<p>The rapid evolution of digital health technologies has sparked transformative changes across the healthcare landscape. These advancements were at the heart of discussions during the recent academic conference co-organized by The First Affiliated Hospital, Sun Yat-sen University (FAH-SYSU) and University of California at Berkeley, the Pacific-Rim Conference on Healthcare Innovation (PRC-HI 2024), convening under the theme “The Future of Medicine: Integrating Robotics, AI and Healthcare.” This article distills the key developments and their implications for the future of healthcare, focusing on innovations in robotic surgery, health data science, and AI for medicine.</p><p>Robotic surgery has become a cornerstone of modern surgical practices, offering enhanced precision, reduced recovery times, and lower complication rates. Dr. Xiaoyu Yin detailed advancements in robot-assisted pancreatic surgeries at FAH-SYSU, emphasizing the hospital's extensive experience with the Da Vinci surgical system. Since 2015, Dr. Yin has performed over 1000 robotic surgeries, including nearly 700 pancreatic resections. These procedures included advanced techniques such as robot-assisted Whipple procedures, organ-preserving pancreatectomies, and total pancreatectomies. His presentation highlighted the learning curves associated with these complex procedures, showcasing research on iterative improvements in surgical outcomes through case refinement and skill enhancement [<span>1, 2</span>].</p><p>Similarly, Dr. Junhang Luo presented a novel “gradual renal segmental artery off-clamping” technique for treating large renal tumors. By utilizing preoperative computed tomography (CT) reconstructions, the technique identifies renal arterial branches, allowing surgeons to precisely minimize ischemia to healthy tissue while ensuring effective tumor removal. Clinical data revealed significantly shorter ischemia times, reduced blood loss, and improved long-term renal function compared to traditional methods.</p><p>Dr. Qingbo Huang shared groundbreaking work on robotic telesurgery, particularly focusing on its applications in regions with limited medical resources. Through successful demonstrations of remote surgeries between Beijing and distant locations such as Sanya, Dr. Huang's research highlighted how low-latency communication networks and advanced robotic systems can overcome geographical barriers [<span>3</span>].</p><p>Dr. Chao Cheng discussed the application of robotic surgery in thoracic procedures, particularly for lung cancer and large thymoma. His presentation highlighted how robotic systems enhance surgical precision and reduce recovery times, with notable success in segmentectomies and thymectomies [<span>4</span>]. The integration of 3D visualization and enhanced dexterity offered by robotic systems has transformed the management of challenging thoracic cases [<span>5, 6</span>].</p><p>Dr. Peter Nyirady presented on the potential of robotic surgery in addressing gl
{"title":"Innovations in Digital Health From a Global Perspective: Proceedings of PRC-HI 2024","authors":"Xiaoru Feng, Yu Sun, You Wu, Haibo Wang, Yang Wu","doi":"10.1002/hcs2.128","DOIUrl":"https://doi.org/10.1002/hcs2.128","url":null,"abstract":"<p>The rapid evolution of digital health technologies has sparked transformative changes across the healthcare landscape. These advancements were at the heart of discussions during the recent academic conference co-organized by The First Affiliated Hospital, Sun Yat-sen University (FAH-SYSU) and University of California at Berkeley, the Pacific-Rim Conference on Healthcare Innovation (PRC-HI 2024), convening under the theme “The Future of Medicine: Integrating Robotics, AI and Healthcare.” This article distills the key developments and their implications for the future of healthcare, focusing on innovations in robotic surgery, health data science, and AI for medicine.</p><p>Robotic surgery has become a cornerstone of modern surgical practices, offering enhanced precision, reduced recovery times, and lower complication rates. Dr. Xiaoyu Yin detailed advancements in robot-assisted pancreatic surgeries at FAH-SYSU, emphasizing the hospital's extensive experience with the Da Vinci surgical system. Since 2015, Dr. Yin has performed over 1000 robotic surgeries, including nearly 700 pancreatic resections. These procedures included advanced techniques such as robot-assisted Whipple procedures, organ-preserving pancreatectomies, and total pancreatectomies. His presentation highlighted the learning curves associated with these complex procedures, showcasing research on iterative improvements in surgical outcomes through case refinement and skill enhancement [<span>1, 2</span>].</p><p>Similarly, Dr. Junhang Luo presented a novel “gradual renal segmental artery off-clamping” technique for treating large renal tumors. By utilizing preoperative computed tomography (CT) reconstructions, the technique identifies renal arterial branches, allowing surgeons to precisely minimize ischemia to healthy tissue while ensuring effective tumor removal. Clinical data revealed significantly shorter ischemia times, reduced blood loss, and improved long-term renal function compared to traditional methods.</p><p>Dr. Qingbo Huang shared groundbreaking work on robotic telesurgery, particularly focusing on its applications in regions with limited medical resources. Through successful demonstrations of remote surgeries between Beijing and distant locations such as Sanya, Dr. Huang's research highlighted how low-latency communication networks and advanced robotic systems can overcome geographical barriers [<span>3</span>].</p><p>Dr. Chao Cheng discussed the application of robotic surgery in thoracic procedures, particularly for lung cancer and large thymoma. His presentation highlighted how robotic systems enhance surgical precision and reduce recovery times, with notable success in segmentectomies and thymectomies [<span>4</span>]. The integration of 3D visualization and enhanced dexterity offered by robotic systems has transformed the management of challenging thoracic cases [<span>5, 6</span>].</p><p>Dr. Peter Nyirady presented on the potential of robotic surgery in addressing gl","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"4 1","pages":"66-69"},"PeriodicalIF":0.0,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hcs2.128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Diyue Liu, Siyuan Fan, Xincheng Huang, Wenjing Gu, Yifan Yin, Ziyi Zhang, Baotong Ma, Ruitong Xia, Yuanwei Lu, Jingwen Liu, Hanjia Xin, Yumeng Cao, Saier Yang, Runqing Li, Han Li, Ji Zhao, Jin Zhang, Zheng Gao, Yaxin Zeng, Yixiao Ding, Zhuolun Ren, Yan Guan, Na Zhang, Jia Li, Yan Ma, Pei Wei, Jingjing Dong, Yajing Zhou, Yong Dong, Yan Qian, Chen Chen, Yujie Zhao, Yimiao Li, Yujia Zheng, Rongyi Chen, Xiaomeng Li, Yuke Han, Yaoyao Xia, Huixin Xu, Zhaolin Wu, Mingyou Wu, Xinrui Wu, Junyi Hou, Yuelai Cai, Xiaofan Dai, Wenbo Li, Ting Nie, Chongzhe Zhang, Xiaoya Wang, Dan Li, Siyao Yan, Zhiheng Yi, Chenxi Liu, Xinyue Zhang, Lei Shi, Haomiao Li, Feng Jiang, Xiaoming Zhou, Xinying Sun, Yibo Wu, Psychology and Behavior Investigation of Chinese Residents project team
Introduction
This study protocol specifies the primary research line and theoretical framework of the 2023 Survey of the Psychology and Behavior of the Chinese Population. It aims to establish a consistent database of Chinese residents' psychological and behavioral surveys through multi-center and large-sample cross-sectional surveys to provide robust data support for developing research in related fields. It will track the public's physical and psychological health more comprehensively and systematically.
Methods
The study was conducted from June 20, 2023 to August 31, 2023, using stratified and quota sampling methods. A total of 150 cities across 800 communities/villages were surveyed, selected from China (Despite extensive coordination, we have been unable to contact our counterparts in the Taiwan region of China to obtain relevant statistical data). The questionnaires were distributed to the public one-on-one and face-to-face by trained surveyors. The questionnaires included basic information about the individual, personal health status, basic information about the family, the social environment in which the individual lives, psychological condition scales, behavioral level scales, other scales, and attitudes towards topical social issues. Supervisors conducted quality control during the distribution process and returned questionnaires, logically checked and cleaned for data analysis.
Discussion
Data collection has been finished, and scientific outputs based on this data will support the development of health promotion strategies in China and globally. In the aftermath of the pandemic, it will guide policymakers and healthcare organizations to improve their existing policies and services to maximize the physical and mental health of the Chinese population.
Trial Registration
This study was filed in the National Health Security Information Platform (Record No.: MR-37-23-017876) and officially registered in the China Clinical Trials Registry (Registration No.: ChiCTR2300072573).
{"title":"Study protocol: A national cross-sectional study on psychology and behavior investigation of Chinese residents in 2023","authors":"Diyue Liu, Siyuan Fan, Xincheng Huang, Wenjing Gu, Yifan Yin, Ziyi Zhang, Baotong Ma, Ruitong Xia, Yuanwei Lu, Jingwen Liu, Hanjia Xin, Yumeng Cao, Saier Yang, Runqing Li, Han Li, Ji Zhao, Jin Zhang, Zheng Gao, Yaxin Zeng, Yixiao Ding, Zhuolun Ren, Yan Guan, Na Zhang, Jia Li, Yan Ma, Pei Wei, Jingjing Dong, Yajing Zhou, Yong Dong, Yan Qian, Chen Chen, Yujie Zhao, Yimiao Li, Yujia Zheng, Rongyi Chen, Xiaomeng Li, Yuke Han, Yaoyao Xia, Huixin Xu, Zhaolin Wu, Mingyou Wu, Xinrui Wu, Junyi Hou, Yuelai Cai, Xiaofan Dai, Wenbo Li, Ting Nie, Chongzhe Zhang, Xiaoya Wang, Dan Li, Siyao Yan, Zhiheng Yi, Chenxi Liu, Xinyue Zhang, Lei Shi, Haomiao Li, Feng Jiang, Xiaoming Zhou, Xinying Sun, Yibo Wu, Psychology and Behavior Investigation of Chinese Residents project team","doi":"10.1002/hcs2.125","DOIUrl":"10.1002/hcs2.125","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>This study protocol specifies the primary research line and theoretical framework of the 2023 Survey of the Psychology and Behavior of the Chinese Population. It aims to establish a consistent database of Chinese residents' psychological and behavioral surveys through multi-center and large-sample cross-sectional surveys to provide robust data support for developing research in related fields. It will track the public's physical and psychological health more comprehensively and systematically.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The study was conducted from June 20, 2023 to August 31, 2023, using stratified and quota sampling methods. A total of 150 cities across 800 communities/villages were surveyed, selected from China (Despite extensive coordination, we have been unable to contact our counterparts in the Taiwan region of China to obtain relevant statistical data). The questionnaires were distributed to the public one-on-one and face-to-face by trained surveyors. The questionnaires included basic information about the individual, personal health status, basic information about the family, the social environment in which the individual lives, psychological condition scales, behavioral level scales, other scales, and attitudes towards topical social issues. Supervisors conducted quality control during the distribution process and returned questionnaires, logically checked and cleaned for data analysis.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>Data collection has been finished, and scientific outputs based on this data will support the development of health promotion strategies in China and globally. In the aftermath of the pandemic, it will guide policymakers and healthcare organizations to improve their existing policies and services to maximize the physical and mental health of the Chinese population.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Trial Registration</h3>\u0000 \u0000 <p>This study was filed in the National Health Security Information Platform (Record No.: MR-37-23-017876) and officially registered in the China Clinical Trials Registry (Registration No.: ChiCTR2300072573).</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 6","pages":"475-492"},"PeriodicalIF":0.0,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Population aging presents a growing societal challenge and imposes a heavy burden on the healthcare system in many Asian countries. Given the limited availability of formal long-term care (LTC) facilities and personnel, family caregivers play a vital role in providing care for the increasing population of older adults. While awareness of the challenges faced by caregivers is rising, discussions often remain within academic circles, resulting in the lived experiences, well-being, and needs of family caregivers being frequently overlooked. In this review, we identify four key priority areas to advance research, practice, and policy related to family caregivers in Asia: (1) Emphasizing family caregivers as sociocultural navigators in the healthcare system; (2) addressing the mental and physical health needs of family caregivers; (3) recognizing the diverse caregiving experiences across different cultural backgrounds, socioeconomic status, and countries of residence; and (4) strengthening policy support for family caregivers. Our review also identifies deficiencies in institutional LTC and underscores the importance of providing training and empowerment to caregivers. Policymakers, practitioners, and researchers interested in supporting family caregivers should prioritize these key areas to tackle the challenge of population aging in Asian countries. Cross-country knowledge exchange and capacity development are crucial for better serving both the aging population and their caregivers.
{"title":"Caregiving in Asia: Priority areas for research, policy, and practice to support family caregivers","authors":"Nan Jiang, Bei Wu, Yan Li","doi":"10.1002/hcs2.124","DOIUrl":"10.1002/hcs2.124","url":null,"abstract":"<p>Population aging presents a growing societal challenge and imposes a heavy burden on the healthcare system in many Asian countries. Given the limited availability of formal long-term care (LTC) facilities and personnel, family caregivers play a vital role in providing care for the increasing population of older adults. While awareness of the challenges faced by caregivers is rising, discussions often remain within academic circles, resulting in the lived experiences, well-being, and needs of family caregivers being frequently overlooked. In this review, we identify four key priority areas to advance research, practice, and policy related to family caregivers in Asia: (1) Emphasizing family caregivers as sociocultural navigators in the healthcare system; (2) addressing the mental and physical health needs of family caregivers; (3) recognizing the diverse caregiving experiences across different cultural backgrounds, socioeconomic status, and countries of residence; and (4) strengthening policy support for family caregivers. Our review also identifies deficiencies in institutional LTC and underscores the importance of providing training and empowerment to caregivers. Policymakers, practitioners, and researchers interested in supporting family caregivers should prioritize these key areas to tackle the challenge of population aging in Asian countries. Cross-country knowledge exchange and capacity development are crucial for better serving both the aging population and their caregivers.</p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 6","pages":"374-382"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The COVID-19 pandemic presented unparalleled challenges to prompt and adaptive responses from nations worldwide. This review examines China's multifaceted approach to the crisis, focusing on five key areas of response: infrastructure and system design, medical care and treatment, disease prevention and control, economic and social resilience, and China's engagement in global health. This review demonstrates the effectiveness of a top-down command system at the national level, intersectoral coordination, a legal framework, and public social governance. This study also examines medical care and treatment strategies, highlighting the importance of rapid emergency response, evidence-based treatment, and well-planned vaccination rollout. Further discussion on disease prevention and control measures emphasizes the importance of adaptive measures, timely infection control, transmission interruption, population herd immunity, and technology applications. Socioeconomic impact was also assessed, detailing strategies for disease prevention, material supply, livelihood preservation, and social economy revival. Lastly, we examine China's contributions to the global health community, with a focus on knowledge-sharing, information exchange, and multilateral assistance. While it is true that each nation's response must be tailored to its own context, there are universal lessons to be drawn from China's approach. These insights are pivotal for enhancing global health security, especially as the world navigates evolving health crises.
{"title":"Innovative public strategies in response to COVID-19: A review of practices from China","authors":"You Wu, Zijian Cao, Jing Yang, Xinran Bi, Weiqing Xiong, Xiaoru Feng, Yue Yan, Zeyu Zhang, Zongjiu Zhang","doi":"10.1002/hcs2.122","DOIUrl":"10.1002/hcs2.122","url":null,"abstract":"<p>The COVID-19 pandemic presented unparalleled challenges to prompt and adaptive responses from nations worldwide. This review examines China's multifaceted approach to the crisis, focusing on five key areas of response: infrastructure and system design, medical care and treatment, disease prevention and control, economic and social resilience, and China's engagement in global health. This review demonstrates the effectiveness of a top-down command system at the national level, intersectoral coordination, a legal framework, and public social governance. This study also examines medical care and treatment strategies, highlighting the importance of rapid emergency response, evidence-based treatment, and well-planned vaccination rollout. Further discussion on disease prevention and control measures emphasizes the importance of adaptive measures, timely infection control, transmission interruption, population herd immunity, and technology applications. Socioeconomic impact was also assessed, detailing strategies for disease prevention, material supply, livelihood preservation, and social economy revival. Lastly, we examine China's contributions to the global health community, with a focus on knowledge-sharing, information exchange, and multilateral assistance. While it is true that each nation's response must be tailored to its own context, there are universal lessons to be drawn from China's approach. These insights are pivotal for enhancing global health security, especially as the world navigates evolving health crises.</p>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 6","pages":"383-408"},"PeriodicalIF":0.0,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Haihong Zhang, You Wu, Haibo Wang, Weili Zhao, Yali Cong
<p>On October 19, 2024, the 75th General Assembly of the World Medical Association (WMA) in Finland adopted the latest version of the Declaration of Helsinki (DoH)—Ethical Principles for Medical Research Involving Human Participants [<span>1</span>] (hereafter referred to as “the Declaration”). This revision process took 30 months, with the working group comprising representatives from medical associations in 19 countries and regions. From April 2022 to September 2024, the working group held eight regional expert meetings and two times global consultations, gathering suggestions from both experts and the general public [<span>2</span>]. Besides, the working group developed regular online meeting working mechanisms.</p><p>Basically, the general ethical principles for respecting and protecting human participants are stable over time, while more tailored interpretations and justifications should be adapted in a timely manner. The revision focused on alignment with wide-accepted ethical guidelines at the international level, emphasizing coherence with other related documents within, and beyond the WMA. The Declaration emphasises the overarching principles and does not delve into many specifics; however, its core principles remain universally applicable.</p><p>While these revisions represent significant progress, some also reflect substantial compromises. Notably, to strengthen the protection of research participants’ rights and well-being, the Declaration reaffirms that “<i>These purposes can never take precedence over the rights and interests of individual research participants</i>” and requires all stakeholders, including individuals, teams, and organizations involved in medical research to adhere to ethical principles that respect for and protect of research participants [<span>7</span>]. Given the WMA's mandate as a global organization of physicians, the term “<i>medical research</i>” was retained rather than adopting broader terminology such as “health-related research.” However, the document refers to “<i>physicians</i>,” or “<i>physicians and other researchers</i>” in constituent paragraphs, acknowledging both the critical role of physicians in medical practices and the specialized division of roles in research involving human participants. Compared to the 2013 version, the new Declaration strengthens researchers' responsibilities and protections for research participants (e.g., Articles 9, 10, 12, 17, 21, 23, 32, and 34), increasing the instances of the word “must” from 46 to 58 and clarifying the distinction between “<i>should</i>” and “<i>must</i>.”</p><p>Renaming “<i>subjects</i>” as “<i>participants</i>” not only mandates respect for participants' rights and agency but also calls for a partnership between researchers and participants. Developing such partnership requires that physicians/researchers strictly fulfill their duties, with the best interests of patients, including those participating in research, as the priority, promoting and
{"title":"Sixty years of ethical evolution: The 2024 revision of the Declaration of Helsinki (DoH)","authors":"Haihong Zhang, You Wu, Haibo Wang, Weili Zhao, Yali Cong","doi":"10.1002/hcs2.126","DOIUrl":"10.1002/hcs2.126","url":null,"abstract":"<p>On October 19, 2024, the 75th General Assembly of the World Medical Association (WMA) in Finland adopted the latest version of the Declaration of Helsinki (DoH)—Ethical Principles for Medical Research Involving Human Participants [<span>1</span>] (hereafter referred to as “the Declaration”). This revision process took 30 months, with the working group comprising representatives from medical associations in 19 countries and regions. From April 2022 to September 2024, the working group held eight regional expert meetings and two times global consultations, gathering suggestions from both experts and the general public [<span>2</span>]. Besides, the working group developed regular online meeting working mechanisms.</p><p>Basically, the general ethical principles for respecting and protecting human participants are stable over time, while more tailored interpretations and justifications should be adapted in a timely manner. The revision focused on alignment with wide-accepted ethical guidelines at the international level, emphasizing coherence with other related documents within, and beyond the WMA. The Declaration emphasises the overarching principles and does not delve into many specifics; however, its core principles remain universally applicable.</p><p>While these revisions represent significant progress, some also reflect substantial compromises. Notably, to strengthen the protection of research participants’ rights and well-being, the Declaration reaffirms that “<i>These purposes can never take precedence over the rights and interests of individual research participants</i>” and requires all stakeholders, including individuals, teams, and organizations involved in medical research to adhere to ethical principles that respect for and protect of research participants [<span>7</span>]. Given the WMA's mandate as a global organization of physicians, the term “<i>medical research</i>” was retained rather than adopting broader terminology such as “health-related research.” However, the document refers to “<i>physicians</i>,” or “<i>physicians and other researchers</i>” in constituent paragraphs, acknowledging both the critical role of physicians in medical practices and the specialized division of roles in research involving human participants. Compared to the 2013 version, the new Declaration strengthens researchers' responsibilities and protections for research participants (e.g., Articles 9, 10, 12, 17, 21, 23, 32, and 34), increasing the instances of the word “must” from 46 to 58 and clarifying the distinction between “<i>should</i>” and “<i>must</i>.”</p><p>Renaming “<i>subjects</i>” as “<i>participants</i>” not only mandates respect for participants' rights and agency but also calls for a partnership between researchers and participants. Developing such partnership requires that physicians/researchers strictly fulfill their duties, with the best interests of patients, including those participating in research, as the priority, promoting and ","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 6","pages":"371-373"},"PeriodicalIF":0.0,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671210/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Han Yuan, Chuan Hong, Nguyen Tuan Anh Tran, Xinxing Xu, Nan Liu
Background
Pneumothorax is a medical emergency caused by the abnormal accumulation of air in the pleural space—the potential space between the lungs and chest wall. On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum, and we refer to this area as “lung + space.” While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end-to-end approach. These models directly map chest radiographs to clinician-annotated lesion areas, often neglecting the vital domain knowledge that pneumothorax is inherently location-sensitive.
Methods
We propose a novel approach that incorporates the lung + space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs. To circumvent the need for additional annotations and to prevent potential label leakage on the target task, our method utilizes external datasets and an auxiliary task of lung segmentation. This approach generates a specific constraint of lung + space for each chest radiograph. Furthermore, we have incorporated a discriminator to eliminate unreliable constraints caused by the domain shift between the auxiliary and target datasets.
Results
Our results demonstrated considerable improvements, with average performance gains of 4.6%, 3.6%, and 3.3% regarding intersection over union, dice similarity coefficient, and Hausdorff distance. These results were consistent across six baseline models built on three architectures (U-Net, LinkNet, or PSPNet) and two backbones (VGG-11 or MobileOne-S0). We further conducted an ablation study to evaluate the contribution of each component in the proposed method and undertook several robustness studies on hyper-parameter selection to validate the stability of our method.
Conclusions
The integration of domain knowledge in DL models for medical applications has often been underemphasized. Our research underscores the significance of incorporating medical domain knowledge about the location-specific nature of pneumothorax to enhance DL-based lesion segmentation and further bolster clinicians' trust in DL tools. Beyond pneumothorax, our approach is promising for other thoracic conditions that possess location-relevant characteristics.
{"title":"Leveraging anatomical constraints with uncertainty for pneumothorax segmentation","authors":"Han Yuan, Chuan Hong, Nguyen Tuan Anh Tran, Xinxing Xu, Nan Liu","doi":"10.1002/hcs2.119","DOIUrl":"10.1002/hcs2.119","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Pneumothorax is a medical emergency caused by the abnormal accumulation of air in the pleural space—the potential space between the lungs and chest wall. On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum, and we refer to this area as “lung + space.” While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end-to-end approach. These models directly map chest radiographs to clinician-annotated lesion areas, often neglecting the vital domain knowledge that pneumothorax is inherently location-sensitive.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We propose a novel approach that incorporates the lung + space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs. To circumvent the need for additional annotations and to prevent potential label leakage on the target task, our method utilizes external datasets and an auxiliary task of lung segmentation. This approach generates a specific constraint of lung + space for each chest radiograph. Furthermore, we have incorporated a discriminator to eliminate unreliable constraints caused by the domain shift between the auxiliary and target datasets.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our results demonstrated considerable improvements, with average performance gains of 4.6%, 3.6%, and 3.3% regarding intersection over union, dice similarity coefficient, and Hausdorff distance. These results were consistent across six baseline models built on three architectures (U-Net, LinkNet, or PSPNet) and two backbones (VGG-11 or MobileOne-S0). We further conducted an ablation study to evaluate the contribution of each component in the proposed method and undertook several robustness studies on hyper-parameter selection to validate the stability of our method.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The integration of domain knowledge in DL models for medical applications has often been underemphasized. Our research underscores the significance of incorporating medical domain knowledge about the location-specific nature of pneumothorax to enhance DL-based lesion segmentation and further bolster clinicians' trust in DL tools. Beyond pneumothorax, our approach is promising for other thoracic conditions that possess location-relevant characteristics.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 6","pages":"456-474"},"PeriodicalIF":0.0,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Somit Jain, Shobhit Agrawal, Eshaan Mohapatra, Kathiravan Srinivasan
Background
The global impact of the highly contagious COVID-19 virus has created unprecedented challenges, significantly impacting public health and economies worldwide. This research article conducts a time series analysis of COVID-19 data across various countries, including India, Brazil, Russia, and the United States, with a particular emphasis on total confirmed cases.
Methods
The proposed approach combines auto-regressive integrated moving average (ARIMA)'s ability to capture linear trends and seasonality with long short-term memory (LSTM) networks, which are designed to learn complex nonlinear dependencies in the data. This hybrid approach surpasses both individual models and existing ARIMA-artificial neural network (ANN) hybrids, which often struggle with highly nonlinear time series like COVID-19 data. By integrating ARIMA and LSTM, the model aims to achieve superior forecasting accuracy compared to baseline models, including ARIMA, Gated Recurrent Unit (GRU), LSTM, and Prophet.
Results
The hybrid ARIMA-LSTM model outperformed the benchmark models, achieving a mean absolute percentage error (MAPE) score of 2.4%. Among the benchmark models, GRU performed the best with a MAPE score of 2.9%, followed by LSTM with a score of 3.6%.
Conclusions
The proposed ARIMA-LSTM hybrid model outperforms ARIMA, GRU, LSTM, Prophet, and the ARIMA-ANN hybrid model when evaluating using metrics like MAPE, symmetric mean absolute percentage error, and median absolute percentage error across all countries analyzed. These findings have the potential to significantly improve preparedness and response efforts by public health authorities, allowing for more efficient resource allocation and targeted interventions.
{"title":"A novel ensemble ARIMA-LSTM approach for evaluating COVID-19 cases and future outbreak preparedness","authors":"Somit Jain, Shobhit Agrawal, Eshaan Mohapatra, Kathiravan Srinivasan","doi":"10.1002/hcs2.123","DOIUrl":"10.1002/hcs2.123","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The global impact of the highly contagious COVID-19 virus has created unprecedented challenges, significantly impacting public health and economies worldwide. This research article conducts a time series analysis of COVID-19 data across various countries, including India, Brazil, Russia, and the United States, with a particular emphasis on total confirmed cases.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The proposed approach combines auto-regressive integrated moving average (ARIMA)'s ability to capture linear trends and seasonality with long short-term memory (LSTM) networks, which are designed to learn complex nonlinear dependencies in the data. This hybrid approach surpasses both individual models and existing ARIMA-artificial neural network (ANN) hybrids, which often struggle with highly nonlinear time series like COVID-19 data. By integrating ARIMA and LSTM, the model aims to achieve superior forecasting accuracy compared to baseline models, including ARIMA, Gated Recurrent Unit (GRU), LSTM, and Prophet.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The hybrid ARIMA-LSTM model outperformed the benchmark models, achieving a mean absolute percentage error (MAPE) score of 2.4%. Among the benchmark models, GRU performed the best with a MAPE score of 2.9%, followed by LSTM with a score of 3.6%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The proposed ARIMA-LSTM hybrid model outperforms ARIMA, GRU, LSTM, Prophet, and the ARIMA-ANN hybrid model when evaluating using metrics like MAPE, symmetric mean absolute percentage error, and median absolute percentage error across all countries analyzed. These findings have the potential to significantly improve preparedness and response efforts by public health authorities, allowing for more efficient resource allocation and targeted interventions.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 6","pages":"409-425"},"PeriodicalIF":0.0,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671211/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chenlin Du, Zeyu Zhang, Baoqin Liu, Zijian Cao, Nan Jiang, Zongjiu Zhang
Background
Frailty in older adults is linked to increased risks and lower quality of life. Pre-frailty, a condition preceding frailty, is intervenable, but its determinants and assessment are challenging. This study aims to develop and validate an explainable machine learning model for pre-frailty risk assessment among community-dwelling older adults.
Methods
The study included 3141 adults aged 60 or above from the China Health and Retirement Longitudinal Study. Pre-frailty was characterized by one or two criteria from the physical frailty phenotype scale. We extracted 80 distinct features across seven dimensions to evaluate pre-frailty risk. A model was constructed using recursive feature elimination and a stacking-CatBoost distillation module on 80% of the sample and validated on a separate 20% holdout data set.
Results
The study used data from 2508 community-dwelling older adults (mean age, 67.24 years [range, 60–96]; 1215 [48.44%] females) to develop a pre-frailty risk assessment model. We selected 57 predictive features and built a distilled CatBoost model, which achieved the highest discrimination (AUROC: 0.7560 [95% CI: 0.7169, 0.7928]) on the 20% holdout data set. The living city, BMI, and peak expiratory flow (PEF) were the three most significant contributors to pre-frailty risk. Physical and environmental factors were the top 2 impactful feature dimensions.
Conclusions
An accurate and interpretable pre-frailty risk assessment framework using state-of-the-art machine learning techniques and explanation methods has been developed. Our framework incorporates a wide range of features and determinants, allowing for a comprehensive and nuanced understanding of pre-frailty risk.
{"title":"Explainable machine learning model for pre-frailty risk assessment in community-dwelling older adults","authors":"Chenlin Du, Zeyu Zhang, Baoqin Liu, Zijian Cao, Nan Jiang, Zongjiu Zhang","doi":"10.1002/hcs2.120","DOIUrl":"10.1002/hcs2.120","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Frailty in older adults is linked to increased risks and lower quality of life. Pre-frailty, a condition preceding frailty, is intervenable, but its determinants and assessment are challenging. This study aims to develop and validate an explainable machine learning model for pre-frailty risk assessment among community-dwelling older adults.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The study included 3141 adults aged 60 or above from the China Health and Retirement Longitudinal Study. Pre-frailty was characterized by one or two criteria from the physical frailty phenotype scale. We extracted 80 distinct features across seven dimensions to evaluate pre-frailty risk. A model was constructed using recursive feature elimination and a stacking-CatBoost distillation module on 80% of the sample and validated on a separate 20% holdout data set.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The study used data from 2508 community-dwelling older adults (mean age, 67.24 years [range, 60–96]; 1215 [48.44%] females) to develop a pre-frailty risk assessment model. We selected 57 predictive features and built a distilled CatBoost model, which achieved the highest discrimination (AUROC: 0.7560 [95% CI: 0.7169, 0.7928]) on the 20% holdout data set. The living city, BMI, and peak expiratory flow (PEF) were the three most significant contributors to pre-frailty risk. Physical and environmental factors were the top 2 impactful feature dimensions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>An accurate and interpretable pre-frailty risk assessment framework using state-of-the-art machine learning techniques and explanation methods has been developed. Our framework incorporates a wide range of features and determinants, allowing for a comprehensive and nuanced understanding of pre-frailty risk.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 6","pages":"426-437"},"PeriodicalIF":0.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Skin cancer poses a significant global health threat, with early detection being essential for successful treatment. While deep learning algorithms have greatly enhanced the categorization of skin lesions, the black-box nature of many models limits interpretability, posing challenges for dermatologists.
Methods
To address these limitations, SkinSage XAI utilizes advanced explainable artificial intelligence (XAI) techniques for skin lesion categorization. A data set of around 50,000 images from the Customized HAM10000, selected for diversity, serves as the foundation. The Inception v3 model is used for classification, supported by gradient-weighted class activation mapping and local interpretable model-agnostic explanations algorithms, which provide clear visual explanations for model outputs.
Results
SkinSage XAI demonstrated high performance, accurately categorizing seven types of skin lesions—dermatofibroma, benign keratosis, melanocytic nevus, vascular lesion, actinic keratosis, basal cell carcinoma, and melanoma. It achieved an accuracy of 96%, with precision at 96.42%, recall at 96.28%, F1 score at 96.14%, and an area under the curve of 99.83%.
Conclusions
SkinSage XAI represents a significant advancement in dermatology and artificial intelligence by bridging gaps in accuracy and explainability. The system provides transparent, accurate diagnoses, improving decision-making for dermatologists and potentially enhancing patient outcomes.
{"title":"SkinSage XAI: An explainable deep learning solution for skin lesion diagnosis","authors":"Geetika Munjal, Paarth Bhardwaj, Vaibhav Bhargava, Shivendra Singh, Nimish Nagpal","doi":"10.1002/hcs2.121","DOIUrl":"10.1002/hcs2.121","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Skin cancer poses a significant global health threat, with early detection being essential for successful treatment. While deep learning algorithms have greatly enhanced the categorization of skin lesions, the black-box nature of many models limits interpretability, posing challenges for dermatologists.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To address these limitations, SkinSage XAI utilizes advanced explainable artificial intelligence (XAI) techniques for skin lesion categorization. A data set of around 50,000 images from the Customized HAM10000, selected for diversity, serves as the foundation. The Inception v3 model is used for classification, supported by gradient-weighted class activation mapping and local interpretable model-agnostic explanations algorithms, which provide clear visual explanations for model outputs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>SkinSage XAI demonstrated high performance, accurately categorizing seven types of skin lesions—dermatofibroma, benign keratosis, melanocytic nevus, vascular lesion, actinic keratosis, basal cell carcinoma, and melanoma. It achieved an accuracy of 96%, with precision at 96.42%, recall at 96.28%, <span><i>F</i><sub>1</sub></span> score at 96.14%, and an area under the curve of 99.83%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>SkinSage XAI represents a significant advancement in dermatology and artificial intelligence by bridging gaps in accuracy and explainability. The system provides transparent, accurate diagnoses, improving decision-making for dermatologists and potentially enhancing patient outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 6","pages":"438-455"},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11671215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142904685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the geographical distribution of private hospitals in Australian capital cities in relation to the Index of Relative Socioeconomic Disadvantage.
Methods
Using Geographic Information System analysis, the study examined how private hospitals are distributed across different socioeconomic quartiles, providing a comprehensive visualisation of health care accessibility.
Results
The results indicate an unequal distribution with a substantial concentration of private hospitals within the vicinity of communities classified in the highest socioeconomic classification. This raises significant concerns about health care equity, particularly in light of the increased strain on health care systems before, during and after the COVID-19 pandemic.
Conclusions
This study underscores the need for targeted policy interventions to enhance the resilience and accessibility of the private health care sector, specifically targeting disadvantaged communities. It suggests that comprehensive, geographically-informed data is crucial for policymakers to make informed decisions that promote health equity in the postpandemic landscape.
{"title":"Are private hospital emergency departments in Australia distributed to serve the wealthy community?","authors":"Mazen Baazeem, Estie Kruger, Marc Tennant","doi":"10.1002/hcs2.112","DOIUrl":"10.1002/hcs2.112","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>This study investigates the geographical distribution of private hospitals in Australian capital cities in relation to the Index of Relative Socioeconomic Disadvantage.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Using Geographic Information System analysis, the study examined how private hospitals are distributed across different socioeconomic quartiles, providing a comprehensive visualisation of health care accessibility.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The results indicate an unequal distribution with a substantial concentration of private hospitals within the vicinity of communities classified in the highest socioeconomic classification. This raises significant concerns about health care equity, particularly in light of the increased strain on health care systems before, during and after the COVID-19 pandemic.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study underscores the need for targeted policy interventions to enhance the resilience and accessibility of the private health care sector, specifically targeting disadvantaged communities. It suggests that comprehensive, geographically-informed data is crucial for policymakers to make informed decisions that promote health equity in the postpandemic landscape.</p>\u0000 </section>\u0000 </div>","PeriodicalId":100601,"journal":{"name":"Health Care Science","volume":"3 5","pages":"287-297"},"PeriodicalIF":0.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11520243/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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