitted signals. In the second stage, an AI-based detection model that utilizes MobileNet V2 is implemented. The images acquired by the patch antenna system are fed into MobileNet V2, which extracts high-level features by employing depth-wise separable convolutions and inverted residual blocks. The fully connected layer is used to classify brain tumors in an effective manner by passing these extracted features.ResultsThe results of the simulation indicate that the model performs exceptionally well, with an accuracy of 98.44%, precision of 98.03%, recall of 99.00%, F1-score of 98.52%, and specificity of 97.82%.ConclusionThis method offers a promising solution for the non-invasive and real-time detection of brain tumors, taking advantage of the electromagnetic properties of brain tissue and the capabilities of AI to address the limitations of current diagnostic methods, such as MRI and CT scans.

BackgroundHealth apps offer promising support for people with diabetes; however, the retention rates are low. Team-based apps and gamification can increase engagement and contribute to sustained use.ObjectiveThis pilot study explored how a team-based gamification app can support diabetes self-care.MethodsIndividuals with diabetes were introduced to a team-based gamification app that encourages the development of new habits. After 6 weeks of use, participants completed a questionnaire on system satisfaction, ease of use, enjoyment, usefulness for self-care, and burden, using a five-point scale. Qualitative data were also collected.ResultsOf the 32 participants, 65% were satisfied, 81% found it useful for lifestyle management, and 71% found it useful for exercise. The team system and challenge-tracking features were the most useful. Participants stated that the app provided emotional support and motivated healthy habits through social comparison; however, they also reported confusion in aligning team and individual needs.ConclusionsThe team-based gamification health app provided emotional support by team members who shared the same goals and motivated healthy lifestyle habits through social comparison.