Clinical eHealth

Lian Wu

Dan Xiao

Weipen Jiang

Zhihao Jian

Katherine Song

Dawei Yang

Niels H. Chavannes

Chunxue Bai

Expert Consensus for Smoking Cessation with Metaverse in Medicine

医学元宇宙戒烟专家共识

Lung cancer is the leading cause of cancer deaths. Although targeted therapies and programmed cell death protein 1 (PD-1) blockade have offered great advances, five-year survival rates remained low. Diagnosis of lung cancer at an earlier stage is the most efficient approach to improve survival. Tumor associated autoantibodies (TAAb) have been proven a promising innovative biomarker of lung cancer. We aimed to comprehensively evaluate the sensitivity, specificity, and accuracy of TAAbs in lung cancer detection, especially in early-stage patients, through a multicenter prospective observational clinical trial, and to screen out the novel combination of TAAbs with the best detection value for Chinese population. We aimed to enroll 1,400 participants from three clinical centers and divide into two cohorts. One cohort is participants with newly pathologically confirmed lung cancer as the case cohort, and the other is participants with benign nodules, matched healthy controls and other benign lung diseases as the control cohort. Cases and controls were randomly distributed into training or validation set. The level of 14 autoantibody candidates in their plasma were detected. A Monte-Carlo Simulated Annealing method was implemented to develop a composite panel of autoantibodies to distinguish between matched lung cancer cases and controls in the training set. The newly developed autoantibody panel was tested in the validation set for sensitivity, specificity and accuracy. This is the first and largest clinical trial designed to develop a novel autoantibody panel for lung cancer detection specifically for Chinese people. We performed a multicenter, prospective, observational study to find a novel panel of autoantibody markers that can help the diagnosis of lung cancer and the characterization of pulmonary nodules in Chinese population. The results may help to provide evidence-based recommendations to clinicians for lung cancer early detection and pulmonary nodule management. This study is registered in the ClinicalTrials.gov (NCT04216511).

Lin Tong

Jiayuan Sun

Xiaoju Zhang

Di Ge

Yimin Yang

Jian Zhou

Dong Wang

Xin Hu

Hao Liu

Diagnostic value of tumor associated autoantibody panel in early detection of lung cancer in Chinese population: Protocol for a prospective, observational, and multicenter clinical trial

中国人群中肿瘤相关自身抗体谱在早期肺癌检测中的诊断价值：一项前瞻性、观察性和多中心临床试验方案

The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans. This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model. We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results.

Tao Xu

Xun Wang

Deng Chen

Ziqiang Zhang

Lichuan Zhang

Jie Liu

Kui Xiao

Li Bai

Yong Zhang

Lin Zhao

Chaomin Wu

Yaoli Wang

Chunling Dong

Maosong Ye

Yu Xu

Zhenju Song

Hong Chen

Jing Li

Jiwei Wang

Fei Tan

Hai Yu

Chunhua Du

Hongqing Zhao

Yu Shang

Linian Huang

Jianping Zhao

Yang Jin

Charles A. Powell

Jinming Yu

Yuanlin Song

A large-scale clinical validation study using nCapp cloud plus terminal by frontline doctors for the rapid diagnosis of COVID-19 and COVID-19 pneumonia in China

大规模临床验证研究：一线医生使用nCapp云加终端在中国快速诊断COVID-19及COVID-19肺炎

Bipolar disorder (BD) is a complex and severe mental illness with high mortality and relapse rates. Psychoeducational interventions based on scientific evidence could increase treatment adherence rates through a better comprehension of the illness. The aim of this study is to investigate the efficacy of a digital intervention using a mobile phone application as adjunctive treatment for BD. The study has been conducted since March 2019 and is divided in three phases: (1) mobile application development process and assessment, (2) pilot trial, and (3) evaluation and controlled trial. During the first phase MyBee app was developed with the following axes: general information about BD and its course, BD comorbidities and available treatments, lifestyle, sleep quality, diet, suicide and stigma, stress management, and relaxation exercises based on mindfulness techniques. The second phase was the 12-week long pilot trial, which used a quasi-experimental design and aimed to evaluate both the content of the digital intervention and the functionality of the MyBee app. Following, the controlled trial was conducted to compare the digital intervention through a mobile phone application as adjunctive treatment for BD, with a control group without intervention over a period of 12 weeks. Online-based strategies through mobile applications have been an important strategy to monitor symptoms, offer self-management, improve treatment adherence, and prevent relapse among BD patients.

Caroline Encinas Audibert

Adna de Moura Fereli Reis

Robson Zazula

Regina Célia Bueno Rezende Machado

Suzana Maria Menezes Guariente

Sandra Odebrecht Vargas Nunes

Development of digital intervention through a mobile phone application as an adjunctive treatment for bipolar disorder: MyBee project

移动电话应用程序作为双相障碍辅助治疗的数字干预开发：MyBee项目

With the rapid development of telemedicine equipment and information communication technology, telemedicine has developed rapidly and is used widely around the world as a new mode of medical service. This new technology has been used widely in China and has benefited a large number of patients. Due to the hospital-centric structure in health sector, resources and patients became increasingly concentrated in hospitals instead of primary care. In this way, telemedicine is a crucial method for government to the inequality of medical resources between urban and rural areas. But little is known about the influence of telemedicine on public primary hospitals in China. The objective of this study is to review the influence of telemedicine on capacity development of respiratory department in public primary hospital in China. A scoping review was conducted using electronic databases including PubMed, Cochrane Library, EMBASE and Google Scholar. Telemedicine has a major impact on the establishment of a complete national health system. The immediate sharing of medical information effectively alleviates the situation of serious information imbalance between doctors and patients. It also guides the public to make rational choice of medical behaviour. In public emergencies, telemedicine can quickly concentrate all kinds of high-quality medical resources to the sites, ensure the safety of people's lives, and provide effective rescue and treatment for the wounded in various special environments. It is conducive to giving full play to the market competition value of public hospital reputation, encouraging hospitals to continuously improve medical technology level, improving medical service quality, actively taking social medical and health responsibility, and this could promote the overall medical and health capacities development.

Junyue Zhang

Qingjun Lu

Leiyu Shi

The influence of telemedicine on capacity development in public primary hospitals in China: A scoping review

远程医疗对中国公立基层医院能力建设的影响：范围综述

Juan S. Izquierdo-Condoy

Jorge Vásconez-González

Esteban Ortiz-Prado

“AI et al.” The perils of overreliance on Artificial Intelligence by authors in scientific research

“AI等人” 科学研究中作者过度依赖人工智能的危险

Seyed Matin Malakouti

Mohammad Bagher Menhaj

Amir Abolfazl Suratgar

Machine learning and transfer learning techniques for accurate brain tumor classification

机器学习和迁移学习技术在脑肿瘤分类中的准确应用

Andrea Mangion

Bruno Ivasic

Neil Piller

A systematic review of eHealth and mHealth interventions for lymphedema patients

淋巴水肿患者电子健康和移动健康干预措施的系统综述

The emergence of the Internet of Things (IoT) has sparked a profound transformation in the field of digital health, leading to the rise of the Internet of Medical Things (IoMT). These IoT applications, while promising significant enhancements in patient care and health outcomes, simultaneously present a myriad of ethical dilemmas. This paper aims to address these ethical challenges by introducing the Adaptive Ethical Framework for IoT in Digital Health (AEFIDH), a comprehensive evaluation framework designed to examine the ethical implications of IoT technologies within digital health contexts. The AEFIDH is developed using a mixed-methods approach, encompassing expert consultations, surveys, and interviews. This approach was employed to validate and refine the AEFIDH, ensuring it encapsulates critical ethical dimensions, including data privacy, informed consent, user autonomy, algorithmic fairness, regulatory compliance, ethical design, and equitable access to healthcare services. The research reveals pressing issues related to data privacy, security, and user autonomy and highlights the imperative need for an increased focus on algorithmic transparency and the integration of ethical considerations in the design and development of IoT applications. Despite certain limitations, the AEFIDH provides a promising roadmap for guiding the responsible development, deployment, and utilization of IoT technologies in digital health, ensuring its relevance amidst the rapidly evolving digital health landscape. This paper contributes a novel, dynamic framework that encapsulates current ethical considerations and is designed to adapt to future technological evolutions, thereby fostering ethical resilience in the face of ongoing digital health innovation. The framework's inherent adaptability allows it to evolve in tandem with technological advancements, positioning it as an invaluable tool for stakeholders navigating the ethical terrain of IoT in healthcare.

Abubakar Wakili

Sara Bakkali

Internet of Things in healthcare: An adaptive ethical framework for IoT in digital health

医疗物联网：数字健康中物联网的自适应伦理框架

涉及主题	计算机科学医学政治学法学心理学内科学经济生物精神科经济增长病理医疗保健操作系统哲学护理部人工智能工程类万维网
出版信息	出版商: KeAi Communications Co.，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2021，原创研究文献占比: 0.00%，自引率:N.A.， Gold OA占比: 0.00%
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Clinical eHealth

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Expert Consensus for Smoking Cessation with Metaverse in Medicine

Diagnostic value of tumor associated autoantibody panel in early detection of lung cancer in Chinese population: Protocol for a prospective, observational, and multicenter clinical trial

A large-scale clinical validation study using nCapp cloud plus terminal by frontline doctors for the rapid diagnosis of COVID-19 and COVID-19 pneumonia in China

Development of digital intervention through a mobile phone application as an adjunctive treatment for bipolar disorder: MyBee project

The influence of telemedicine on capacity development in public primary hospitals in China: A scoping review

Expert Consensus for Smoking Cessation with Metaverse in Medicine

“AI et al.” The perils of overreliance on Artificial Intelligence by authors in scientific research

Machine learning and transfer learning techniques for accurate brain tumor classification

A systematic review of eHealth and mHealth interventions for lymphedema patients

Internet of Things in healthcare: An adaptive ethical framework for IoT in digital health

ISSN	2588-9141
h-index	6