Clinical eHealth最新文献

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Advancements in digital data acquisition and CAD technology in Dentistry: Innovation, clinical Impact, and promising integration of artificial intelligence 牙科数字数据采集和CAD技术的进展：创新、临床影响和人工智能的有前途的集成

Clinical eHealth

Pub Date : 2025-03-24 DOI: 10.1016/j.ceh.2025.03.001

Mohammed Ahmed Alghauli , Waad Aljohani , Shahad Almutairi , Rola Aljohani , Ahmed Yaseen Alqutaibi

This review examines recent advancements in digital data acquisition and CAD technology in dentistry, highlighting improvements in communication, AI integration, and predictive analytics in diagnostic and treatment tools. Over the past decade, these innovations have enhanced workflow efficiency, enabling precise planning, automated processes, and faster treatment turnaround times. AI-enhanced CAD systems show significant promise for improving diagnostic accuracy and treatment outcomes. Utilizing these advanced technologies improved dental workflow, particularly the full digital workflow. Intraoral scanning, CBCT data acquisition, facial scanning, smile, and CAD design have revolutionized dental practice, rendering digital dentistry the primary daily routine.

The future of dentistry is entirely digital; virtual dental arches, virtual smiles, virtual articulators, and virtual patients are the face of the modern dental era. AI aids significantly in data acquisition, diagnosis, planning, and CAD designing. However, the review underscores the need for validation, monitoring, and ethical oversight to ensure safe and effective AI applications in clinical settings. It also emphasizes the importance of practitioners’ understanding of CAD components in CAD-CAM systems, facilitating informed technology selection to optimize treatment efficacy and patient outcomes.

本文综述了牙科领域数字数据采集和CAD技术的最新进展，重点介绍了诊断和治疗工具中通信、人工智能集成和预测分析方面的改进。在过去的十年中，这些创新提高了工作流程效率，实现了精确的计划、自动化流程和更快的处理周转时间。人工智能增强的CAD系统在提高诊断准确性和治疗效果方面显示出巨大的希望。利用这些先进的技术改进了牙科工作流程，特别是全数字工作流程。口腔内扫描、CBCT数据采集、面部扫描、微笑和CAD设计已经彻底改变了牙科实践，使数字牙科成为主要的日常工作。牙科的未来是完全数字化的；虚拟牙弓、虚拟微笑、虚拟发音器和虚拟患者是现代牙科时代的面孔。人工智能在数据采集、诊断、规划和CAD设计方面具有重要意义。然而，该审查强调需要验证、监测和伦理监督，以确保人工智能在临床环境中的安全有效应用。它还强调了从业者对CAD- cam系统中CAD组件的理解的重要性，促进了知情的技术选择，以优化治疗效果和患者预后。

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引用次数: 0

Factors influencing REducing Delay through edUcation on eXacerbations (REDUX) implementation: A stakeholder analysis 影响通过实施恶化教育减少延误的因素：利益相关者分析

Clinical eHealth

Pub Date : 2025-02-17 DOI: 10.1016/j.ceh.2025.02.001

Xiaoyue Song , Cynthia Hallensleben , Haibo Wang , Jun Guo , Weihong Zhang , Hongxia Shen , Robbert J.J. Gobbens , Niels H. Chavannes , Anke Versluis

REducing Delay through edUcation on eXacerbations (REDUX) shows promise in reducing exacerbation recognition and action delays for chronic lung diseases in the Netherlands. However, factors influencing its successful implementation in China remain unclear. To identify the perceived factors influencing nurse-led self-management implementation of REDUX in China, stakeholder analysis using qualitative and quantitative approaches was conducted. A qualitative approach assessed support for REDUX, perceived influencing factors, and preferred intervention delivery mode among patients, healthcare professionals, and policymakers. A quantitative approach identified necessary conditions for developing and implementing a digital-version intervention, involving app developers and cyber-security officers. The study followed COREQ and stakeholder analysis guidelines. Thirty-five patients, healthcare professionals, and policymakers highly supported REDUX. Perceived influencing factors included facilitators (e.g., easy-to-use design, perceived benefits) and barriers (e.g., patients’ affordability, lack of policy support). Preferred intervention delivery modes varied among stakeholders. Eighty-seven app developers and cyber-security officers completed quantitative surveys. The quantitative data showed that the work process of developing the health apps and protecting the users’ security and privacy mostly aligned with the related international guideline recommendations. The study identified key interdependent factors that were perceived as crucial for REDUX implementation success. Healthcare policies should prioritize self-management intervention, and minor action plan adjustments are needed.

在荷兰，通过急性加重教育减少延误（REDUX）在减少慢性肺病的急性加重识别和行动延误方面显示出希望。然而，影响其在中国成功实施的因素仍不清楚。为了确定影响中国护士主导的REDUX自我管理实施的感知因素，采用定性和定量方法进行了利益相关者分析。定性方法评估了患者、医疗保健专业人员和政策制定者对REDUX的支持、感知的影响因素和首选的干预交付模式。定量方法确定了开发和实施数字版本干预的必要条件，涉及应用程序开发人员和网络安全官员。该研究遵循COREQ和利益相关者分析指南。35名患者、医疗保健专业人员和政策制定者高度支持REDUX。感知到的影响因素包括促进因素（例如，易于使用的设计、感知到的好处）和障碍因素（例如，患者的负担能力、缺乏政策支持）。利益相关者偏好的干预交付模式各不相同。87名应用程序开发人员和网络安全官员完成了定量调查。定量数据显示，开发健康应用程序和保护用户安全和隐私的工作过程基本符合相关的国际指南建议。该研究确定了被认为对REDUX实现成功至关重要的关键相互依赖因素。医疗保健政策应优先考虑自我管理干预，并需要对行动计划进行微调。

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引用次数: 0

Glu4: An open-source package for real-time forecasting and alerting post-bariatric hypoglycemia based on continuous glucose monitoring Glu4：基于连续血糖监测的实时预测和预警减肥后低血糖的开源软件包

Clinical eHealth

Pub Date : 2025-01-17 DOI: 10.1016/j.ceh.2025.01.003

Luca Cossu , Francesco Prendin , Giacomo Cappon , David Herzig , Lia Bally , Andrea Facchinetti

Background

Post-bariatric hypoglycemia (PBH) is a severe and often overlooked complication of bariatric surgery (BS), characterized by dangerously low blood glucose levels after meals, particularly those high in carbohydrates. Unlike in Type 1 and Type 2 diabetes (T1D, T2D), where decision support systems (DSS) and continuous glucose monitoring (CGM) tools aid blood glucose management, no dedicated DSS exists for PBH. This leaves individuals vulnerable to recurrent, unpredictable hypoglycemia, posing significant health risks. To address this gap, we propose Glu4, an open-source software package designed to predict and notify users of impending PBH events using CGM data.

Methods

Glu4 employs a two-step approach to predict PBH. A run-to-run algorithm forecasts future glucose levels using past CGM data, identifying potential hypoglycemic events 30 min in advance. An intelligent alarm system alerts users when glucose levels are predicted to drop below a critical threshold, prompting preventive action. A pilot study involving three PBH patients collected real-time glucose data to validate the system’s predictive performance.

Results

The pilot study demonstrated that Glu4 reliably predicted impending hypoglycemia in all participants, providing timely alerts 30 min before glucose drops. The system showed a high specificity, with no false alarms being triggered during the monitoring period. The proactive notifications enabled participants to manage their glucose levels more effectively by taking preventive actions such as consuming rescue carbohydrates before the onset of severe hypoglycemia.

Conclusions

Glu4 represents a promising tool for managing PBH, leveraging CGM data to deliver accurate, timely alerts that enable proactive intervention. By improving safety and quality of life for individuals with PBH, Glu4 addresses a critical unmet need. Future work will focus on enhancing system capabilities and conducting larger-scale studies to validate its effectiveness and refine its usability for clinical adoption.

背景：减肥后低血糖（PBH）是减肥手术（BS）的一种严重且常被忽视的并发症，其特征是餐后血糖水平危险低，尤其是那些高碳水化合物的餐后。与1型和2型糖尿病（T1D, T2D）不同，决策支持系统（DSS）和连续血糖监测（CGM）工具有助于血糖管理，PBH没有专门的DSS。这使得个体容易出现反复的、不可预测的低血糖，造成重大的健康风险。为了解决这一差距，我们提出了Glu4，这是一个开源软件包，旨在使用CGM数据预测和通知用户即将发生的PBH事件。方法glu4采用两步法预测PBH。跑步到跑步算法使用过去的CGM数据预测未来的血糖水平，提前30分钟识别潜在的低血糖事件。智能警报系统会在血糖水平预计降至临界阈值以下时向用户发出警报，提示采取预防措施。一项涉及三名PBH患者的试点研究收集了实时血糖数据，以验证该系统的预测性能。结果初步研究表明，Glu4可靠地预测所有参与者即将发生的低血糖，在血糖下降前30分钟提供及时警报。该系统具有较高的特异性，在监测期间无误报发生。主动通知使参与者能够通过采取预防措施，如在严重低血糖发作前摄入救援碳水化合物，更有效地控制血糖水平。结论：glu4是一种很有前途的PBH管理工具，利用CGM数据提供准确、及时的警报，从而实现主动干预。通过提高PBH患者的安全性和生活质量，Glu4解决了一个关键的未满足的需求。未来的工作将集中在增强系统能力和开展更大规模的研究，以验证其有效性和完善其临床应用的可用性。

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引用次数: 0

Enhancing thyroid disease prediction and comorbidity management through advanced machine learning frameworks 通过先进的机器学习框架加强甲状腺疾病预测和合并症管理

Clinical eHealth

Pub Date : 2025-01-16 DOI: 10.1016/j.ceh.2025.01.002

P. Sanju , N. Syed Siraj Ahmed , P. Ramachandran , P. Mohamed Sajid , R. Jayanthi

Thyroid disease is one of the most prevalent endocrine disorders worldwide, necessitating precise and efficient diagnostic models for improved clinical outcomes. This study proposes a Hybrid Feature Selection and Deep Learning Framework (HFSDLF) that integrates Random Forests with Principal Component Analysis (PCA) and L1 regularization for effective feature selection and classification. Utilizing the UCI Thyroid Dataset, the framework combines the strengths of deep learning-based feature extraction and traditional machine learning classifiers. The Random Forest classifier achieved the highest accuracy of 96.30 %, outperforming other models such as Decision Trees and Logistic Regression, with notable improvements in sensitivity and specificity. The novelty of this work lies in its hybrid approach to feature selection, which reduces dimensionality while retaining the most informative features, and its application of an optimized Random Forest model for enhanced classification accuracy. Comparative analysis with existing methods further highlights the superiority of the proposed framework in terms of accuracy and processing efficiency. This research addresses key limitations of existing approaches and contributes to the field by demonstrating a scalable and interpretable solution for thyroid disease diagnosis. The proposed framework provides a benchmark for future studies, underscoring the importance of hybrid methodologies in medical data analysis.

甲状腺疾病是世界上最常见的内分泌疾病之一，需要精确和有效的诊断模型来改善临床结果。本研究提出了一种混合特征选择和深度学习框架（HFSDLF），该框架将随机森林与主成分分析（PCA）和L1正则化相结合，用于有效的特征选择和分类。该框架利用UCI甲状腺数据集，结合了基于深度学习的特征提取和传统机器学习分类器的优势。随机森林分类器达到了96.30%的最高准确率，优于决策树和逻辑回归等其他模型，在灵敏度和特异性方面都有显著提高。这项工作的新颖之处在于其混合的特征选择方法，在保留最具信息量的特征的同时降低了维数，并应用了优化的随机森林模型来提高分类精度。通过与现有方法的对比分析，进一步突出了该框架在精度和处理效率方面的优势。本研究解决了现有方法的关键局限性，并通过展示可扩展和可解释的甲状腺疾病诊断解决方案，为该领域做出了贡献。拟议的框架为今后的研究提供了基准，强调了混合方法在医疗数据分析中的重要性。

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