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Żydowicz WM, Skokowski J, Marano L, Polom K. Navigating the Metaverse: A New Virtual Tool with Promising Real Benefits for Breast Cancer Patients. J Clin Med 2024; 13:4337. [PMID: 39124604 PMCID: PMC11313674 DOI: 10.3390/jcm13154337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/22/2024] [Accepted: 07/22/2024] [Indexed: 08/12/2024] Open
Abstract
BC, affecting both women and men, is a complex disease where early diagnosis plays a crucial role in successful treatment and enhances patient survival rates. The Metaverse, a virtual world, may offer new, personalized approaches to diagnosing and treating BC. Although Artificial Intelligence (AI) is still in its early stages, its rapid advancement indicates potential applications within the healthcare sector, including consolidating patient information in one accessible location. This could provide physicians with more comprehensive insights into disease details. Leveraging the Metaverse could facilitate clinical data analysis and improve the precision of diagnosis, potentially allowing for more tailored treatments for BC patients. However, while this article highlights the possible transformative impacts of virtual technologies on BC treatment, it is important to approach these developments with cautious optimism, recognizing the need for further research and validation to ensure enhanced patient care with greater accuracy and efficiency.
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Affiliation(s)
- Weronika Magdalena Żydowicz
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
| | - Jaroslaw Skokowski
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
| | - Luigi Marano
- Department of General Surgery and Surgical Oncology, “Saint Wojciech” Hospital, “Nicolaus Copernicus” Health Center, Jana Pawła II 50, 80-462 Gdańsk, Poland; (W.M.Ż.); (J.S.)
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
| | - Karol Polom
- Department of Medicine, Academy of Applied Medical and Social Sciences, Akademia Medycznych I Spolecznych Nauk Stosowanych (AMiSNS), 2 Lotnicza Street, 82-300 Elbląg, Poland;
- Department of Gastrointestinal Surgical Oncology, Greater Poland Cancer Centre, Garbary 15, 61-866 Poznan, Poland
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2
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Johnson Z, Saikia MJ. Digital Twins for Healthcare Using Wearables. Bioengineering (Basel) 2024; 11:606. [PMID: 38927842 PMCID: PMC11200512 DOI: 10.3390/bioengineering11060606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 05/31/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024] Open
Abstract
Digital twins are a relatively new form of digital modeling that has been gaining popularity in recent years. This is in large part due to their ability to update in real time to their physical counterparts and connect across multiple devices. As a result, much interest has been directed towards using digital twins in the healthcare industry. Recent advancements in smart wearable technologies have allowed for the utilization of human digital twins in healthcare. Human digital twins can be generated using biometric data from the patient gathered from wearables. These data can then be used to enhance patient care through a variety of means, such as simulated clinical trials, disease prediction, and monitoring treatment progression remotely. This revolutionary method of patient care is still in its infancy, and as such, there is limited research on using wearables to generate human digital twins for healthcare applications. This paper reviews the literature pertaining to human digital twins, including methods, applications, and challenges. The paper also presents a conceptual method for creating human body digital twins using wearable sensors.
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Affiliation(s)
| | - Manob Jyoti Saikia
- Department of Electrical Engineering, University of North Florida, Jacksonville, FL 32224, USA
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3
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Mosquera-Lopez C, Jacobs PG. Digital twins and artificial intelligence in metabolic disease research. Trends Endocrinol Metab 2024; 35:549-557. [PMID: 38744606 DOI: 10.1016/j.tem.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 04/25/2024] [Accepted: 04/25/2024] [Indexed: 05/16/2024]
Abstract
Digital twin technology is emerging as a transformative paradigm for personalized medicine in the management of chronic conditions. In this article, we explore the concept and key characteristics of a digital twin and its applications in chronic non-communicable metabolic disease management, with a focus on diabetes case studies. We cover various types of digital twin models, including mechanistic models based on ODEs, data-driven ML algorithms, and hybrid modeling strategies that combine the strengths of both approaches. We present successful case studies demonstrating the potential of digital twins in improving glucose outcomes for individuals with T1D and T2D, and discuss the benefits and challenges of translating digital twin research applications to clinical practice.
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Affiliation(s)
- Clara Mosquera-Lopez
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA
| | - Peter G Jacobs
- Artificial Intelligence for Medical Systems Lab, Department of Biomedical Engineering, Oregon Health and Science University, Portland, OR, USA.
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4
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Vallée A. Envisioning the Future of Personalized Medicine: Role and Realities of Digital Twins. J Med Internet Res 2024; 26:e50204. [PMID: 38739913 PMCID: PMC11130780 DOI: 10.2196/50204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 10/01/2023] [Accepted: 12/29/2023] [Indexed: 05/16/2024] Open
Abstract
Digital twins have emerged as a groundbreaking concept in personalized medicine, offering immense potential to transform health care delivery and improve patient outcomes. It is important to highlight the impact of digital twins on personalized medicine across the understanding of patient health, risk assessment, clinical trials and drug development, and patient monitoring. By mirroring individual health profiles, digital twins offer unparalleled insights into patient-specific conditions, enabling more accurate risk assessments and tailored interventions. However, their application extends beyond clinical benefits, prompting significant ethical debates over data privacy, consent, and potential biases in health care. The rapid evolution of this technology necessitates a careful balancing act between innovation and ethical responsibility. As the field of personalized medicine continues to evolve, digital twins hold tremendous promise in transforming health care delivery and revolutionizing patient care. While challenges exist, the continued development and integration of digital twins hold the potential to revolutionize personalized medicine, ushering in an era of tailored treatments and improved patient well-being. Digital twins can assist in recognizing trends and indicators that might signal the presence of diseases or forecast the likelihood of developing specific medical conditions, along with the progression of such diseases. Nevertheless, the use of human digital twins gives rise to ethical dilemmas related to informed consent, data ownership, and the potential for discrimination based on health profiles. There is a critical need for robust guidelines and regulations to navigate these challenges, ensuring that the pursuit of advanced health care solutions does not compromise patient rights and well-being. This viewpoint aims to ignite a comprehensive dialogue on the responsible integration of digital twins in medicine, advocating for a future where technology serves as a cornerstone for personalized, ethical, and effective patient care.
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Affiliation(s)
- Alexandre Vallée
- Department of Epidemiology and Public Health, Foch Hospital, Suresnes, France
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5
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Katsoulakis E, Wang Q, Wu H, Shahriyari L, Fletcher R, Liu J, Achenie L, Liu H, Jackson P, Xiao Y, Syeda-Mahmood T, Tuli R, Deng J. Digital twins for health: a scoping review. NPJ Digit Med 2024; 7:77. [PMID: 38519626 PMCID: PMC10960047 DOI: 10.1038/s41746-024-01073-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 03/07/2024] [Indexed: 03/25/2024] Open
Abstract
The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.
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Affiliation(s)
- Evangelia Katsoulakis
- VA Informatics and Computing Infrastructure, Salt Lake City, UT, 84148, USA
- Department of Radiation Oncology, University of South Florida, Tampa, FL, 33606, USA
| | - Qi Wang
- Department of Mathematics, University of South Carolina, Columbia, SC, 29208, USA
| | - Huanmei Wu
- Department of Health Services Administration and Policy, Temple University, Philadelphia, PA, 19122, USA
| | - Leili Shahriyari
- Department of Mathematics and Statistics, University of Massachusetts Amherst, Amherst, MA, 01003, USA
| | - Richard Fletcher
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02139, USA
| | - Jinwei Liu
- Department of Computer and Information Sciences, Florida A&M University, Tallahassee, FL, 32307, USA
| | - Luke Achenie
- Department of Chemical Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24060, USA
| | - Hongfang Liu
- McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Pamela Jackson
- Precision Neurotherapeutics Innovation Program & Department of Neurosurgery, Mayo Clinic, Phoenix, AZ, 85003, USA
| | - Ying Xiao
- Department of Radiation Oncology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | | | - Richard Tuli
- Department of Radiation Oncology, University of South Florida, Tampa, FL, 33606, USA
| | - Jun Deng
- Department of Therapeutic Radiology, Yale University, New Haven, CT, 06510, USA.
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Jabin MSR, Yaroson EV, Ilodibe A, Eldabi T. Ethical and Quality of Care-Related Challenges of Digital Health Twins in Older Care Settings: Protocol for a Scoping Review. JMIR Res Protoc 2024; 13:e51153. [PMID: 38393771 PMCID: PMC10924255 DOI: 10.2196/51153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 11/19/2023] [Accepted: 12/13/2023] [Indexed: 02/23/2024] Open
Abstract
BACKGROUND Digital health twins (DHTs) have been evolving with their diverse applications in medicine, specifically in older care settings, with the increasing demands of older adults. DHTs have already contributed to improving the quality of dementia and trauma care, cardiac treatment, and health care services for older individuals. Despite its many benefits, the optimum implementation of DHTs has faced several challenges associated with ethical issues, quality of care, management and leadership, and design considerations in older care settings. Since the need for such care is continuously rising and there is evident potential for DHTs to meet those needs, this review aims to map key concepts to address the gaps in the research knowledge to improve DHT implementation. OBJECTIVE The review aims to compile and synthesize the best available evidence regarding the problems encountered by older adults and care providers associated with the application of DHTs. The synthesis will collate the evidence of the issues associated with quality of care, the ethical implications of DHTs, and the strategies undertaken to overcome those challenges in older care settings. METHODS The review will follow the Joanna Briggs Institute (JBI) methodology. The published studies will be searched through CINAHL, MEDLINE, JBI, and Web of Science, and the unpublished studies through Mednar, Trove, OCLC WorldCat, and Dissertations and Theses. Studies published in English from 2002 will be considered. This review will include studies of older individuals (aged 65 years or older) undergoing care delivery associated with DHTs and their respective care providers. The concept will include the application of the technology, and the context will involve studies based on the older care setting. A broad scope of evidence, including quantitative, qualitative, text and opinion studies, will be considered. A total of 2 independent reviewers will screen the titles and abstracts and then review the full text. Data will be extracted from the included studies using a data extraction tool developed for this study. RESULTS The results will be presented in a PRISMA-ScR (Preferred Reporting Items for Systematic Review and Meta-Analysis extension for Scoping Reviews) flow diagram. A draft charting table will be developed as a data extraction tool. The results will be presented as a "map" of the data in a logical, diagrammatic, or tabular form in a descriptive format. CONCLUSIONS The evidence synthesis is expected to uncover the shreds of evidence required to address the ethical and care quality-related challenges associated with applying DHTs. A synthesis of various strategies used to overcome identified challenges will provide more prospects for adopting them elsewhere and create a resource allocation model for older individuals. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/51153.
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Affiliation(s)
- Md Shafiqur Rahman Jabin
- Department of Medicine and Optometry, Linnaeus University, Kalmar, Sweden
- Faculty of Health Studies, University of Bradford, Bradford, United Kingdom
| | - Emillia Vann Yaroson
- Department of Operations and Analytics, University of Huddersfield, Huddersfield, United Kingdom
| | - Adaobi Ilodibe
- Department of Applied Artificial Intelligence and Data Analytics, University of Bradford, Bradford, United Kingdom
| | - Tillal Eldabi
- Faculty of Management, Law & Social Sciences, University of Bradford, Bradford, United Kingdom
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7
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Rovati L, Gary PJ, Cubro E, Dong Y, Kilickaya O, Schulte PJ, Zhong X, Wörster M, Kelm DJ, Gajic O, Niven AS, Lal A. Development and usability testing of a patient digital twin for critical care education: a mixed methods study. Front Med (Lausanne) 2024; 10:1336897. [PMID: 38274456 PMCID: PMC10808677 DOI: 10.3389/fmed.2023.1336897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024] Open
Abstract
Background Digital twins are computerized patient replicas that allow clinical interventions testing in silico to minimize preventable patient harm. Our group has developed a novel application software utilizing a digital twin patient model based on electronic health record (EHR) variables to simulate clinical trajectories during the initial 6 h of critical illness. This study aimed to assess the usability, workload, and acceptance of the digital twin application as an educational tool in critical care. Methods A mixed methods study was conducted during seven user testing sessions of the digital twin application with thirty-five first-year internal medicine residents. Qualitative data were collected using a think-aloud and semi-structured interview format, while quantitative measurements included the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and a short survey. Results Median SUS scores and NASA-TLX were 70 (IQR 62.5-82.5) and 29.2 (IQR 22.5-34.2), consistent with good software usability and low to moderate workload, respectively. Residents expressed interest in using the digital twin application for ICU rotations and identified five themes for software improvement: clinical fidelity, interface organization, learning experience, serious gaming, and implementation strategies. Conclusion A digital twin application based on EHR clinical variables showed good usability and high acceptance for critical care education.
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Affiliation(s)
- Lucrezia Rovati
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
- School of Medicine and Surgery, University of Milano-Bicocca, Milan, Italy
| | - Phillip J. Gary
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Edin Cubro
- Department of Information Technology, Mayo Clinic, Rochester, MN, United States
| | - Yue Dong
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN, United States
| | - Oguz Kilickaya
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Phillip J. Schulte
- Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, United States
| | - Xiang Zhong
- Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States
| | - Malin Wörster
- Center for Anesthesiology and Intensive Care Medicine, Department of Anesthesiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Diana J. Kelm
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Ognjen Gajic
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Alexander S. Niven
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
| | - Amos Lal
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, United States
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8
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Heudel PE, Renard F, Attye A. [Digital twins in cancer research and treatment: A future for personalized medicine]. Bull Cancer 2023; 110:1085-1087. [PMID: 37661550 DOI: 10.1016/j.bulcan.2023.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 07/19/2023] [Accepted: 07/19/2023] [Indexed: 09/05/2023]
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Vallée A. Digital twin for healthcare systems. Front Digit Health 2023; 5:1253050. [PMID: 37744683 PMCID: PMC10513171 DOI: 10.3389/fdgth.2023.1253050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 08/28/2023] [Indexed: 09/26/2023] Open
Abstract
Digital twin technology is revolutionizing healthcare systems by leveraging real-time data integration, advanced analytics, and virtual simulations to enhance patient care, enable predictive analytics, optimize clinical operations, and facilitate training and simulation. With the ability to gather and analyze a wealth of patient data from various sources, digital twins can offer personalized treatment plans based on individual characteristics, medical history, and real-time physiological data. Predictive analytics and preventive interventions are made possible by machine learning algorithms, allowing for early detection of health risks and proactive interventions. Digital twins can optimize clinical operations by analyzing workflows and resource allocation, leading to streamlined processes and improved patient care. Moreover, digital twins can provide a safe and realistic environment for healthcare professionals to enhance their skills and practice complex procedures. The implementation of digital twin technology in healthcare has the potential to significantly improve patient outcomes, enhance patient safety, and drive innovation in the healthcare industry.
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Affiliation(s)
- Alexandre Vallée
- Department of Epidemiology and Public Health, Foch Hospital, Suresnes, France
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10
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Gazerani P. Intelligent Digital Twins for Personalized Migraine Care. J Pers Med 2023; 13:1255. [PMID: 37623505 PMCID: PMC10455577 DOI: 10.3390/jpm13081255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 08/04/2023] [Accepted: 08/12/2023] [Indexed: 08/26/2023] Open
Abstract
Intelligent digital twins closely resemble their real-life counterparts. In health and medical care, they enable the real-time monitoring of patients, whereby large amounts of data can be collected to produce actionable information. These powerful tools are constructed with the aid of artificial intelligence, machine learning, and deep learning; the Internet of Things; and cloud computing to collect a diverse range of digital data (e.g., from digital patient journals, wearable sensors, and digitized monitoring equipment or processes), which can provide information on the health conditions and therapeutic responses of their physical twins. Intelligent digital twins can enable data-driven clinical decision making and advance the realization of personalized care. Migraines are a highly prevalent and complex neurological disorder affecting people of all ages, genders, and geographical locations. It is ranked among the top disabling diseases, with substantial negative personal and societal impacts, but the current treatment strategies are suboptimal. Personalized care for migraines has been suggested to optimize their treatment. The implementation of intelligent digital twins for migraine care can theoretically be beneficial in supporting patient-centric care management. It is also expected that the implementation of intelligent digital twins will reduce costs in the long run and enhance treatment effectiveness. This study briefly reviews the concept of digital twins and the available literature on digital twins for health disorders such as neurological diseases. Based on these, the potential construction and utility of digital twins for migraines will then be presented. The potential and challenges when implementing intelligent digital twins for the future management of migraines are also discussed.
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Affiliation(s)
- Parisa Gazerani
- Department of Life Sciences and Health, Faculty of Health Sciences, Oslo Metropolitan University, 0130 Oslo, Norway;
- Centre for Intelligent Musculoskeletal Health (CIM), Faculty of Health Sciences, Oslo Metropolitan University, 0130 Oslo, Norway
- Department of Health Science and Technology, Faculty of Medicine, Aalborg University, 9260 Gistrup, Denmark
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11
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Luvhengo TE, Bombil I, Mokhtari A, Moeng MS, Demetriou D, Sanders C, Dlamini Z. Multi-Omics and Management of Follicular Carcinoma of the Thyroid. Biomedicines 2023; 11:biomedicines11041217. [PMID: 37189835 DOI: 10.3390/biomedicines11041217] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 04/05/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
Follicular thyroid carcinoma (FTC) is the second most common cancer of the thyroid gland, accounting for up to 20% of all primary malignant tumors in iodine-replete areas. The diagnostic work-up, staging, risk stratification, management, and follow-up strategies in patients who have FTC are modeled after those of papillary thyroid carcinoma (PTC), even though FTC is more aggressive. FTC has a greater propensity for haematogenous metastasis than PTC. Furthermore, FTC is a phenotypically and genotypically heterogeneous disease. The diagnosis and identification of markers of an aggressive FTC depend on the expertise and thoroughness of pathologists during histopathological analysis. An untreated or metastatic FTC is likely to de-differentiate and become poorly differentiated or undifferentiated and resistant to standard treatment. While thyroid lobectomy is adequate for the treatment of selected patients who have low-risk FTC, it is not advisable for patients whose tumor is larger than 4 cm in diameter or has extensive extra-thyroidal extension. Lobectomy is also not adequate for tumors that have aggressive mutations. Although the prognosis for over 80% of PTC and FTC is good, nearly 20% of the tumors behave aggressively. The introduction of radiomics, pathomics, genomics, transcriptomics, metabolomics, and liquid biopsy have led to improvements in the understanding of tumorigenesis, progression, treatment response, and prognostication of thyroid cancer. The article reviews the challenges that are encountered during the diagnostic work-up, staging, risk stratification, management, and follow-up of patients who have FTC. How the application of multi-omics can strengthen decision-making during the management of follicular carcinoma is also discussed.
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Affiliation(s)
- Thifhelimbilu Emmanuel Luvhengo
- Department of Surgery, Charlotte Maxeke Johannesburg Academic Hospital, University of the Witwatersrand, Parktown, Johannesburg 2193, South Africa
| | - Ifongo Bombil
- Department of Surgery, Chris Hani Baragwanath Academic Hospital, University of the Witwatersrand, Johannesburg 1864, South Africa
| | - Arian Mokhtari
- Department of Surgery, Dr. George Mukhari Academic Hospital, Sefako Makgatho Health Sciences University, Ga-Rankuwa 0208, South Africa
| | - Maeyane Stephens Moeng
- Department of Surgery, Charlotte Maxeke Johannesburg Academic Hospital, University of the Witwatersrand, Parktown, Johannesburg 2193, South Africa
| | - Demetra Demetriou
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield 0028, South Africa
| | - Claire Sanders
- Department of Surgery, Helen Joseph Hospital, University of the Witwatersrand, Auckland Park, Johannesburg 2006, South Africa
| | - Zodwa Dlamini
- SAMRC Precision Oncology Research Unit (PORU), DSI/NRF SARChI Chair in Precision Oncology and Cancer Prevention (POCP), Pan African Cancer Research Institute (PACRI), University of Pretoria, Hatfield 0028, South Africa
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12
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Moztarzadeh O, Jamshidi MB, Sargolzaei S, Jamshidi A, Baghalipour N, Malekzadeh Moghani M, Hauer L. Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer. Bioengineering (Basel) 2023; 10:bioengineering10040455. [PMID: 37106642 PMCID: PMC10136137 DOI: 10.3390/bioengineering10040455] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 03/26/2023] [Accepted: 04/05/2023] [Indexed: 04/29/2023] Open
Abstract
Medical digital twins, which represent medical assets, play a crucial role in connecting the physical world to the metaverse, enabling patients to access virtual medical services and experience immersive interactions with the real world. One serious disease that can be diagnosed and treated using this technology is cancer. However, the digitalization of such diseases for use in the metaverse is a highly complex process. To address this, this study aims to use machine learning (ML) techniques to create real-time and reliable digital twins of cancer for diagnostic and therapeutic purposes. The study focuses on four classical ML techniques that are simple and fast for medical specialists without extensive Artificial Intelligence (AI) knowledge, and meet the requirements of the Internet of Medical Things (IoMT) in terms of latency and cost. The case study focuses on breast cancer (BC), the second most prevalent form of cancer worldwide. The study also presents a comprehensive conceptual framework to illustrate the process of creating digital twins of cancer, and demonstrates the feasibility and reliability of these digital twins in monitoring, diagnosing, and predicting medical parameters.
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Affiliation(s)
- Omid Moztarzadeh
- Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, 32300 Pilsen, Czech Republic
- Department of Anatomy, Faculty of Medicine in Pilsen, Charles University, 32300 Pilsen, Czech Republic
| | | | - Saleh Sargolzaei
- Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad 9187147578, Iran
| | - Alireza Jamshidi
- Dentistry School, Babol University of Medical Sciences, Babol 4717647745, Iran
| | - Nasimeh Baghalipour
- Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, 32300 Pilsen, Czech Republic
| | - Mona Malekzadeh Moghani
- Department of Radiation Oncology, Medical School, Shahid Beheshti, University of Medical Sciences, Teheran 1985717443, Iran
| | - Lukas Hauer
- Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, 32300 Pilsen, Czech Republic
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13
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Cook EJ, Elliott E, Donald L, Gaitan A, Randhawa G, Cartwright S, Waqar M, Egbutah C, Nduka I, Guppy A, Ali N. Knowledge, perceived risk, and attitudes towards COVID-19 protective measures amongst ethnic minorities in the UK: A cross-sectional study. Front Public Health 2023; 10:1060694. [PMID: 36711414 PMCID: PMC9880421 DOI: 10.3389/fpubh.2022.1060694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 12/19/2022] [Indexed: 01/15/2023] Open
Abstract
Background Minority ethnic groups are at increased risk of COVID-19 related mortality or morbidity yet continue to have a disproportionally lower uptake of the vaccine. The importance of adherence to prevention and control measures to keep vulnerable populations and their families safe therefore remains crucial. This research sought to examine the knowledge, perceived risk, and attitudes toward COVID-19 among an ethnically diverse community. Methods A cross-sectional self-administered questionnaire was implemented to survey ethnic minority participants purposefully recruited from Luton, an ethnically diverse town in the southeast of England. The questionnaire was structured to assess participants knowledge, perceived risk, attitudes toward protective measures as well as the sources of information about COVID-19. The questionnaire was administered online via Qualtrics with the link shared through social media platforms such as Facebook, Twitter, and WhatsApp. Questionnaires were also printed into brochures and disseminated via community researchers and community links to individuals alongside religious, community and outreach organisations. Data were analysed using appropriate statistical techniques, with the significance threshold for all analyses assumed at p = 0.05. Findings 1,058 participants (634; 60% females) with a median age of 38 (IQR, 22) completed the survey. National TV and social networks were the most frequently accessed sources of COVID-19 related information; however, healthcare professionals, whilst not widely accessed, were viewed as the most trusted. Knowledge of transmission routes and perceived susceptibility were significant predictors of attitudes toward health-protective practises. Conclusion/recommendation Improving the local information provision, including using tailored communication strategies that draw on trusted sources, including healthcare professionals, could facilitate understanding of risk and promote adherence to health-protective actions.
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Affiliation(s)
- Erica Jane Cook
- School of Psychology, University of Bedfordshire, Luton, United Kingdom,*Correspondence: Erica Jane Cook ✉
| | | | - Louisa Donald
- School of Psychology, University of Bedfordshire, Luton, United Kingdom
| | - Alfredo Gaitan
- School of Psychology, University of Bedfordshire, Luton, United Kingdom
| | - Gurch Randhawa
- Institute for Health Research, University of Bedfordshire, Luton, United Kingdom
| | - Sally Cartwright
- Public Health Luton, Luton Borough Council, Luton, United Kingdom
| | - Muhammad Waqar
- Institute for Health Research, University of Bedfordshire, Luton, United Kingdom
| | - Chimeme Egbutah
- Public Health Luton, Luton Borough Council, Luton, United Kingdom
| | - Ifunanya Nduka
- Institute for Health Research, University of Bedfordshire, Luton, United Kingdom
| | - Andy Guppy
- School of Psychology, University of Bedfordshire, Luton, United Kingdom
| | - Nasreen Ali
- Institute for Health Research, University of Bedfordshire, Luton, United Kingdom
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Sheng B, Wang Z, Qiao Y, Xie SQ, Tao J, Duan C. Detecting latent topics and trends of digital twins in healthcare: A structural topic model-based systematic review. Digit Health 2023; 9:20552076231203672. [PMID: 37846404 PMCID: PMC10576938 DOI: 10.1177/20552076231203672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 09/08/2023] [Indexed: 10/18/2023] Open
Abstract
Objective Digital twins (DTs) have received widespread attention recently, providing new ideas and possibilities for future healthcare. This review aims to provide a quantitative review to analyze specific study contents, research focus, and trends of DT in healthcare. Simultaneously, this review intends to expand the connotation of "healthcare" into two directions, namely "Disease treatment" and "Health enhancement" to analyze the content within the "DT + healthcare" field thoroughly. Methods A data mining method named Structure Topic Modeling (STM) was used as the analytical tool due to its topic analysis ability and versatility. Google Scholar, Web of Science, and China National Knowledge Infrastructure supplied the material papers in this review. Results A total of 94 high-quality papers published between 2018 and 2022 were gathered and categorized into eight topics, collectively covering the transformative impact across a broader spectrum in healthcare. Three main findings have emerged: (1) papers published in healthcare predominantly concentrate on technology development (artificial intelligence, Internet of Things, etc.) and application scenarios(personalized, precise, and real-time health service); (2) the popularity of research topics is influenced by various factors, including policies, COVID-19, and emerging technologies; and (3) the preference for topics is diverse, with a general inclination toward the attribute of "Health enhancement." Conclusions This review underscores the significance of real-time capability and accuracy in shaping the future of DT, where algorithms and data transmission methods assume central importance in achieving these goals. Moreover, technological advancements, such as omics and Metaverse, have opened up new possibilities for DT in healthcare. These findings contribute to the existing literature by offering quantitative insights and valuable guidance to keep researchers ahead of the curve.
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Affiliation(s)
- Bo Sheng
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
- Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai, China
| | - Zheyu Wang
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
| | - Yujiao Qiao
- ShanghaiTech University Center for Innovative Teaching and Learning, ShanghaiTech University, Shanghai, China
| | - Sheng Quan Xie
- School of Electronic and Electrical Engineering, University of Leeds, Leeds, UK
| | - Jing Tao
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
| | - Chaoqun Duan
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
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15
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Al-Zyoud I, Laamarti F, Ma X, Tobón D, El Saddik A. Towards a Machine Learning-Based Digital Twin for Non-Invasive Human Bio-Signal Fusion. SENSORS (BASEL, SWITZERLAND) 2022; 22:9747. [PMID: 36560115 PMCID: PMC9786606 DOI: 10.3390/s22249747] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 11/10/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
Human bio-signal fusion is considered a critical technological solution that needs to be advanced to enable modern and secure digital health and well-being applications in the metaverse. To support such efforts, we propose a new data-driven digital twin (DT) system to fuse three human physiological bio-signals: heart rate (HR), breathing rate (BR), and blood oxygen saturation level (SpO2). To accomplish this goal, we design a computer vision technology based on the non-invasive photoplethysmography (PPG) technique to extract raw time-series bio-signal data from facial video frames. Then, we implement machine learning (ML) technology to model and measure the bio-signals. We accurately demonstrate the digital twin capability in the modelling and measuring of three human bio-signals, HR, BR, and SpO2, and achieve strong performance compared to the ground-truth values. This research sets the foundation and the path forward for realizing a holistic human health and well-being DT model for real-world medical applications.
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Affiliation(s)
- Izaldein Al-Zyoud
- Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Fedwa Laamarti
- Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- Mohamed bin Zayed University of AI, Abu Dhabi, United Arab Emirates
| | - Xiaocong Ma
- Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Diana Tobón
- Faculty of Engineering, University of Medellín, Medellín 050010, Colombia
| | - Abdulmotaleb El Saddik
- Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
- Mohamed bin Zayed University of AI, Abu Dhabi, United Arab Emirates
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Siebenhofer A, Könczöl C, Jeitler K, Schmid D, Elliott P, Avian A. Predictors for adherent behavior in the COVID-19 pandemic: A cross-sectional telephone survey. Front Public Health 2022; 10:894128. [PMID: 36339221 PMCID: PMC9632415 DOI: 10.3389/fpubh.2022.894128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 10/10/2022] [Indexed: 01/22/2023] Open
Abstract
Background During the COVID-19 pandemic, protective measures have been prescribed to prevent or slow down the spread of the SARS-CoV-2 virus and protect the population. Individuals follow these measures to varying degrees. We aimed to identify factors influencing the extent to which protective measures are adhered to. Methods A cross-sectional survey (telephone interviews) was undertaken between April and June 2021 to identify factors influencing the degree to which individuals adhere to protective measures. A representative sample of 1,003 people (age >16 years) in two Austrian states (Carinthia, Vorarlberg) was interviewed. The questionnaire was based on the Health Belief Model, but also included potential response-modifying factors. Predictors for adherent behavior were identified using multiple regression analysis. All predictors were standardized so that regression coefficients (β) could be compared. Results Overall median adherence was 0.75 (IQR: 0.5-1.0). Based on a regression model, the following variables were identified as significant in raising adherence: higher age (β = 0.43, 95%CI: 0.33-0.54), social standards of acceptable behavior (β = 0.33, 95%CI: 0.27-0.40), subjective/individual assessment of an increased personal health risk (β = 0.12, 95%CI: 0.05-0.18), self-efficacy (β = 0.06, 95%CI: 0.02-0.10), female gender (β = 0.05, 95%CI: 0.01-0.08), and low corona fatigue (behavioral fatigue: β = -0.11, 95%CI: -0.18 to -0.03). The model showed that such aspects as personal trust in institutions, perceived difficulties in adopting health-promoting measures, and individual assessments of the risk of infection, had no significant influence. Conclusions This study reveals that several factors significantly influence adherence to measures aimed at controlling the COVID-19 pandemic. To enhance adherence, the government, media, and other relevant stakeholders should take the findings into consideration when formulating policy. By developing social standards and promoting self-efficacy, individuals can influence the behavior of others and contribute toward coping with the pandemic.
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Affiliation(s)
- Andrea Siebenhofer
- Institute of General Practice and Evidence Based Health Services Research, Medical University Graz, Graz, Austria,Institute of General Practice, Johann Wolfgang Goethe University Frankfurt, Frankfurt, Germany
| | | | - Klaus Jeitler
- Institute of General Practice and Evidence Based Health Services Research, Medical University Graz, Graz, Austria,Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria
| | - Daniela Schmid
- Austrian Agency for Health and Food Safety Ltd. AGES, Vienna, Austria
| | - Phillip Elliott
- Institute of General Practice, Johann Wolfgang Goethe University Frankfurt, Frankfurt, Germany
| | - Alexander Avian
- Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Graz, Austria,*Correspondence: Alexander Avian
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