Technology and Health Care最新文献

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.

BackgroundDigital healthcare's advance has underscored an urgent requirement for solid medical record quality control, critical for data integrity, surpassing manual methods' inadequacies.ObjectiveThe goal was to develop an AI system to manage medical record quality control comprehensively, using advanced AI like reinforcement learning and NLP to boost management's precision and efficiency.MethodsThis AI system uses a closed-loop framework for real-time record review using natural language processing techniques and reinforcement learning, synchronized with the hospital information system. It features a data layer for monitoring, a service layer for AI analysis, and a presentation layer for user engagement. Its impact was evaluated by comparing quality metrics pre- and post-deployment.ResultsWith the AI system, quality control became fully operational, with review times per record plummeting from 4200 s to 2 s. The share of Grade A records rose from 89.43% to 99.21%, and the system markedly minimized formal and substantive record errors, enhancing completeness and accuracy. The implementation of the artificial intelligence-based medical record quality control system optimizes the quality control process, dynamically regulates the diagnostic behavior of medical staff, and promotes the standardization and normalization of clinical medical record writing.ConclusionsThe AI-driven system significantly upgraded the management of medical records in terms of efficiency and accuracy. It provides a scalable approach for hospitals to refine quality control, propelling healthcare towards heightened intelligence and automation, and foreshadowing AI's pivotal role in future healthcare quality management.

Background: Peripherally Inserted Central Catheters (PICC) are widely used for long-term intravenous therapy in pediatric patients and are effective in preventing catheter displacement.

Objective: This study aimed to investigate the effect of magnetic navigation technology compared with ultrasound imaging and manual control of the catheter path.

Methods: The control group underwent PICC placement using the Seldinger technique under ultrasound guidance (n = 86), while the magnetic navigation group received magnet-assisted PICC placement (n = 80). Both groups used chest X-ray (CXR) after catheter placement to confirm the tip position. Insertion time, first-attempt success rate, complication rate, post-procedural pain, post-procedural anxiety, and family satisfaction were compared.

Results: Compared to the control group, magnetic navigation significantly reduced catheter insertion time (28.2 ± 3.67 min vs. 34.85 ± 2.94 min, P < 0.001), improved first-attempt success rate (91.25% vs. 41.86%, P < 0.001), and lowered the complication rate (21.25% vs. 66.28%, P < 0.001). In addition, magnetic navigation alleviated post-procedural pain and anxiety (P < 0.01), and improved family satisfaction (P < 0.01).

Conclusion: Compared to traditional ultrasound-guided methods, magnetic navigation offers superior efficiency in pediatric PICC placement, highlighting its promising potential for clinical application and broader implementation.

Introduction: Infective endocarditis (IE), despite advancements in diagnostic and therapeutic strategies, remains a life-threatening condition with high in-hospital mortality. The aim of this study was to assess an importance of a different echocardiographic techniques in the evaluation of patients with IE.

Methods: This prospective study included all consecutive patients hospitalized with a diagnosis of IE. Each patient underwent both 2D transesophageal echocardiography (2DTOE) and 3D transesophageal echocardiography (3DTOE) as part of the initial diagnostic evaluation. Laboratory results, isolated pathogens, and monitoring during hospitalization were also taken into account.

Results: The study included 59 patients (69.49% male, mean age 64.4 ± 16.0). Native valve endocarditis (NVE) was present in 32 (54.24%), prosthetic valve endocarditis (PVE) in 17 (28.81%), and cardiac device-related IE (CDIE) in 10 (16.95%). Blood cultures were positive in 72.4% of cases, with Enterococcus faecalis predominant in NVE, and Staphylococcus species in PVE (S. epidermidis) and CDIE (S. aureus) (p = 0.039). TOE provided detailed imaging, detecting more lesions, with 3D TOE excelling in identifying destructive lesions, particularly perforations (p < 0.001). Vegetations were most frequent in NVE and CDIE, while destructive lesions were more common in PVE (p < 0.05). 3D TOE identified longer vegetations and more destructive lesions, especially in PVE (p < 0.05).

Conclusion: 3D TOE, provide a detailed real time imaging, and could be considered as key adjunctive modality in practice when the cardiac anatomy is not precisely visualized by 2D TOE, particularly when advanced surgical planning is required.

BackgroundChatbots are increasingly integrated into healthcare, offering personalized and accessible health advice. However, the impact of factors such as chatbot authority, health information type, and interaction style on users' decision-making remains unclear.ObjectiveThis study aims to investigate how these elements influence users' willingness to adopt health advice provided by chatbots.MethodsA 2 × 2 × 2 factorial experiment was conducted with 480 university students to examine the effects of chatbot authority (authoritative vs. non-authoritative), health information type (preventive vs. treatment-related), and interaction style (formal vs. informal). Participants' willingness to adopt the health advice was measured before and after interacting with the chatbot.ResultsThe study found that a authoritative chatbot delivering treatment-related advice in a formal style significantly increased willingness to adopt the advice. Conversely, preventive information was more effective when presented informally by a non-authoritative chatbot. These results support the media evocation paradigm, which suggests that chatbots framed as authoritative figures evoke greater user engagement and trust in health contexts.ConclusionThe findings extend the media evocation paradigm by demonstrating that chatbot authority, information type, and interaction style should be aligned with the nature of health advice to maximize effectiveness. This study provides insights for designing chatbots that improve health decision-making by tailoring their communication strategies.

BackgroundLiver cancer is still one of the most common causes of death from cancer globally. The accurate segmentation of liver tumors from CT images is critical for diagnosis, treatment planning, and tracking. Conventional segmentation techniques frequently struggle to handle the intricacy of medical images, requiring the usage of sophisticated artificial intelligence (AI) methods to enhance accuracy and effectiveness.ObjectiveThe main objective of this study is to create and test an improved U-Net model (AM-UNet) that incorporates an attention mechanism to enhance the segmentation and classification accuracy of liver tumors in CT images. This method seeks to surpass previous techniques in terms of accuracy, precision, recall, and F1 score.MethodsThe dataset used includes 194 liver tumor CT scans obtained from 131 individuals for training and 70 for testing. The open-source 3DIRCAD-B dataset, which is incorporated into LiTS, contains images of both normal and pathological conditions. Preprocessing methods such as Median Filtering (MF) and Histogram Equalization (HE) were used to reduce noise and improve contrast. The AM-UNet model was then used to segment the tumors before classifying them as malignant or benign. The efficiency was assessed utilizing metrics like accuracy, precision, recall, F1-score, and ROC (Receiver Operating Characteristic).ResultsThe suggested AM-UNet model produced excellent outcomes, with a recall of 95%, accuracy of 92%, precision of 94%, and an F1-score of 93%. These metrics show that the model outperforms conventional techniques in correctly segmenting and classifying liver tumors in CT images.ConclusionThe AM-UNet model improves the segmentation and classification of liver tumors, providing substantial performance metrics over traditional methods. Its utilization can transform liver cancer diagnosis by assisting physicians in accurate tumor identification and treatment planning, resulting in improved patient results.

Background: Functional myocardial ischemia (FMI) in stable coronary artery disease (SCAD) remains a critical challenge in cardiovascular care. While fractional flow reserve (FFR) is a gold-standard diagnostic technology, its clinical adoption is limited by cost and complexity. Integrating technological biomarkers and advanced analytics could enhance risk stratification and guide precision interventions.

Objective: This study leverages data-driven methodologies to identify and validate technological biomarkers associated with FMI in SCAD, aiming to optimize clinical decision-making through predictive modeling.

Methods: A systematic search across PubMed, Embase, and Web of Science (inception-October 2023) identified studies evaluating SCAD and FMI.

Inclusion criteria: cohort/case-control studies (n ≥ 100) using FFR or angiographic technologies. Meta-analyses were conducted via RevMan 5.4 and Stata 16.0, employing fixed/random-effects models. Heterogeneity was assessed using I² statistics.

Results: Analysis of 15 studies (n = 4854) revealed that anatomical biomarkers-stenosis severity (DS%: SMD = 0.95, p < 0.0001), minimal lumen diameter (SMD = -1.33, p < 0.0001), and lesion length (SMD = 0.72, p < 0.0001)-were strongly linked to FMI. Diabetes (OR = 1.31, p = 0.003) and smoking (OR = 1.47, p < 0.0001) emerged as significant modifiable risks, while hypertension showed no association (p = 0.14). Age and gender disparities highlighted the need for personalized risk algorithms.

Conclusion: Technological biomarkers and data-driven modeling provide actionable insights into FMI risk in SCAD, bridging gaps between anatomical assessments and functional outcomes. Future integration of machine learning and predictive analytics could refine risk stratification, enabling tailored therapeutic strategies.