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Tezsezen E, Yigci D, Ahmadpour A, Tasoglu S. AI-Based Metamaterial Design. ACS APPLIED MATERIALS & INTERFACES 2024; 16:29547-29569. [PMID: 38808674 PMCID: PMC11181287 DOI: 10.1021/acsami.4c04486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 05/30/2024]
Abstract
The use of metamaterials in various devices has revolutionized applications in optics, healthcare, acoustics, and power systems. Advancements in these fields demand novel or superior metamaterials that can demonstrate targeted control of electromagnetic, mechanical, and thermal properties of matter. Traditional design systems and methods often require manual manipulations which is time-consuming and resource intensive. The integration of artificial intelligence (AI) in optimizing metamaterial design can be employed to explore variant disciplines and address bottlenecks in design. AI-based metamaterial design can also enable the development of novel metamaterials by optimizing design parameters that cannot be achieved using traditional methods. The application of AI can be leveraged to accelerate the analysis of vast data sets as well as to better utilize limited data sets via generative models. This review covers the transformative impact of AI and AI-based metamaterial design for optics, acoustics, healthcare, and power systems. The current challenges, emerging fields, future directions, and bottlenecks within each domain are discussed.
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Affiliation(s)
- Ece Tezsezen
- Graduate
School of Science and Engineering, Koç
University, Istanbul 34450, Türkiye
| | - Defne Yigci
- School
of Medicine, Koç University, Istanbul 34450, Türkiye
| | - Abdollah Ahmadpour
- Department
of Mechanical Engineering, Koç University
Sariyer, Istanbul 34450, Türkiye
| | - Savas Tasoglu
- Department
of Mechanical Engineering, Koç University
Sariyer, Istanbul 34450, Türkiye
- Koç
University Translational Medicine Research Center (KUTTAM), Koç University, Istanbul 34450, Türkiye
- Bogaziçi
Institute of Biomedical Engineering, Bogaziçi
University, Istanbul 34684, Türkiye
- Koç
University Arçelik Research Center for Creative Industries
(KUAR), Koç University, Istanbul 34450, Türkiye
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2
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Rovere G, Bosco F, Miceli A, Ratano S, Freddo G, D'Itri L, Ferruzza M, Maccauro G, Farsetti P, Camarda L. Adoption of blockchain as a step forward in orthopedic practice. Eur J Transl Myol 2024. [PMID: 38785351 DOI: 10.4081/ejtm.2024.12197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 02/25/2024] [Indexed: 05/25/2024] Open
Abstract
Blockchain technology has gained popularity since the invention of Bitcoin in 2008. It offers a decentralized and secure system for managing and protecting data. In the healthcare sector, where data protection and patient privacy are crucial, blockchain has the potential to revolutionize various aspects, including patient data management, orthopedic registries, medical imaging, research data, and the integration of Internet of Things (IoT) devices. This manuscript explores the applications of blockchain in orthopedics and highlights its benefits. Furthermore, the combination of blockchain with artificial intelligence (AI), machine learning, and deep learning can enable more accurate diagnoses and treatment recommendations. AI algorithms can learn from large datasets stored on the blockchain, leading to advancements in automated clinical decision-making. Overall, blockchain technology has the potential to enhance data security, interoperability, and collaboration in orthopedics. While there are challenges to overcome, such as adoption barriers and data sharing willingness, the benefits offered by blockchain make it a promising innovation for the field.
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Affiliation(s)
- Giuseppe Rovere
- Department of Orthopaedics and Traumatology, Fondazione Policlinico Universitario A. Gemelli IRCCS - Università Cattolica del Sacro Cuore, Rome, Italy; Department of Clinical Science and Translational Medicine, Section of Orthopaedics and Traumatology, University of Rome "Tor Vergata", Rome.
| | - Francesco Bosco
- Department of Precision Medicine in the Medical, Surgical and Critical Care Area (ME.PRE.C.C.), University of Palermo, Palermo.
| | - Angelo Miceli
- Department of Precision Medicine in the Medical, Surgical and Critical Care Area (ME.PRE.C.C.), University of Palermo, Palermo.
| | - Salvatore Ratano
- Department of Precision Medicine in the Medical, Surgical and Critical Care Area (ME.PRE.C.C.), University of Palermo, Palermo.
| | - Giuseppe Freddo
- Department of Precision Medicine in the Medical, Surgical and Critical Care Area (ME.PRE.C.C.), University of Palermo, Palermo.
| | - Lorenzo D'Itri
- Department of Precision Medicine in the Medical, Surgical and Critical Care Area (ME.PRE.C.C.), University of Palermo, Palermo.
| | - Massimo Ferruzza
- Department of Precision Medicine in the Medical, Surgical and Critical Care Area (ME.PRE.C.C.), University of Palermo, Palermo.
| | - Giulio Maccauro
- Department of Orthopaedics and Traumatology, Fondazione Policlinico Universitario A. Gemelli IRCCS - Università Cattolica del Sacro Cuore, Rome.
| | - Pasquale Farsetti
- Department of Clinical Science and Translational Medicine, Section of Orthopaedics and Traumatology, University of Rome "Tor Vergata", Rome.
| | - Lawrence Camarda
- Department of Precision Medicine in the Medical, Surgical and Critical Care Area (ME.PRE.C.C.), University of Palermo, Palermo.
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Lee KS, Jung SH, Kim DH, Chung SW, Yoon JP. Reply Letter to the Editor regarding "Artificial intelligence- and computer-assisted navigation for shoulder surgery". J Orthop Surg (Hong Kong) 2024; 32:10225536241263658. [PMID: 38902024 DOI: 10.1177/10225536241263658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/22/2024] Open
Affiliation(s)
- Kang-San Lee
- Department of Orthopaedic Surgery, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Seung Ho Jung
- Department of Orthopaedic Surgery, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Dong-Hyun Kim
- Department of Orthopaedic Surgery, School of Medicine, Kyungpook National University, Daegu, Korea
| | - Seok Won Chung
- Department of Orthopaedic Surgery, School of Medicine, Konkuk University Medical Center, Seoul, Korea
| | - Jong Pil Yoon
- Department of Orthopaedic Surgery, School of Medicine, Kyungpook National University, Daegu, Korea
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Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering (Basel) 2024; 11:337. [PMID: 38671759 PMCID: PMC11047988 DOI: 10.3390/bioengineering11040337] [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: 02/28/2024] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.
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Affiliation(s)
| | - Mohamad Forouzanfar
- Département de Génie des Systèmes, École de Technologie Supérieure (ÉTS), Université du Québec, Montréal, QC H3C 1K3, Canada
- Centre de Recherche de L’institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC H3W 1W5, Canada
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5
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Gruson D, Cobbaert C, Dabla PK, Stankovic S, Homsak E, Kotani K, Khali R, Nichols JH, Gouget B. Validation and verification framework and data integration of biosensors and in vitro diagnostic devices: a position statement of the IFCC Committee on Mobile Health and Bioengineering in Laboratory Medicine (C-MBHLM) and the IFCC Scientific Division. Clin Chem Lab Med 2024; 0:cclm-2023-1455. [PMID: 38379410 DOI: 10.1515/cclm-2023-1455] [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: 12/17/2023] [Accepted: 01/29/2024] [Indexed: 02/22/2024]
Abstract
Advances in technology have transformed healthcare and laboratory medicine. Biosensors have emerged as a promising technology in healthcare, providing a way to monitor human physiological parameters in a continuous, real-time, and non-intrusive manner and offering value and benefits in a wide range of applications. This position statement aims to present the current situation around biosensors, their perspectives and importantly the need to set the framework for their validation and safe use. The development of a qualification framework for biosensors should be conceptually adopted and extended to cover digitally measured biomarkers from biosensors for advancing healthcare and achieving more individualized patient management and better patient outcome.
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Affiliation(s)
- Damien Gruson
- Department of Clinical Biochemistry, Cliniques Universitaires St-Luc and Université Catholique de Louvain, Brussels, Belgium
- Pôle de recherche en Endocrinologie, Diabète et Nutrition, Institut de Recherche Expérimentale et Clinique, Cliniques Universitaires St-Luc and Université Catholique de Louvain, Brussels, Belgium
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
| | - Christa Cobbaert
- Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Centre (LUMC), Leiden, Netherlands
- International Federation of Clinical Chemistry (IFCC) Scientific Division, Milan, Italy
| | - Pradeep Kumar Dabla
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
- Department of Biochemistry, G.B. Pant Institute of Postgraduate Medical Education & Research, Associated Maulana Azad Medical College, New Delhi, India
| | - Sanja Stankovic
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
- Center for Medical Biochemistry, University Clinical Center of Serbia, Belgrade, Serbia
- Faculty of Medical Sciences, University of Kragujevac, Kragujevac, Serbia
| | - Evgenija Homsak
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
- Department for Laboratory Diagnostics, University Clinical Center Maribor, Maribor, Slovenia
| | - Kazuhiko Kotani
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
- Division of Community and Family Medicine, Jichi Medical University, Shimotsuke-City, Japan
| | - Ramy Khali
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
- Egyptian Association of Healthcare Quality and Patient Safety, Alexandria, Egypt
| | - James H Nichols
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
- Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bernard Gouget
- Emerging Technologies Division and MHBLM Committee, International Federation Clinical Chemistry and Laboratory Medicine (IFCC), Milan, Italy
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Rahman A, Debnath T, Kundu D, Khan MSI, Aishi AA, Sazzad S, Sayduzzaman M, Band SS. Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities. AIMS Public Health 2024; 11:58-109. [PMID: 38617415 PMCID: PMC11007421 DOI: 10.3934/publichealth.2024004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 12/18/2023] [Indexed: 04/16/2024] Open
Abstract
In recent years, machine learning (ML) and deep learning (DL) have been the leading approaches to solving various challenges, such as disease predictions, drug discovery, medical image analysis, etc., in intelligent healthcare applications. Further, given the current progress in the fields of ML and DL, there exists the promising potential for both to provide support in the realm of healthcare. This study offered an exhaustive survey on ML and DL for the healthcare system, concentrating on vital state of the art features, integration benefits, applications, prospects and future guidelines. To conduct the research, we found the most prominent journal and conference databases using distinct keywords to discover scholarly consequences. First, we furnished the most current along with cutting-edge progress in ML-DL-based analysis in smart healthcare in a compendious manner. Next, we integrated the advancement of various services for ML and DL, including ML-healthcare, DL-healthcare, and ML-DL-healthcare. We then offered ML and DL-based applications in the healthcare industry. Eventually, we emphasized the research disputes and recommendations for further studies based on our observations.
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Affiliation(s)
- Anichur Rahman
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
- Department of CSE, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Tanoy Debnath
- Department of CSE, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
- Department of CSE, Green University of Bangladesh, 220/D, Begum Rokeya Sarani, Dhaka -1207, Bangladesh
| | - Dipanjali Kundu
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
| | - Md. Saikat Islam Khan
- Department of CSE, Mawlana Bhashani Science and Technology University, Tangail, Bangladesh
| | - Airin Afroj Aishi
- Department of Computing and Information System, Daffodil International University, Savar, Dhaka, Bangladesh
| | - Sadia Sazzad
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
| | - Mohammad Sayduzzaman
- Department of CSE, National Institute of Textile Engineering and Research (NITER), Constituent Institute of the University of Dhaka, Savar, Dhaka-1350
| | - Shahab S. Band
- Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Taiwan
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7
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Purvis S, Gion T. Fluid Status Vulnerability in Older Adults. JOURNAL OF INFUSION NURSING 2024; 47:49-53. [PMID: 38211615 DOI: 10.1097/nan.0000000000000534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
There is a growing body of evidence about physiological changes with age that impact fluid and electrolyte balance. It is important that infusion nurses have knowledge in managing care for geriatric patients so they can identify these changes when they are exhibited. Knowing how to minimize the effect of these changes on the health of older adults is critical. The infusion nurse with knowledge of geriatric-focused care can avoid complications and critical illness in older adults. In addition, it is important to provide specific patient education that is grounded in geriatric best practices. This information will assist older adults to better protect themselves from dehydration, kidney injury, and other complications associated with fluid balance, such as delirium. This article reviews the literature on specific changes with aging that predispose older adults to adverse complications with fluid imbalance. New technology in geriatrics that can improve management of fluid status, such as dehydration and electrolyte monitors, are also discussed. This review included searches of the Medline®/PubMed® Database using MeSH terms (National Library of Medicine). Search terms included the following: aging-biological; aging kidney; water-electrolyte imbalance; dehydration; hypo-hypernatremia; hypo-hyperkalemia; delirium; wearable technology; and hydration monitors.
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Affiliation(s)
- Suzanne Purvis
- Confidence in Aging, LLC, Alexandria, Virginia (Purvis); Confidence in Aging, LLC, Madison, Wisconsin (Gion)
- Suzanne Purvis, DNP, APRN, GCNS-BC, FCNS, brings over 20 years' experience working in health care systems as a clinical nurse specialist in geriatrics to her current business, Confidence in Aging, LLC. The primary mission of Confidence in Aging is to help individuals, schools, communities, and businesses to better understand and manage aging. Suzanne has a doctorate in nursing from the University of Minnesota. She has won several awards in clinical excellence and in health care education and has achieved fellowship in the National Association of Clinical Nurse Specialists. Additionally, she is published on a variety of health care topics, including as contributing author in the current edition of the Adult-Gerontology Certification Review (ANCC, Springer Publishing, 2021). Suzanne's passion is to help everyone embrace and manage aging as a unique life phase
- Terry Gion, DNP, RN, CRRN, NE-BC, has over 40 years of experience working in the health care system. She has been a clinical nurse specialist in acute rehabilitation, nurse leader in medical and procedural nursing, and a consultant in various health care-related businesses. Terry has a doctorate of nursing from Concordia University. She has won various awards in clinical excellence and nursing leadership and has held board positions for professional organizations. She is published on clinical and leadership health care topics. Terry's passion is to provide evidence-based educational materials that can help others understand and manage their health and health care needs
| | - Terry Gion
- Confidence in Aging, LLC, Alexandria, Virginia (Purvis); Confidence in Aging, LLC, Madison, Wisconsin (Gion)
- Suzanne Purvis, DNP, APRN, GCNS-BC, FCNS, brings over 20 years' experience working in health care systems as a clinical nurse specialist in geriatrics to her current business, Confidence in Aging, LLC. The primary mission of Confidence in Aging is to help individuals, schools, communities, and businesses to better understand and manage aging. Suzanne has a doctorate in nursing from the University of Minnesota. She has won several awards in clinical excellence and in health care education and has achieved fellowship in the National Association of Clinical Nurse Specialists. Additionally, she is published on a variety of health care topics, including as contributing author in the current edition of the Adult-Gerontology Certification Review (ANCC, Springer Publishing, 2021). Suzanne's passion is to help everyone embrace and manage aging as a unique life phase
- Terry Gion, DNP, RN, CRRN, NE-BC, has over 40 years of experience working in the health care system. She has been a clinical nurse specialist in acute rehabilitation, nurse leader in medical and procedural nursing, and a consultant in various health care-related businesses. Terry has a doctorate of nursing from Concordia University. She has won various awards in clinical excellence and nursing leadership and has held board positions for professional organizations. She is published on clinical and leadership health care topics. Terry's passion is to provide evidence-based educational materials that can help others understand and manage their health and health care needs
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O'Reilly D, McGrath J, Martin-Loeches I. Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future. JOURNAL OF INTENSIVE MEDICINE 2024; 4:34-45. [PMID: 38263963 PMCID: PMC10800769 DOI: 10.1016/j.jointm.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 09/29/2023] [Accepted: 10/01/2023] [Indexed: 01/25/2024]
Abstract
Sepsis remains a major challenge internationally for healthcare systems. Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management. In sepsis, mortality increases with every hour left untreated. Artificial intelligence (AI) is transforming worldwide healthcare delivery at present. This review has outlined how AI can augment strategies to address this global disease burden. AI and machine learning (ML) algorithms can analyze vast quantities of increasingly complex clinical datasets from electronic medical records to assist clinicians in diagnosing and treating sepsis earlier than traditional methods. Our review highlights how these models can predict the risk of sepsis and organ failure even before it occurs. This gives providers additional time to plan and execute treatment plans, thereby avoiding increasing complications associated with delayed diagnosis of sepsis. The potential for cost savings with AI implementation is also discussed, including improving workflow efficiencies, reducing administrative costs, and improving healthcare outcomes. Despite these advantages, clinicians have been slow to adopt AI into clinical practice. Some of the limitations posed by AI solutions include the lack of diverse data sets for model building so that they are widely applicable for routine clinical use. Furthermore, the subsequent algorithms are often based on complex mathematics leading to clinician hesitancy to embrace such technologies. Finally, we highlight the need for robust political and regulatory frameworks in this area to achieve the trust and approval of clinicians and patients to implement this transformational technology.
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Affiliation(s)
- Darragh O'Reilly
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
| | - Jennifer McGrath
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
| | - Ignacio Martin-Loeches
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
- Department of Respiratory Intensive care, Hospital Clinic, Universitat de Barcelona, IDIBAPS, CIBERES, Barcelona, Spain
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Caiafa CF, Sun Z, Tanaka T, Marti-Puig P, Solé-Casals J. Special Issue "Machine Learning Methods for Biomedical Data Analysis". SENSORS (BASEL, SWITZERLAND) 2023; 23:9377. [PMID: 38067750 PMCID: PMC10708713 DOI: 10.3390/s23239377] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 11/21/2023] [Indexed: 12/18/2023]
Abstract
Machine learning is an effective method for developing automatic algorithms for analysing sophisticated biomedical data [...].
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Affiliation(s)
- Cesar F. Caiafa
- Instituto Argentino de Radioastronomía—CCT La Plata, CONICET/CIC-PBA/UNLP, V. Elisa 1894, Argentina
| | - Zhe Sun
- Computational Engineering Applications Unit, Head Office for Information Systems and Cybersecurity, RIKEN, Wako-Shi 351-0198, Japan;
| | - Toshihisa Tanaka
- Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan;
| | - Pere Marti-Puig
- Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, 08500 Vic, Catalonia, Spain;
| | - Jordi Solé-Casals
- Data and Signal Processing Research Group, University of Vic-Central University of Catalonia, 08500 Vic, Catalonia, Spain;
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Shi B, Dhaliwal SS, Soo M, Chan C, Wong J, Lam NWC, Zhou E, Paitimusa V, Loke KY, Chin J, Chua MT, Liaw KCS, Lim AWH, Insyirah FF, Yen SC, Tay A, Ang SB. Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation. JMIR AI 2023; 2:e48340. [PMID: 38875549 PMCID: PMC11041426 DOI: 10.2196/48340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 08/31/2023] [Accepted: 09/28/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND Diabetes mellitus is the most challenging and fastest-growing global public health concern. Approximately 10.5% of the global adult population is affected by diabetes, and almost half of them are undiagnosed. The growing at-risk population exacerbates the shortage of health resources, with an estimated 10.6% and 6.2% of adults worldwide having impaired glucose tolerance and impaired fasting glycemia, respectively. All current diabetes screening methods are invasive and opportunistic and must be conducted in a hospital or laboratory by trained professionals. At-risk participants might remain undetected for years and miss the precious time window for early intervention to prevent or delay the onset of diabetes and its complications. OBJECTIVE We aimed to develop an artificial intelligence solution to recognize elevated blood glucose levels (≥7.8 mmol/L) noninvasively and evaluate diabetic risk based on repeated measurements. METHODS This study was conducted at KK Women's and Children's Hospital in Singapore, and 500 participants were recruited (mean age 38.73, SD 10.61 years; mean BMI 24.4, SD 5.1 kg/m2). The blood glucose levels for most participants were measured before and after consuming 75 g of sugary drinks using both a conventional glucometer (Accu-Chek Performa) and a wrist-worn wearable. The results obtained from the glucometer were used as ground-truth measurements. We performed extensive feature engineering on photoplethysmography (PPG) sensor data and identified features that were sensitive to glucose changes. These selected features were further analyzed using an explainable artificial intelligence approach to understand their contribution to our predictions. RESULTS Multiple machine learning models were trained and assessed with 10-fold cross-validation, using participant demographic data and critical features extracted from PPG measurements as predictors. A support vector machine with a radial basis function kernel had the best detection performance, with an average accuracy of 84.7%, a sensitivity of 81.05%, a specificity of 88.3%, a precision of 87.51%, a geometric mean of 84.54%, and F score of 84.03%. CONCLUSIONS Our findings suggest that PPG measurements can be used to identify participants with elevated blood glucose measurements and assist in the screening of participants for diabetes risk.
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Affiliation(s)
- Bohan Shi
- Actxa Pte Ltd, Singapore, Singapore
- Activate Interactive Pte Ltd, Singapore, Singapore
| | - Satvinder Singh Dhaliwal
- Curtin Health Innovation Research Institute, Curtin University, Perth, Australia
- Faculty of Health Sciences, Curtin University, Perth, Australia
- Duke-NUS Graduate Medical School, National University of Singapore, Singapore, Singapore
| | | | - Cheri Chan
- KK Women's and Children's Hospital, Singapore, Singapore
| | | | | | - Entong Zhou
- Activate Interactive Pte Ltd, Singapore, Singapore
| | | | - Kum Yin Loke
- Activate Interactive Pte Ltd, Singapore, Singapore
| | - Joel Chin
- Activate Interactive Pte Ltd, Singapore, Singapore
| | - Mei Tuan Chua
- KK Women's and Children's Hospital, Singapore, Singapore
| | | | | | | | - Shih-Cheng Yen
- Innovation and Design Programme, Faculty of Engineering, National University of Singapore, Singapore, Singapore
| | - Arthur Tay
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Seng Bin Ang
- Family Medicine Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore
- Menopause Unit, KK Women's and Children's Hospital, Singapore, Singapore
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Chang MC, Kim JK, Park D, Kim JH, Kim CR, Choo YJ. The Use of Artificial Intelligence to Predict the Prognosis of Patients Undergoing Central Nervous System Rehabilitation: A Narrative Review. Healthcare (Basel) 2023; 11:2687. [PMID: 37830724 PMCID: PMC10572243 DOI: 10.3390/healthcare11192687] [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: 09/01/2023] [Revised: 09/27/2023] [Accepted: 09/30/2023] [Indexed: 10/14/2023] Open
Abstract
Applications of machine learning in the healthcare field have become increasingly diverse. In this review, we investigated the integration of artificial intelligence (AI) in predicting the prognosis of patients with central nervous system disorders such as stroke, traumatic brain injury, and spinal cord injury. AI algorithms have shown promise in prognostic assessment, but challenges remain in achieving a higher prediction accuracy for practical clinical use. We suggest that accumulating more diverse data, including medical imaging and collaborative efforts among hospitals, can enhance the predictive capabilities of AI. As healthcare professionals become more familiar with AI, its role in central nervous system rehabilitation is expected to advance significantly, revolutionizing patient care.
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Affiliation(s)
- Min Cheol Chang
- Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu 42415, Republic of Korea;
| | - Jeoung Kun Kim
- Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si 38541, Republic of Korea;
| | - Donghwi Park
- Department of Rehabilitation Medicine, Daegu Fatima Hospital, Daegu 41199, Republic of Korea;
| | - Jang Hwan Kim
- Department of Rehabilitation Technology, Graduate School of Hanseo University, Seosan, Chungcheongnam-do 31962, Republic of Korea;
| | - Chung Reen Kim
- Department of Physical Medicine and Rehabilitation, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan 44033, Republic of Korea;
| | - Yoo Jin Choo
- Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu 42415, Republic of Korea;
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van Breugel M, Fehrmann RSN, Bügel M, Rezwan FI, Holloway JW, Nawijn MC, Fontanella S, Custovic A, Koppelman GH. Current state and prospects of artificial intelligence in allergy. Allergy 2023; 78:2623-2643. [PMID: 37584170 DOI: 10.1111/all.15849] [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: 04/20/2023] [Revised: 07/08/2023] [Accepted: 07/31/2023] [Indexed: 08/17/2023]
Abstract
The field of medicine is witnessing an exponential growth of interest in artificial intelligence (AI), which enables new research questions and the analysis of larger and new types of data. Nevertheless, applications that go beyond proof of concepts and deliver clinical value remain rare, especially in the field of allergy. This narrative review provides a fundamental understanding of the core concepts of AI and critically discusses its limitations and open challenges, such as data availability and bias, along with potential directions to surmount them. We provide a conceptual framework to structure AI applications within this field and discuss forefront case examples. Most of these applications of AI and machine learning in allergy concern supervised learning and unsupervised clustering, with a strong emphasis on diagnosis and subtyping. A perspective is shared on guidelines for good AI practice to guide readers in applying it effectively and safely, along with prospects of field advancement and initiatives to increase clinical impact. We anticipate that AI can further deepen our knowledge of disease mechanisms and contribute to precision medicine in allergy.
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Affiliation(s)
- Merlijn van Breugel
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- MIcompany, Amsterdam, the Netherlands
| | - Rudolf S N Fehrmann
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | | | - Faisal I Rezwan
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- Department of Computer Science, Aberystwyth University, Aberystwyth, UK
| | - John W Holloway
- Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, UK
- National Institute for Health and Care Research Southampton Biomedical Research Centre, University Hospitals Southampton NHS Foundation Trust, Southampton, UK
| | - Martijn C Nawijn
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Department of Pathology and Medical Biology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Sara Fontanella
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Adnan Custovic
- National Heart and Lung Institute, Imperial College London, London, UK
- National Institute for Health and Care Research Imperial Biomedical Research Centre (BRC), London, UK
| | - Gerard H Koppelman
- Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
- Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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13
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Mohamed AA, Marques O. Diagnostic Efficacy and Clinical Relevance of Artificial Intelligence in Detecting Cognitive Decline. Cureus 2023; 15:e47004. [PMID: 37965412 PMCID: PMC10641267 DOI: 10.7759/cureus.47004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/13/2023] [Indexed: 11/16/2023] Open
Abstract
Cognitive impairment is an age-associated disorder of increasing prevalence as the aging population continues to grow. Classified based on the level of cognitive decline, memory, function, and capacity to conduct activities of daily living, cognitive impairment ranges from mild cognitive impairment to dementia. When considering the insidious nature of the etiologies responsible for varying degrees of cognitive impairment, early diagnosis may provide a clinical benefit through the facilitation of early treatment. Typical diagnosis relies heavily on evaluation in a primary care setting. However, there is evidence that other diagnostic tools may aid in an earlier diagnosis of the different underlying pathologies responsible for cognitive impairment. Artificial intelligence represents a new intersecting field with healthcare that may aid in the early detection of neurodegenerative disorders. When assessing the role of AI in detecting cognitive decline, it is important to consider both the diagnostic efficacy of AI algorithms and the clinical relevance and impact of early interventions as a result of early detection. Thus, this review highlights promising investigations and developments in the space of artificial intelligence and healthcare and their potential to impact patient outcomes.
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Affiliation(s)
- Ali A Mohamed
- Neurological Surgery, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, USA
- Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, USA
| | - Oge Marques
- Biomedical Sciences, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, USA
- Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, USA
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14
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Ravindra NG, Espinosa C, Berson E, Phongpreecha T, Zhao P, Becker M, Chang AL, Shome S, Marić I, De Francesco D, Mataraso S, Saarunya G, Thuraiappah M, Xue L, Gaudillière B, Angst MS, Shaw GM, Herzog ED, Stevenson DK, England SK, Aghaeepour N. Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity. NPJ Digit Med 2023; 6:171. [PMID: 37770643 PMCID: PMC10539360 DOI: 10.1038/s41746-023-00911-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Accepted: 08/21/2023] [Indexed: 09/30/2023] Open
Abstract
Preterm birth (PTB) is the leading cause of infant mortality globally. Research has focused on developing predictive models for PTB without prioritizing cost-effective interventions. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations (LMICs). However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy. In this study, we use physical activity data collected using a wearable device comprising over 181,944 h of data across N = 1083 patients. Using a new state-of-the art deep learning time-series classification architecture, we develop a 'clock' of healthy dynamics during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also develop novel interpretability algorithms that integrate unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy. We found that deviations from a normal 'clock' of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e - 67, permutation test) and when our model overestimates GA, there are 1.44 times (P = 2.82e - 39, permutation test) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043, Spearman's), indicating that our model assigns a more advanced GA when an individual's daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e - 21, Mann-Whitney U). Combining prediction and interpretability allows us to signal when activity behaviors alter the likelihood of preterm birth and advocates for the development of clinical decision support through passive monitoring and exercise habit and sleep recommendations, which can be easily implemented in LMICs.
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Affiliation(s)
- Neal G Ravindra
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Eloïse Berson
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford School of Medicine, Stanford, CA, USA
| | - Thanaphong Phongpreecha
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford School of Medicine, Stanford, CA, USA
| | - Peinan Zhao
- Department of Biology, Washington University in St. Louis, St. Louis, MO, USA
- Department of Obstetrics and Gynecology, Washington University in St. Louis, St. Louis, MO, USA
| | - Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Alan L Chang
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Sayane Shome
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Ivana Marić
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Davide De Francesco
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Samson Mataraso
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Geetha Saarunya
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Melan Thuraiappah
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Lei Xue
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Brice Gaudillière
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Martin S Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA
| | - Gary M Shaw
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
| | - Erik D Herzog
- Department of Biology, Washington University in St. Louis, St. Louis, MO, USA
| | - David K Stevenson
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA
| | - Sarah K England
- Department of Obstetrics and Gynecology, Washington University in St. Louis, St. Louis, MO, USA
| | - Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford School of Medicine, Stanford, CA, USA.
- Department of Pediatrics, Stanford School of Medicine, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
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15
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Jerath R, Syam M, Ahmed S. The Future of Stress Management: Integration of Smartwatches and HRV Technology. SENSORS (BASEL, SWITZERLAND) 2023; 23:7314. [PMID: 37687769 PMCID: PMC10490434 DOI: 10.3390/s23177314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 08/10/2023] [Accepted: 08/15/2023] [Indexed: 09/10/2023]
Abstract
In the modern world, stress has become a pervasive concern that affects individuals' physical and mental well-being. To address this issue, many wearable devices have emerged as potential tools for stress detection and management by measuring heart rate, heart rate variability (HRV), and various metrics related to it. This literature review aims to provide a comprehensive analysis of existing research on HRV tracking and biofeedback using smartwatches pairing with reliable 3rd party mobile apps like Elite HRV, Welltory, and HRV4Training specifically designed for stress detection and management. We apply various algorithms and methodologies employed for HRV analysis and stress detection including time-domain, frequency-domain, and non-linear analysis techniques. Prominent smartwatches, such as Apple Watch, Garmin, Fitbit, Polar, and Samsung Galaxy Watch, are evaluated based on their HRV measurement accuracy, data quality, sensor technology, and integration with stress management features. We describe the efficacy of smartwatches in providing real-time stress feedback, personalized stress management interventions, and promoting overall well-being. To assist researchers, doctors, and developers with using smartwatch technology to address stress and promote holistic well-being, we discuss the data's advantages and limitations, future developments, and the significance of user-centered design and personalized interventions.
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Rojas-Valverde D, Gómez-Ruano MA, Ibañez SJ, Nikolaidis PT. Editorial: New technologies and statistical models applied to sports and exercise science research: methodological, technical and practical considerations. Front Sports Act Living 2023; 5:1267548. [PMID: 37662114 PMCID: PMC10473824 DOI: 10.3389/fspor.2023.1267548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 08/01/2023] [Indexed: 09/05/2023] Open
Affiliation(s)
- Daniel Rojas-Valverde
- Centro de Investigación y Diagnóstico en Salud y Deporte (CIDISAD-NARS), Escuela Ciencias del Movimiento Humano y Calidad de Vida, Universidad Nacional, Heredia, Costa Rica
- Sport Injury Clinic, Escuela Ciencias del Movimiento Humano y Calidad de Vida, Universidad Nacional, Heredia, Costa Rica
| | - Miguel A. Gómez-Ruano
- Facultad de Ciencias de la Actividad Física y el Deporte, Universidad Politécnica de Madrid, Madrid, Spain
| | - Sergio J. Ibañez
- Grupo de Optimización del Entrenamiento y Rendimiento Deportivo, Universidad de Extremadura, Cáceres, Spain
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Liu JH, Shih CY, Huang HL, Peng JK, Cheng SY, Tsai JS, Lai F. Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study. J Med Internet Res 2023; 25:e47366. [PMID: 37594793 PMCID: PMC10474512 DOI: 10.2196/47366] [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: 03/17/2023] [Revised: 07/02/2023] [Accepted: 07/14/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life. OBJECTIVE This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings. METHODS This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning-based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance. RESULTS From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase. CONCLUSIONS We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care. TRIAL REGISTRATION ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907.
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Affiliation(s)
- Jen-Hsuan Liu
- Department of Family Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
| | - Chih-Yuan Shih
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Hsien-Liang Huang
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Jen-Kuei Peng
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Shao-Yi Cheng
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Jaw-Shiun Tsai
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Feipei Lai
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
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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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19
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Jeyaraman M, Balaji S, Jeyaraman N, Yadav S. Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare. Cureus 2023; 15:e43262. [PMID: 37692617 PMCID: PMC10492220 DOI: 10.7759/cureus.43262] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/10/2023] [Indexed: 09/12/2023] Open
Abstract
The integration of artificial intelligence (AI) into healthcare promises groundbreaking advancements in patient care, revolutionizing clinical diagnosis, predictive medicine, and decision-making. This transformative technology uses machine learning, natural language processing, and large language models (LLMs) to process and reason like human intelligence. OpenAI's ChatGPT, a sophisticated LLM, holds immense potential in medical practice, research, and education. However, as AI in healthcare gains momentum, it brings forth profound ethical challenges that demand careful consideration. This comprehensive review explores key ethical concerns in the domain, including privacy, transparency, trust, responsibility, bias, and data quality. Protecting patient privacy in data-driven healthcare is crucial, with potential implications for psychological well-being and data sharing. Strategies like homomorphic encryption (HE) and secure multiparty computation (SMPC) are vital to preserving confidentiality. Transparency and trustworthiness of AI systems are essential, particularly in high-risk decision-making scenarios. Explainable AI (XAI) emerges as a critical aspect, ensuring a clear understanding of AI-generated predictions. Cybersecurity becomes a pressing concern as AI's complexity creates vulnerabilities for potential breaches. Determining responsibility in AI-driven outcomes raises important questions, with debates on AI's moral agency and human accountability. Shifting from data ownership to data stewardship enables responsible data management in compliance with regulations. Addressing bias in healthcare data is crucial to avoid AI-driven inequities. Biases present in data collection and algorithm development can perpetuate healthcare disparities. A public-health approach is advocated to address inequalities and promote diversity in AI research and the workforce. Maintaining data quality is imperative in AI applications, with convolutional neural networks showing promise in multi-input/mixed data models, offering a comprehensive patient perspective. In this ever-evolving landscape, it is imperative to adopt a multidimensional approach involving policymakers, developers, healthcare practitioners, and patients to mitigate ethical concerns. By understanding and addressing these challenges, we can harness the full potential of AI in healthcare while ensuring ethical and equitable outcomes.
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Affiliation(s)
- Madhan Jeyaraman
- Orthopedics, ACS Medical College and Hospital, Dr. MGR Educational and Research Institute, Chennai, IND
| | - Sangeetha Balaji
- Orthopedics, Government Medical College, Omandurar Government Estate, Chennai, IND
| | - Naveen Jeyaraman
- Orthopedics, ACS Medical College and Hospital, Dr. MGR Educational and Research Institute, Chennai, IND
| | - Sankalp Yadav
- Medicine, Shri Madan Lal Khurana Chest Clinic, New Delhi, IND
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20
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Prasad S, Farella M. Wearables for personalized monitoring of masticatory muscle activity - opportunities, challenges, and the future. Clin Oral Investig 2023; 27:4861-4867. [PMID: 37410151 DOI: 10.1007/s00784-023-05127-7] [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: 09/13/2022] [Accepted: 06/20/2023] [Indexed: 07/07/2023]
Abstract
Wearable devices are worn on or remain in close proximity of the human body. The use of wearable devices specific to the orofacial region is steadily increasing. Orofacial applications of wearable devices include supplementing diagnosis, tracking treatment progress, monitoring patient compliance, and understanding oral parafunctional behaviours. In this short communication, the role of wearable devices in advancing personalized dental medicine are highlighted with a specific focus on masticatory muscle activity monitoring in naturalistic settings. Additionally, challenges, opportunities, as well as future research areas for successful use of wearable devices for precise, personalized care of muscle disorders are discussed.
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Affiliation(s)
- Sabarinath Prasad
- Department of Orthodontics, Hamdan Bin Mohammed College of Dental Medicine, Mohammed Bin Rashid University, Dubai, United Arab Emirates.
| | - Mauro Farella
- Discipline of Orthodontics, Faculty of Dentistry, University of Otago, Dunedin, New Zealand
- Discipline of Orthodontics and Pediatric Dentistry, Department of Surgical Science, University of Cagliari, Cagliari, Italy
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21
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Rösler W, Altenbuchinger M, Baeßler B, Beissbarth T, Beutel G, Bock R, von Bubnoff N, Eckardt JN, Foersch S, Loeffler CML, Middeke JM, Mueller ML, Oellerich T, Risse B, Scherag A, Schliemann C, Scholz M, Spang R, Thielscher C, Tsoukakis I, Kather JN. An overview and a roadmap for artificial intelligence in hematology and oncology. J Cancer Res Clin Oncol 2023; 149:7997-8006. [PMID: 36920563 PMCID: PMC10374829 DOI: 10.1007/s00432-023-04667-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 02/23/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals. METHODS In this article, we provide an expert-based consensus statement by the joint Working Group on "Artificial Intelligence in Hematology and Oncology" by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology. RESULTS First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology. CONCLUSION Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future.
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Affiliation(s)
- Wiebke Rösler
- Department for Medical Oncology and Hematology, University Hospital Zurich, Zurich, Switzerland
| | - Michael Altenbuchinger
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Tim Beissbarth
- Department of Medical Bioinformatics, University Medical Center Göttingen, Göttingen, Germany
| | - Gernot Beutel
- Department for Hematology, Hemostasis, Oncology and Stem Cell Transplantation, Hannover Medical School, Hannover, Germany
| | - Robert Bock
- IMMS Institute for Microelectronics and Mechatronics Systems GmbH (NPO), Ilmenau, Germany
| | - Nikolas von Bubnoff
- Department of Hematology and Oncology, Medical Center, University of Schleswig Holstein, Campus Lübeck, Lübeck, Germany
| | - Jan-Niklas Eckardt
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Sebastian Foersch
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - Chiara M L Loeffler
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | - Jan Moritz Middeke
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany
| | | | - Thomas Oellerich
- Medizinische Klinik 2-Haematology/Oncology, University Hospital, Frankfurt am Main, Germany
| | - Benjamin Risse
- Computer Vision and Machine Learning Systems Group, Institute for Geoinformatics, University of Münster, Münster, Germany
| | - André Scherag
- Institute of Medical Statistics, Computer and Data Sciences, Jena University Hospital - Friedrich Schiller University, Jena, Germany
| | | | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany
| | - Rainer Spang
- Department of Statistical Bioinformatics, University of Regensburg, Regensburg, Germany
| | | | - Ioannis Tsoukakis
- Department of Hematology and Oncology, Sana Klinikum Offenbach, Offenbach, Germany
| | - Jakob Nikolas Kather
- Department of Medicine 1, University Hospital Carl Gustav Carus, Technical University Dresden, Dresden, Germany.
- Else Kroener Fresenius Center for Digital Health (EFFZ), Technical University Dresden, Dresden, Germany.
- Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
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22
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Almadhor A, Sampedro GA, Abisado M, Abbas S. Efficient Feature-Selection-Based Stacking Model for Stress Detection Based on Chest Electrodermal Activity. SENSORS (BASEL, SWITZERLAND) 2023; 23:6664. [PMID: 37571448 PMCID: PMC10422546 DOI: 10.3390/s23156664] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 07/10/2023] [Accepted: 07/18/2023] [Indexed: 08/13/2023]
Abstract
Contemporary advancements in wearable equipment have generated interest in continuously observing stress utilizing various physiological indicators. Early stress detection can improve healthcare by lessening the negative effects of chronic stress. Machine learning (ML) methodologies have been modified for healthcare equipment to monitor user health situations utilizing sufficient user information. Nevertheless, more data are needed to make applying Artificial Intelligence (AI) methodologies in the medical field easier. This research aimed to detect stress using a stacking model based on machine learning algorithms using chest-based features from the Wearable Stress and Affect Detection (WESAD) dataset. We converted this natural dataset into a convenient format for the suggested model by performing data visualization and preprocessing using the RESP feature and feature analysis using the Z-score, SelectKBest feature, the Synthetic Minority Over-Sampling Technique (SMOTE), and normalization. The efficiency of the proposed model was estimated regarding accuracy, precision, recall, and F1-score. The experimental outcome illustrated the efficacy of the proposed stacking technique, achieving 0.99% accuracy. The results revealed that the proposed stacking methodology performed better than traditional methodologies and previous studies.
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Affiliation(s)
- Ahmad Almadhor
- Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
| | - Gabriel Avelino Sampedro
- Faculty of Information and Communication Studies, University of the Philippines Open University, Los Baños 4031, Philippines;
- Center for Computational Imaging and Visual Innovations, De La Salle University, Manila 1004, Philippines
| | - Mideth Abisado
- College of Computing and Information Technologies, National University, Manila 1008, Philippines;
| | - Sidra Abbas
- Department of Computer Science, COMSATS University, Islamabad 22060, Pakistan
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23
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Sel K, Mohammadi A, Pettigrew RI, Jafari R. Physics-informed neural networks for modeling physiological time series for cuffless blood pressure estimation. NPJ Digit Med 2023; 6:110. [PMID: 37296218 DOI: 10.1038/s41746-023-00853-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
The bold vision of AI-driven pervasive physiological monitoring, through the proliferation of off-the-shelf wearables that began a decade ago, has created immense opportunities to extract actionable information for precision medicine. These AI algorithms model input-output relationships of a system that, in many cases, exhibits complex nature and personalization requirements. A particular example is cuffless blood pressure estimation using wearable bioimpedance. However, these algorithms need training over significant amount of ground truth data. In the context of biomedical applications, collecting ground truth data, particularly at the personalized level is challenging, burdensome, and in some cases infeasible. Our objective is to establish physics-informed neural network (PINN) models for physiological time series data that would use minimal ground truth information to extract complex cardiovascular information. We achieve this by building Taylor's approximation for gradually changing known cardiovascular relationships between input and output (e.g., sensor measurements to blood pressure) and incorporating this approximation into our proposed neural network training. The effectiveness of the framework is demonstrated through a case study: continuous cuffless BP estimation from time series bioimpedance data. We show that by using PINNs over the state-of-the-art time series models tested on the same datasets, we retain high correlations (systolic: 0.90, diastolic: 0.89) and low error (systolic: 1.3 ± 7.6 mmHg, diastolic: 0.6 ± 6.4 mmHg) while reducing the amount of ground truth training data on average by a factor of 15. This could be helpful in developing future AI algorithms to help interpret pervasive physiologic data using minimal amount of training data.
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Affiliation(s)
- Kaan Sel
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA
| | - Amirmohammad Mohammadi
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA
| | | | - Roozbeh Jafari
- Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA.
- Department of Computer Science and Engineering, Texas A&M University, College Station, TX, USA.
- School of Engineering Medicine, Texas A&M University, Houston, TX, USA.
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24
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Facciorusso S, Spina S, Reebye R, Turolla A, Calabrò RS, Fiore P, Santamato A. Sensor-Based Rehabilitation in Neurological Diseases: A Bibliometric Analysis of Research Trends. Brain Sci 2023; 13:brainsci13050724. [PMID: 37239196 DOI: 10.3390/brainsci13050724] [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: 03/03/2023] [Revised: 04/20/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND As the field of sensor-based rehabilitation continues to expand, it is important to gain a comprehensive understanding of its current research landscape. This study aimed to conduct a bibliometric analysis to identify the most influential authors, institutions, journals, and research areas in this field. METHODS A search of the Web of Science Core Collection was performed using keywords related to sensor-based rehabilitation in neurological diseases. The search results were analyzed with CiteSpace software using bibliometric techniques, including co-authorship analysis, citation analysis, and keyword co-occurrence analysis. RESULTS Between 2002 and 2022, 1103 papers were published on the topic, with slow growth from 2002 to 2017, followed by a rapid increase from 2018 to 2022. The United States was the most active country, while the Swiss Federal Institute of Technology had the highest number of publications among institutions. Sensors published the most papers. The top keywords included rehabilitation, stroke, and recovery. The clusters of keywords comprised machine learning, specific neurological conditions, and sensor-based rehabilitation technologies. CONCLUSIONS This study provides a comprehensive overview of the current state of sensor-based rehabilitation research in neurological diseases, highlighting the most influential authors, journals, and research themes. The findings can help researchers and practitioners to identify emerging trends and opportunities for collaboration and can inform the development of future research directions in this field.
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Affiliation(s)
- Salvatore Facciorusso
- Department of Medical and Surgical Specialties and Dentistry, University of Campania "Luigi Vanvitelli", 80138 Naples, Italy
- Spasticity and Movement Disorders "ReSTaRt", Unit Physical Medicine and Rehabilitation Section, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy
| | - Stefania Spina
- Spasticity and Movement Disorders "ReSTaRt", Unit Physical Medicine and Rehabilitation Section, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy
| | - Rajiv Reebye
- Division of Physical Medicine and Rehabilitation, Faculty of Medicine, University of British Columbia, Vancouver, BC V5Z 2G9, Canada
| | - Andrea Turolla
- Department of Biomedical and Neuromotor Sciences-DIBINEM, Alma Mater Studiorum Università di Bologna, 40138 Bologna, Italy
| | | | - Pietro Fiore
- Neurorehabilitation Unit, Institute of Bari, Istituti Clinici Scientifici Maugeri IRCCS, 70124 Bari, Italy
| | - Andrea Santamato
- Spasticity and Movement Disorders "ReSTaRt", Unit Physical Medicine and Rehabilitation Section, Department of Medical and Surgical Sciences, University of Foggia, 71122 Foggia, Italy
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25
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Almadhor A, Sampedro GA, Abisado M, Abbas S, Kim YJ, Khan MA, Baili J, Cha JH. Wrist-Based Electrodermal Activity Monitoring for Stress Detection Using Federated Learning. SENSORS (BASEL, SWITZERLAND) 2023; 23:3984. [PMID: 37112323 PMCID: PMC10146352 DOI: 10.3390/s23083984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/11/2023] [Accepted: 04/12/2023] [Indexed: 06/19/2023]
Abstract
With the most recent developments in wearable technology, the possibility of continually monitoring stress using various physiological factors has attracted much attention. By reducing the detrimental effects of chronic stress, early diagnosis of stress can enhance healthcare. Machine Learning (ML) models are trained for healthcare systems to track health status using adequate user data. Insufficient data is accessible, however, due to privacy concerns, making it challenging to use Artificial Intelligence (AI) models in the medical industry. This research aims to preserve the privacy of patient data while classifying wearable-based electrodermal activities. We propose a Federated Learning (FL) based approach using a Deep Neural Network (DNN) model. For experimentation, we use the Wearable Stress and Affect Detection (WESAD) dataset, which includes five data states: transient, baseline, stress, amusement, and meditation. We transform this raw dataset into a suitable form for the proposed methodology using the Synthetic Minority Oversampling Technique (SMOTE) and min-max normalization pre-processing methods. In the FL-based technique, the DNN algorithm is trained on the dataset individually after receiving model updates from two clients. To decrease the over-fitting effect, every client analyses the results three times. Accuracies, Precision, Recall, F1-scores, and Area Under the Receiver Operating Curve (AUROC) values are evaluated for each client. The experimental result shows the effectiveness of the federated learning-based technique on a DNN, reaching 86.82% accuracy while also providing privacy to the patient's data. Using the FL-based DNN model over a WESAD dataset improves the detection accuracy compared to the previous studies while also providing the privacy of patient data.
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Affiliation(s)
- Ahmad Almadhor
- Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia;
| | - Gabriel Avelino Sampedro
- Faculty of Information and Communication Studies, University of the Philippines Open University, Los Baños 4031, Philippines;
- Center for Computational Imaging and Visual Innovations, De La Salle University, 2401 Taft Ave., Malate, Manila 1004, Philippines
| | - Mideth Abisado
- College of Computing and Information Technologies, National University, Manila 1008, Philippines;
| | - Sidra Abbas
- Department of Computer Science, COMSATS University, Islamabad 45550, Pakistan
| | - Ye-Jin Kim
- Department of Computer Science, Hanyang University, Seoul 04763, Republic of Korea; (Y.-J.K.); (J.-H.C.)
| | | | - Jamel Baili
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
- Higher Institute of Applied Science and Technology of Sousse (ISSATS), Cité Taffala (Ibn Khaldoun) 4003 Sousse, University of Sousse, Sousse 4000, Tunisia
| | - Jae-Hyuk Cha
- Department of Computer Science, Hanyang University, Seoul 04763, Republic of Korea; (Y.-J.K.); (J.-H.C.)
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26
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Yhdego HH, Nayebnazar A, Amrollahi F, Boussina A, Shashikumar S, Wardi G, Nemati S. Prediction of Unplanned Hospital Readmission using Clinical and Longitudinal Wearable Sensor Features. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.10.23288371. [PMID: 37090626 PMCID: PMC10120790 DOI: 10.1101/2023.04.10.23288371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Predictive models have been suggested as potential tools for identifying highest risk patients for hospital readmissions, in order to improve care coordination and ultimately long-term patient outcomes. However, the accuracy of current predictive models for readmission prediction is still moderate and further data enrichment is needed to identify at risk patients. This paper describes models to predict 90-day readmission, focusing on testing the predictive performance of wearable sensor features generated using multiscale entropy techniques and clinical features. Our study explores ways to incorporate pre-discharge and post-discharge wearable sensor features to make robust patient predictions. Data were used from participants enrolled in the AllofUs Research program. We extracted the inpatient cohort of patients and integrated clinical data from the electronic health records (EHR) and Fitbit sensor measurements. Entropy features were calculated from the longitudinal wearable sensor data, such as heart rate and mobility-related measurements, in order to characterize time series variability and complexity. Our best performing model acheived an AUC of 83%, and at 80% sensitivity acheived 75% specificity and 57% positive predictive value. Our results indicate that it would be possible to improve the ability to predict unplanned hospital readmissions by considering pre-discharge and post-discharge wearable features.
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Affiliation(s)
- Haben H Yhdego
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Arshia Nayebnazar
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Fatemeh Amrollahi
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Aaron Boussina
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Supreeth Shashikumar
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Gabriel Wardi
- Division of Pulmonary, Critical Care and Sleep Medicine, UC San Diego, La Jolla, CA
| | - Shamim Nemati
- Division of Biomedical Informatics, University of California San Diego, La Jolla, CA
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27
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Huang Y, Roy N, Dhar E, Upadhyay U, Kabir MA, Uddin M, Tseng CL, Syed-Abdul S. Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information. Cancers (Basel) 2023; 15:cancers15082232. [PMID: 37190161 DOI: 10.3390/cancers15082232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/07/2023] [Accepted: 04/07/2023] [Indexed: 05/17/2023] Open
Abstract
(1) Background: Predicting the survival of patients in end-of-life care is crucial, and evaluating their performance status is a key factor in determining their likelihood of survival. However, the current traditional methods for predicting survival are limited due to their subjective nature. Wearable technology that provides continuous patient monitoring is a more favorable approach for predicting survival outcomes among palliative care patients. (2) Aims and objectives: In this study, we aimed to explore the potential of using deep learning (DL) model approaches to predict the survival outcomes of end-stage cancer patients. Furthermore, we also aimed to compare the accuracy of our proposed activity monitoring and survival prediction model with traditional prognostic tools, such as the Karnofsky Performance Scale (KPS) and the Palliative Performance Index (PPI). (3) Method: This study recruited 78 patients from the Taipei Medical University Hospital's palliative care unit, with 66 (39 male and 27 female) patients eventually being included in our DL model for predicting their survival outcomes. (4) Results: The KPS and PPI demonstrated an overall accuracy of 0.833 and 0.615, respectively. In comparison, the actigraphy data exhibited a higher accuracy at 0.893, while the accuracy of the wearable data combined with clinical information was even better, at 0.924. (5) Conclusion: Our study highlights the significance of incorporating clinical data alongside wearable sensors to predict prognosis. Our findings suggest that 48 h of data is sufficient for accurate predictions. The integration of wearable technology and the prediction model in palliative care has the potential to improve decision making for healthcare providers and can provide better support for patients and their families. The outcomes of this study can possibly contribute to the development of personalized and patient-centered end-of-life care plans in clinical practice.
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Affiliation(s)
- Yaoru Huang
- Department of Radiation Oncology, Taipei Medical University Hospital, Taipei 110, Taiwan
- Graduate Institute of Biomedical Materials and Tissue Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan
| | - Nidita Roy
- Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong 4349, Bangladesh
| | - Eshita Dhar
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
| | - Umashankar Upadhyay
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
- Faculty of Applied Sciences and Biotechnology, Shoolini University of Biotechnology and Management Sciences, Solan 173229, Himachal Pradesh, India
| | - Muhammad Ashad Kabir
- School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2678, Australia
| | - Mohy Uddin
- Research Quality Management Section, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Ministry of National Guard-Health Affairs, Riyadh 11481, Saudi Arabia
| | - Ching-Li Tseng
- Graduate Institute of Biomedical Materials and Tissue Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan
- International Ph.D. Program in Biomedical Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
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28
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Ghosh A, Nag S, Gomes A, Gosavi A, Ghule G, Kundu A, Purohit B, Srivastava R. Applications of Smart Material Sensors and Soft Electronics in Healthcare Wearables for Better User Compliance. MICROMACHINES 2022; 14:121. [PMID: 36677182 PMCID: PMC9862021 DOI: 10.3390/mi14010121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/25/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
The need for innovation in the healthcare sector is essential to meet the demand of a rapidly growing population and the advent of progressive chronic ailments. Over the last decade, real-time monitoring of health conditions has been prioritized for accurate clinical diagnosis and access to accelerated treatment options. Therefore, the demand for wearable biosensing modules for preventive and monitoring purposes has been increasing over the last decade. Application of machine learning, big data analysis, neural networks, and artificial intelligence for precision and various power-saving approaches are used to increase the reliability and acceptance of smart wearables. However, user compliance and ergonomics are key areas that need focus to make the wearables mainstream. Much can be achieved through the incorporation of smart materials and soft electronics. Though skin-friendly wearable devices have been highlighted recently for their multifunctional abilities, a detailed discussion on the integration of smart materials for higher user compliance is still missing. In this review, we have discussed the principles and applications of sustainable smart material sensors and soft electronics for better ergonomics and increased user compliance in various healthcare devices. Moreover, the importance of nanomaterials and nanotechnology is discussed in the development of smart wearables.
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Affiliation(s)
- Arnab Ghosh
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Sagnik Nag
- Department of Biotechnology, School of Biosciences & Technology, Vellore Institute of Technology (VIT), Tiruvalam Road, Vellore 632014, Tamil Nadu, India
| | - Alyssa Gomes
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Apurva Gosavi
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Gauri Ghule
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Aniket Kundu
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
| | - Buddhadev Purohit
- DTU Bioengineering, Technical University of Denmark, Søltofts Plads 221, 2800 Kongens Lyngby, Denmark
| | - Rohit Srivastava
- Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India
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29
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Sugibayashi K. Transdermal Drug Delivery Systems: From the Dawn and Early Stage to the Development and Maturity Stage, and the Future. YAKUGAKU ZASSHI 2022; 142:1227-1253. [DOI: 10.1248/yakushi.22-00113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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30
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Pitchai R, Dappuri B, Pramila PV, Vidhyalakshmi M, Shanthi S, Alonazi WB, Almutairi KMA, Sundaram RS, Beyene I. An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5489084. [PMID: 36275965 PMCID: PMC9581610 DOI: 10.1155/2022/5489084] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 08/10/2022] [Accepted: 09/15/2022] [Indexed: 11/18/2022]
Abstract
Stroke-related disabilities can have a major negative effect on the economic well-being of the person. When left untreated, a stroke can be fatal. According to the findings of this study, people who have had strokes generally have abnormal biosignals. Patients will be able to obtain prompt therapy in this manner if they are carefully monitored; their biosignals will be precisely assessed and real-time analysis will be performed. On the contrary, most stroke diagnosis and prediction systems rely on image analysis technologies such as CT or MRI, which are not only expensive but also hard to use. In this study, we develop a machine learning algorithm for the prediction of stroke in the brain, and this prediction is carried out from the real-time samples of electromyography (EMG) data. The study uses synthetic samples for training the support vector machine (SVM) classifier and then the testing is conducted in real-time samples. To improve the accuracy of prediction, the samples are generated using the data augmentation principle, which supports training with vast data. The simulation is conducted to test the efficacy of the model, and the results show that the proposed classifier achieves a higher rate of classification accuracy than the existing methods. Furthermore, it is seen that the rate of precision, recall, and f-measure is higher in the proposed SVM than in other methods.
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Affiliation(s)
- R. Pitchai
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur 502313, Telangana, India
| | - Bhasker Dappuri
- Department of Electronics and Communication Engineering, CMR Engineering College, Kandlakoya 501401, Telangana, India
| | - P. V. Pramila
- Department of Computer Science Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 600124, Tamil Nadu, India
| | - M. Vidhyalakshmi
- Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram, Chennai 600089, Tamilnadu, India
| | - S. Shanthi
- Department of Computer Science and Engineering, Kongu Engineering College, Perundurai 638060, Tamil Nadu, India
| | - Wadi B. Alonazi
- Health Administration Department, College of Business Administration, King Saud University, P. O Box: 71115, Riyadh 11587, Saudi Arabia
| | - Khalid M. A. Almutairi
- Department of Community Health Sciences, College of Applied Medical Sciences, King Saud University, P. O. Box 10219, Riyadh 11433, Saudi Arabia
| | - R. S. Sundaram
- Department of Health Sciences, University of Texas, TX, USA
| | - Ibsa Beyene
- Department of IT, Mettu University, Mettu, Ethiopia
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31
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Shumba AT, Montanaro T, Sergi I, Fachechi L, De Vittorio M, Patrono L. Leveraging IoT-Aware Technologies and AI Techniques for Real-Time Critical Healthcare Applications. SENSORS (BASEL, SWITZERLAND) 2022; 22:7675. [PMID: 36236773 PMCID: PMC9571691 DOI: 10.3390/s22197675] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 10/04/2022] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
Personalised healthcare has seen significant improvements due to the introduction of health monitoring technologies that allow wearable devices to unintrusively monitor physiological parameters such as heart health, blood pressure, sleep patterns, and blood glucose levels, among others. Additionally, utilising advanced sensing technologies based on flexible and innovative biocompatible materials in wearable devices allows high accuracy and precision measurement of biological signals. Furthermore, applying real-time Machine Learning algorithms to highly accurate physiological parameters allows precise identification of unusual patterns in the data to provide health event predictions and warnings for timely intervention. However, in the predominantly adopted architectures, health event predictions based on Machine Learning are typically obtained by leveraging Cloud infrastructures characterised by shortcomings such as delayed response times and privacy issues. Fortunately, recent works highlight that a new paradigm based on Edge Computing technologies and on-device Artificial Intelligence significantly improve the latency and privacy issues. Applying this new paradigm to personalised healthcare architectures can significantly improve their efficiency and efficacy. Therefore, this paper reviews existing IoT healthcare architectures that utilise wearable devices and subsequently presents a scalable and modular system architecture to leverage emerging technologies to solve identified shortcomings. The defined architecture includes ultrathin, skin-compatible, flexible, high precision piezoelectric sensors, low-cost communication technologies, on-device intelligence, Edge Intelligence, and Edge Computing technologies. To provide development guidelines and define a consistent reference architecture for improved scalable wearable IoT-based critical healthcare architectures, this manuscript outlines the essential functional and non-functional requirements based on deductions from existing architectures and emerging technology trends. The presented system architecture can be applied to many scenarios, including ambient assisted living, where continuous surveillance and issuance of timely warnings can afford independence to the elderly and chronically ill. We conclude that the distribution and modularity of architecture layers, local AI-based elaboration, and data packaging consistency are the more essential functional requirements for critical healthcare application use cases. We also identify fast response time, utility, comfort, and low cost as the essential non-functional requirements for the defined system architecture.
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Affiliation(s)
- Angela-Tafadzwa Shumba
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, 73010 Lecce, Italy
| | - Teodoro Montanaro
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy
| | - Ilaria Sergi
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy
| | - Luca Fachechi
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, 73010 Lecce, Italy
| | - Massimo De Vittorio
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, 73010 Lecce, Italy
| | - Luigi Patrono
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy
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Huang Y, Upadhyay U, Dhar E, Kuo LJ, Syed-Abdul S. A Scoping Review to Assess Adherence to and Clinical Outcomes of Wearable Devices in the Cancer Population. Cancers (Basel) 2022; 14:cancers14184437. [PMID: 36139602 PMCID: PMC9496886 DOI: 10.3390/cancers14184437] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/06/2022] [Accepted: 09/08/2022] [Indexed: 11/20/2022] Open
Abstract
Simple Summary The use of wearable devices in clinical care is gaining popularity among cancer patients. The COVID-19 pandemic highlighted the value of wearable devices for monitoring health. Wearable devices are used to record and monitor real-time data like physical activity, sleep metrics, and heart rate variables. The use of wearable devices can directly impact clinical decision-making. There are few pieces of evidence that prove that wearable could improve the quality of patient care while reducing the cost of care, such as remote health monitoring. The generated big data by the wearable device is both a challenge and an opportunity. Researchers can apply artificial intelligence and machine learning techniques to improve wearable devices and their usage among cancer patients. In this scoping review, we assessed the adherence to clinical outcomes of wrist-worn wearable devices in the cancer population. Abstract The use of wearable devices (WDs) in healthcare monitoring and management has attracted increasing attention. A major problem is patients’ adherence and acceptance of WDs given that they are already experiencing a disease burden and treatment side effects. This scoping review explored the use of wrist-worn devices in the cancer population, with a special focus on adherence and clinical outcomes. Relevant articles focusing on the use of WDs in cancer care management were retrieved from PubMed, Scopus, and Embase from 1 January 2017 to 3 March 2022. Studies were independently screened and relevant information was extracted. We identified 752 studies, of which 38 met our inclusion criteria. Studies focused on mixed, breast, colorectal, lung, gastric, urothelial, skin, liver, and blood cancers. Adherence to WDs varied from 60% to 100%. The highest adherence was reported in the 12-week studies. Most studies focused on physical activity, sleep analysis, and heart vital signs. Of the 10 studies that described patient-reported outcomes using questionnaires and personal interviews, 8 indicated a positive correlation between the patient-reported and wearable outcomes. The definitions of the outcome measures and adherence varied across the studies. A better understanding of the intervention standards in terms of the clinical outcomes could improve adherence to wearables.
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Affiliation(s)
- Yaoru Huang
- Department of Radiation Oncology, Taipei Medical University Hospital, Taipei 110, Taiwan
- Graduate Institute of Biomedical Materials and Tissue Engineering, College of Biomedical Engineering, Taipei Medical University, Taipei 110, Taiwan
| | - Umashankar Upadhyay
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
| | - Eshita Dhar
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
| | - Li-Jen Kuo
- Division of Colorectal Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei 110, Taiwan
- Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
| | - Shabbir Syed-Abdul
- Graduate Institute of Biomedical Informatics, College of Medical Sciences and Technology, Taipei Medical University, Taipei 106, Taiwan
- International Center for Health Information Technology, College of Medical Science and Technology, Taipei Medical University, Taipei 106, Taiwan
- School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, Taipei 110, Taiwan
- Correspondence: ; Tel.: +886-2-6638-2736 (ext. 1514)
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Smart Consumer Wearables as Digital Diagnostic Tools: A Review. Diagnostics (Basel) 2022; 12:diagnostics12092110. [PMID: 36140511 PMCID: PMC9498278 DOI: 10.3390/diagnostics12092110] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022] Open
Abstract
The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.
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Wearable Technologies for Pediatric Patients with Surgical Infections—More than Counting Steps? BIOSENSORS 2022; 12:bios12080634. [PMID: 36005030 PMCID: PMC9405945 DOI: 10.3390/bios12080634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 07/30/2022] [Accepted: 08/09/2022] [Indexed: 12/04/2022]
Abstract
Reliable vital sign assessments are crucial for the management of patients with infectious diseases. Wearable devices enable easy and comfortable continuous monitoring across settings, especially in pediatric patients, but information about their performance in acutely unwell children is scarce. Vital signs were continuously measured with a multi-sensor wearable device (Everion®, Biofourmis, Zurich, Switzerland) in 21 pediatric patients during their hospitalization for appendicitis, osteomyelitis, or septic arthritis to describe acceptance and feasibility and to compare validity and reliability with conventional measurements. Using a wearable device was highly accepted and feasible for health-care workers, parents, and children. There were substantial data gaps in continuous monitoring up to 24 h. The wearable device measured heart rate and oxygen saturation reliably (mean difference, 2.5 bpm and 0.4% SpO2) but underestimated body temperature by 1.7 °C. Data availability was suboptimal during the study period, but a good relationship was determined between wearable device and conventional measurements for heart rate and oxygen saturation. Acceptance and feasibility were high in all study groups. We recommend that wearable devices designed for medical use in children be validated in the targeted population to assure future high-quality continuous vital sign assessments in an easy and non-burdening way.
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Battineni G, Chintalapudi N, Hossain MA, Losco G, Ruocco C, Sagaro GG, Traini E, Nittari G, Amenta F. Artificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review. Bioengineering (Basel) 2022; 9:bioengineering9080370. [PMID: 36004895 PMCID: PMC9405227 DOI: 10.3390/bioengineering9080370] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 07/25/2022] [Accepted: 08/02/2022] [Indexed: 11/16/2022] Open
Abstract
Background: The progressive aging of populations, primarily in the industrialized western world, is accompanied by the increased incidence of several non-transmittable diseases, including neurodegenerative diseases and adult-onset dementia disorders. To stimulate adequate interventions, including treatment and preventive measures, an early, accurate diagnosis is necessary. Conventional magnetic resonance imaging (MRI) represents a technique quite common for the diagnosis of neurological disorders. Increasing evidence indicates that the association of artificial intelligence (AI) approaches with MRI is particularly useful for improving the diagnostic accuracy of different dementia types. Objectives: In this work, we have systematically reviewed the characteristics of AI algorithms in the early detection of adult-onset dementia disorders, and also discussed its performance metrics. Methods: A document search was conducted with three databases, namely PubMed (Medline), Web of Science, and Scopus. The search was limited to the articles published after 2006 and in English only. The screening of the articles was performed using quality criteria based on the Newcastle–Ottawa Scale (NOS) rating. Only papers with an NOS score 7 were considered for further review. Results: The document search produced a count of 1876 articles and, because of duplication, 1195 papers were not considered. Multiple screenings were performed to assess quality criteria, which yielded 29 studies. All the selected articles were further grouped based on different attributes, including study type, type of AI model used in the identification of dementia, performance metrics, and data type. Conclusions: The most common adult-onset dementia disorders occurring were Alzheimer’s disease and vascular dementia. AI techniques associated with MRI resulted in increased diagnostic accuracy ranging from 73.3% to 99%. These findings suggest that AI should be associated with conventional MRI techniques to obtain a precise and early diagnosis of dementia disorders occurring in old age.
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Affiliation(s)
- Gopi Battineni
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
- Correspondence: ; Tel.: +39-3331728206
| | - Nalini Chintalapudi
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Mohammad Amran Hossain
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Giuseppe Losco
- School of Architecture and Design, University of Camerino, 63100 Ascoli Piceno, Italy
| | - Ciro Ruocco
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Getu Gamo Sagaro
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Enea Traini
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Giulio Nittari
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
| | - Francesco Amenta
- Clinical Research Centre, School of Medicinal and Health Products Sciences, University of Camerino, 62032 Camerino, Italy
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