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Wu Y, Xia S, Liang Z, Chen R, Qi S. Artificial intelligence in COPD CT images: identification, staging, and quantitation. Respir Res 2024; 25:319. [PMID: 39174978 PMCID: PMC11340084 DOI: 10.1186/s12931-024-02913-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 07/09/2024] [Indexed: 08/24/2024] Open
Abstract
Chronic obstructive pulmonary disease (COPD) stands as a significant global health challenge, with its intricate pathophysiological manifestations often demanding advanced diagnostic strategies. The recent applications of artificial intelligence (AI) within the realm of medical imaging, especially in computed tomography, present a promising avenue for transformative changes in COPD diagnosis and management. This review delves deep into the capabilities and advancements of AI, particularly focusing on machine learning and deep learning, and their applications in COPD identification, staging, and imaging phenotypes. Emphasis is laid on the AI-powered insights into emphysema, airway dynamics, and vascular structures. The challenges linked with data intricacies and the integration of AI in the clinical landscape are discussed. Lastly, the review casts a forward-looking perspective, highlighting emerging innovations in AI for COPD imaging and the potential of interdisciplinary collaborations, hinting at a future where AI doesn't just support but pioneers breakthroughs in COPD care. Through this review, we aim to provide a comprehensive understanding of the current state and future potential of AI in shaping the landscape of COPD diagnosis and management.
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Affiliation(s)
- Yanan Wu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Shuyue Xia
- Respiratory Department, Central Hospital Affiliated to Shenyang Medical College, Shenyang, China
- Key Laboratory of Medicine and Engineering for Chronic Obstructive Pulmonary Disease in Liaoning Province, Shenyang, China
| | - Zhenyu Liang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
| | - Rongchang Chen
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
- Shenzhen Institute of Respiratory Disease, Shenzhen People's Hospital, Shenzhen, China
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
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Akduman S, Yilmaz K. Examining the effectiveness of artificial intelligence applications in asthma and COPD outpatient support in terms of patient health and public cost: SWOT analysis. Medicine (Baltimore) 2024; 103:e38998. [PMID: 39029048 PMCID: PMC11398804 DOI: 10.1097/md.0000000000038998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/21/2024] Open
Abstract
This research aimed to examine the effectiveness of artificial intelligence applications in asthma and chronic obstructive pulmonary disease (COPD) outpatient treatment support in terms of patient health and public costs. The data obtained in the research using semiotic analysis, content analysis and trend analysis methods were analyzed with strengths, weakness, opportunities, threats (SWOT) analysis. In this context, 18 studies related to asthma, COPD and artificial intelligence were evaluated. The strengths of artificial intelligence applications in asthma and COPD outpatient treatment stand out as early diagnosis, access to more patients and reduced costs. The points that stand out among the weaknesses are the acceptance and use of technology and vulnerabilities related to artificial intelligence. Opportunities arise in developing differential diagnoses of asthma and COPD and in examining prognoses for the diseases more effectively. Malicious use, commercial data leaks and data security issues stand out among the threats. Although artificial intelligence applications provide great convenience in the outpatient treatment process for asthma and COPD diseases, precautions must be taken on a global scale and with the participation of international organizations against weaknesses and threats. In addition, there is an urgent need for accreditation for the practices to be carried out in this regard.
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Affiliation(s)
- Seha Akduman
- Department of Pulmonary Diseases, Yeditepe University, Faculty of Medicine, Istanbul, Türkiye
| | - Kadir Yilmaz
- Istanbul Commerce University, Social Sciences Institute, Industrial Policies and Technology Management Program (DR), Istanbul, Türkiye
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Kumar S, Bhagat V, Sahu P, Chaube MK, Behera AK, Guizani M, Gravina R, Di Dio M, Fortino G, Curry E, Alsamhi SH. A novel multimodal framework for early diagnosis and classification of COPD based on CT scan images and multivariate pulmonary respiratory diseases. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 243:107911. [PMID: 37981453 DOI: 10.1016/j.cmpb.2023.107911] [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: 08/10/2023] [Revised: 10/23/2023] [Accepted: 11/01/2023] [Indexed: 11/21/2023]
Abstract
BACKGROUND AND OBJECTIVE Chronic Obstructive Pulmonary Disease (COPD) is one of the world's worst diseases; its early diagnosis using existing methods like statistical machine learning techniques, medical diagnostic tools, conventional medical procedures, and other methods is challenging due to misclassification results of COPD diagnosis and takes a long time to perform accurate prediction. Due to the severe consequences of COPD, detection and accurate diagnosis of COPD at an early stage is essential. This paper aims to design and develop a multimodal framework for early diagnosis and accurate prediction of COPD patients based on prepared Computerized Tomography (CT) scan images and lung sound/cough (audio) samples using machine learning techniques, which are presented in this study. METHOD The proposed multimodal framework extracts texture, histogram intensity, chroma, Mel-Frequency Cepstral Coefficients (MFCCs), and Gaussian scale space from the prepared CT images and lung sound/cough samples. Accurate data from All India Institute Medical Sciences (AIIMS), Raipur, India, and the open respiratory CT images and lung sound/cough (audio) sample dataset validate the proposed framework. The discriminatory features are selected from the extracted feature sets using unsupervised ML techniques, and customized ensemble learning techniques are applied to perform early classification and assess the severity levels of COPD patients. RESULTS The proposed framework provided 97.50%, 98%, and 95.30% accuracy for early diagnosis of COPD patients based on the fusion technique, CT diagnostic model, and cough sample model. CONCLUSION Finally, we compare the performance of the proposed framework with existing methods, current approaches, and conventional benchmark techniques for early diagnosis.
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Affiliation(s)
- Santosh Kumar
- Department of Computer Science and Engineering, IIIT-Naya Raipur, Chhattisgarh, India.
| | - Vijesh Bhagat
- Department of Computer Science and Engineering, IIIT-Naya Raipur, Chhattisgarh, India.
| | - Prakash Sahu
- Department of Computer Science and Engineering, IIIT-Naya Raipur, Chhattisgarh, India.
| | | | - Ajoy Kumar Behera
- Department of Pulmonary Medicine & TB, All India Institute of Medical Sciences (AIIMS), Raipur, Chhattisgarh, India.
| | - Mohsen Guizani
- Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates.
| | - Raffaele Gravina
- Department of Informatics, Modeling, Electronic, and System Engineering, University of Calabria, 87036 Rende, Italy.
| | - Michele Di Dio
- Department of Informatics, Modeling, Electronic, and System Engineering, University of Calabria, 87036 Rende, Italy; Annunziata Hospital Cosenza, Italy.
| | - Giancarlo Fortino
- Department of Informatics, Modeling, Electronic, and System Engineering, University of Calabria, 87036 Rende, Italy.
| | - Edward Curry
- Insight Centre for Data Analytics, University of Galway, Galway, Ireland.
| | - Saeed Hamood Alsamhi
- Insight Centre for Data Analytics, University of Galway, Galway, Ireland; Faculty of Engineering, IBB University, Ibb, Yemen.
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Hussain A, Marlowe S, Ali M, Uy E, Bhopalwala H, Gullapalli D, Vangara A, Haroon M, Akbar A, Piercy J. A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders. Cureus 2024; 16:e51581. [PMID: 38313926 PMCID: PMC10836179 DOI: 10.7759/cureus.51581] [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] [Accepted: 01/02/2024] [Indexed: 02/06/2024] Open
Abstract
This systematic review examines the transformative impact of artificial intelligence (AI) in managing lung disorders through a comprehensive analysis of articles spanning 2014 to 2023. Evaluating AI's multifaceted roles in radiological imaging, disease burden prediction, detection, diagnosis, and molecular mechanisms, this review presents a critical synthesis of key insights from select articles. The findings underscore AI's significant strides in bolstering diagnostic accuracy, interpreting radiological imaging, predicting disease burdens, and deepening the understanding of tuberculosis (TB), chronic obstructive pulmonary disease (COPD), silicosis, pneumoconiosis, and lung fibrosis. The synthesis positions AI as a revolutionary tool within the healthcare system, offering vital implications for healthcare workers, policymakers, and researchers in comprehending and leveraging AI's pivotal role in lung disease management.
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Affiliation(s)
- Akbar Hussain
- Internal Medicine, Appalachian Regional Healthcare, Harlan, USA
| | - Stanley Marlowe
- Internal Medicine, Appalachian Regional Healthcare, Harlan, USA
| | - Muhammad Ali
- Pulmonary and Critical Care, Appalachian Regional Healthcare, Hazard, USA
| | - Edilfavia Uy
- Diabetes and Endocrinology, Appalachian Regional Healthcare, Whitesburg, USA
| | - Huzefa Bhopalwala
- Internal Medicine, Appalachian Regional Healthcare, Whitesburg, USA
- Cardiovascular, Mayo Clinic, Rochester, USA
| | | | - Avinash Vangara
- Internal Medicine, Appalachian Regional Healthcare, Harlan, USA
| | - Moeez Haroon
- Internal Medicine, Appalachian Regional Healthcare, Harlan, USA
| | - Aelia Akbar
- Public Health, Appalachian Regional Healthcare, Harlan, USA
| | - Jonathan Piercy
- Internal Medicine, Appalachian Regional Healthcare, Whitesburg, USA
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Antão J, de Mast J, Marques A, Franssen FME, Spruit MA, Deng Q. Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases. Expert Rev Respir Med 2023; 17:1207-1219. [PMID: 38270524 DOI: 10.1080/17476348.2024.2302940] [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: 07/13/2023] [Accepted: 01/04/2024] [Indexed: 01/26/2024]
Abstract
INTRODUCTION Asthma and chronic obstructive pulmonary disease (COPD) are leading causes of morbidity and mortality worldwide. Despite all available diagnostics and treatments, these conditions pose a significant individual, economic and social burden. Artificial intelligence (AI) promises to support clinical decision-making processes by optimizing diagnosis and treatment strategies of these heterogeneous and complex chronic respiratory diseases. Its capabilities extend to predicting exacerbation risk, disease progression and mortality, providing healthcare professionals with valuable insights for more effective care. Nevertheless, the knowledge gap between respiratory clinicians and data scientists remains a major constraint for wide application of AI and may hinder future progress. This narrative review aims to bridge this gap and encourage AI deployment by explaining its methodology and added value in asthma and COPD diagnosis and treatment. AREAS COVERED This review offers an overview of the fundamental concepts of AI and machine learning, outlines the key steps in building a model, provides examples of their applicability in asthma and COPD care, and discusses barriers to their implementation. EXPERT OPINION Machine learning can advance our understanding of asthma and COPD, enabling personalized therapy and better outcomes. Further research and validation are needed to ensure the development of clinically meaningful and generalizable models.
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Affiliation(s)
- Joana Antão
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Jeroen de Mast
- Economics and Business, University of Amsterdam, Amsterdam, The Netherlands
| | - Alda Marques
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Frits M E Franssen
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Martijn A Spruit
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Qichen Deng
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
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Pereira J, Antunes N, Rosa J, Ferreira JC, Mogo S, Pereira M. Intelligent Clinical Decision Support System for Managing COPD Patients. J Pers Med 2023; 13:1359. [PMID: 37763127 PMCID: PMC10532899 DOI: 10.3390/jpm13091359] [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: 08/10/2023] [Revised: 08/30/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Health remote monitoring systems (HRMSs) play a crucial role in managing COPD patients by identifying anomalies in their biometric signs and alerting healthcare professionals. By analyzing the relationships between biometric signs and environmental factors, it is possible to develop artificial intelligence models that are capable of inferring patients' future health deterioration risks. In this research work, we review recent works in this area and develop an intelligent clinical decision support system (CIDSS) that is capable of providing early information concerning patient health evolution and risk analysis in order to support the treatment of COPD patients. The present work's CIDSS is composed of two main modules: the vital signs prediction module and the early warning score calculation module, which generate the patient health information and deterioration risks, respectively. Additionally, the CIDSS generates alerts whenever a biometric sign measurement falls outside the allowed range for a patient or in case a basal value changes significantly. Finally, the system was implemented and assessed in a real case and validated in clinical terms through an evaluation survey answered by healthcare professionals involved in the project. In conclusion, the CIDSS proves to be a useful and valuable tool for medical and healthcare professionals, enabling proactive intervention and facilitating adjustments to the medical treatment of patients.
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Affiliation(s)
- José Pereira
- INOV Inesc Inovação—Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal; (J.P.); (N.A.); (J.R.)
- Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR (Information Sciences, Technologies and Architecture Research Center), 1649-026 Lisboa, Portugal
| | - Nuno Antunes
- INOV Inesc Inovação—Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal; (J.P.); (N.A.); (J.R.)
| | - Joana Rosa
- INOV Inesc Inovação—Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal; (J.P.); (N.A.); (J.R.)
| | - João C. Ferreira
- INOV Inesc Inovação—Instituto de Novas Tecnologias, 1000-029 Lisbon, Portugal; (J.P.); (N.A.); (J.R.)
- Instituto Universitário de Lisboa (ISCTE-IUL), ISTAR (Information Sciences, Technologies and Architecture Research Center), 1649-026 Lisboa, Portugal
- Logistics, Molde University College, NO-6410 Molde, Norway
| | - Sandra Mogo
- Departamento de Física, Universidade da Beira Interior, 6201-001 Covilhã, Portugal;
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Siebert JN, Hartley MA, Courvoisier DS, Salamin M, Robotham L, Doenz J, Barazzone-Argiroffo C, Gervaix A, Bridevaux PO. Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study. BMC Pulm Med 2023; 23:191. [PMID: 37264374 DOI: 10.1186/s12890-022-02255-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 11/20/2022] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Interstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach. METHODS A total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls. DISCUSSION This approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity. Trial registration Registration: August 8, 2022. CLINICALTRIALS gov Identifier: NCT05318599.
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Affiliation(s)
- Johan N Siebert
- Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, 47 Avenue de la Roseraie, 1211, Geneva 14, Switzerland.
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.
| | - Mary-Anne Hartley
- Machine Learning and Optimization (MLO) Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Delphine S Courvoisier
- Quality of Care Unit, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Marlène Salamin
- Division of Pulmonology, Hospital of Valais, Sion, Switzerland
| | - Laura Robotham
- Division of Pulmonology, Hospital of Valais, Sion, Switzerland
| | - Jonathan Doenz
- Machine Learning and Optimization (MLO) Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Constance Barazzone-Argiroffo
- Division of Paediatric Pulmonology, Department of Women, Child and Adolescent, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Alain Gervaix
- Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, 47 Avenue de la Roseraie, 1211, Geneva 14, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
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Jonathan J, Barakabitze AA. ML technologies for diagnosing and treatment of tuberculosis: a survey. HEALTH AND TECHNOLOGY 2023. [DOI: 10.1007/s12553-023-00727-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
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Phan DT, Nguyen CH, Nguyen TDP, Tran LH, Park S, Choi J, Lee BI, Oh J. A Flexible, Wearable, and Wireless Biosensor Patch with Internet of Medical Things Applications. BIOSENSORS 2022; 12:bios12030139. [PMID: 35323409 PMCID: PMC8945966 DOI: 10.3390/bios12030139] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 02/19/2022] [Accepted: 02/20/2022] [Indexed: 05/05/2023]
Abstract
Monitoring the vital signs and physiological responses of the human body in daily activities is particularly useful for the early diagnosis and prevention of cardiovascular diseases. Here, we proposed a wireless and flexible biosensor patch for continuous and longitudinal monitoring of different physiological signals, including body temperature, blood pressure (BP), and electrocardiography. Moreover, these modalities for tracking body movement and GPS locations for emergency rescue have been included in biosensor devices. We optimized the flexible patch design with high mechanical stretchability and compatibility that can provide reliable and long-term attachment to the curved skin surface. Regarding smart healthcare applications, this research presents an Internet of Things-connected healthcare platform consisting of a smartphone application, website service, database server, and mobile gateway. The IoT platform has the potential to reduce the demand for medical resources and enhance the quality of healthcare services. To further address the advances in non-invasive continuous BP monitoring, an optimized deep learning architecture with one-channel electrocardiogram signals is introduced. The performance of the BP estimation model was verified using an independent dataset; this experimental result satisfied the Association for the Advancement of Medical Instrumentation, and the British Hypertension Society standards for BP monitoring devices. The experimental results demonstrated the practical application of the wireless and flexible biosensor patch for continuous physiological signal monitoring with Internet of Medical Things-connected healthcare applications.
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Affiliation(s)
- Duc Tri Phan
- Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Korea; (D.T.P.); (C.H.N.); (T.D.P.N.); (L.H.T.); (S.P.); (J.C.)
- BK21 FOUR ‘New-Senior’ Oriented Smart Health Care Education, Pukyong National University, Busan 48513, Korea
| | - Cong Hoan Nguyen
- Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Korea; (D.T.P.); (C.H.N.); (T.D.P.N.); (L.H.T.); (S.P.); (J.C.)
- BK21 FOUR ‘New-Senior’ Oriented Smart Health Care Education, Pukyong National University, Busan 48513, Korea
| | - Thuy Dung Pham Nguyen
- Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Korea; (D.T.P.); (C.H.N.); (T.D.P.N.); (L.H.T.); (S.P.); (J.C.)
- BK21 FOUR ‘New-Senior’ Oriented Smart Health Care Education, Pukyong National University, Busan 48513, Korea
| | - Le Hai Tran
- Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Korea; (D.T.P.); (C.H.N.); (T.D.P.N.); (L.H.T.); (S.P.); (J.C.)
- BK21 FOUR ‘New-Senior’ Oriented Smart Health Care Education, Pukyong National University, Busan 48513, Korea
| | - Sumin Park
- Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Korea; (D.T.P.); (C.H.N.); (T.D.P.N.); (L.H.T.); (S.P.); (J.C.)
- BK21 FOUR ‘New-Senior’ Oriented Smart Health Care Education, Pukyong National University, Busan 48513, Korea
| | - Jaeyeop Choi
- Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Korea; (D.T.P.); (C.H.N.); (T.D.P.N.); (L.H.T.); (S.P.); (J.C.)
- BK21 FOUR ‘New-Senior’ Oriented Smart Health Care Education, Pukyong National University, Busan 48513, Korea
| | - Byeong-il Lee
- Department of Smart Healthcare, Pukyong National University, Busan 48513, Korea
- Correspondence: (B.-i.L.); (J.O.)
| | - Junghwan Oh
- Industry 4.0 Convergence Bionics Engineering, Pukyong National University, Busan 48513, Korea; (D.T.P.); (C.H.N.); (T.D.P.N.); (L.H.T.); (S.P.); (J.C.)
- BK21 FOUR ‘New-Senior’ Oriented Smart Health Care Education, Pukyong National University, Busan 48513, Korea
- Biomedical Engineering, Pukyong National University, Busan 48513, Korea
- Ohlabs Corporation, Busan 48513, Korea
- Correspondence: (B.-i.L.); (J.O.)
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