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Wieczorek K, Ananth S, Valazquez-Pimentel D. Acoustic biomarkers in asthma: a systematic review. J Asthma 2024:1-16. [PMID: 38634718 DOI: 10.1080/02770903.2024.2344156] [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: 01/18/2024] [Accepted: 04/13/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVE Current monitoring methods of asthma, such as peak expiratory flow testing, have important limitations. The emergence of automated acoustic sound analysis, capturing cough, wheeze, and inhaler use, offers a promising avenue for improving asthma diagnosis and monitoring. This systematic review evaluated the validity of acoustic biomarkers in supporting the diagnosis of asthma and its monitoring. DATA SOURCES A search was performed using two databases (PubMed and Embase) for all relevant studies published before November 2023. STUDY SELECTION 27 studies were included for analysis. Eligible studies focused on acoustic signals as digital biomarkers in asthma, utilizing recording devices to register or analyze sound. RESULTS Various respiratory acoustic signal types were analyzed, with cough and wheeze being predominant. Data collection methods included smartphones, custom sensors and digital stethoscopes. Across all studies, automated acoustic algorithms achieved average accuracy of cough and wheeze detection of 88.7% (range: 61.0 - 100.0%) with a median of 92.0%. The sensitivity of sound detection ranged from 54.0 to 100.0%, with a median of 90.3%; specificity ranged from 67.0 to 99.7%, with a median of 95.0%. Moreover, 70.4% (19/27) studies had a risk of bias identified. CONCLUSIONS This systematic review establishes the promising role of acoustic biomarkers, particularly cough and wheeze, in supporting the diagnosis of asthma and monitoring. The evidence suggests the potential for clinical integration of acoustic biomarkers, emphasizing the need for further validation in larger, clinically-diverse populations.
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Affiliation(s)
| | - Sachin Ananth
- London North West University Healthcare Trust, London, UK
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2
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Santos-Silva C, Ferreira-Cardoso H, Silva S, Vieira-Marques P, Valente JC, Almeida R, A Fonseca J, Santos C, Azevedo I, Jácome C. Feasibility and Acceptability of Pediatric Smartphone Lung Auscultation by Parents: Cross-Sectional Study. JMIR Pediatr Parent 2024; 7:e52540. [PMID: 38602309 PMCID: PMC11024396 DOI: 10.2196/52540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/23/2023] [Accepted: 01/02/2024] [Indexed: 04/12/2024] Open
Abstract
Background The use of a smartphone built-in microphone for auscultation is a feasible alternative to the use of a stethoscope, when applied by physicians. Objective This cross-sectional study aims to assess the feasibility of this technology when used by parents-the real intended end users. Methods Physicians recruited 46 children (male: n=33, 72%; age: mean 11.3, SD 3.1 y; children with asthma: n=24, 52%) during medical visits in a pediatric department of a tertiary hospital. Smartphone auscultation using an app was performed at 4 locations (trachea, right anterior chest, and right and left lung bases), first by a physician (recordings: n=297) and later by a parent (recordings: n=344). All recordings (N=641) were classified by 3 annotators for quality and the presence of adventitious sounds. Parents completed a questionnaire to provide feedback on the app, using a Likert scale ranging from 1 ("totally disagree") to 5 ("totally agree"). Results Most recordings had quality (physicians' recordings: 253/297, 85.2%; parents' recordings: 266/346, 76.9%). The proportions of physicians' recordings (34/253, 13.4%) and parents' recordings (31/266, 11.7%) with adventitious sounds were similar. Parents found the app easy to use (questionnaire: median 5, IQR 5-5) and were willing to use it (questionnaire: median 5, IQR 5-5). Conclusions Our results show that smartphone auscultation is feasible when performed by parents in the clinical context, but further investigation is needed to test its feasibility in real life.
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Affiliation(s)
| | | | - Sónia Silva
- Department of Pediatrics, Centro Hospitalar Universitário de São João, Porto, Portugal
| | - Pedro Vieira-Marques
- CINTESIS - Center for Health Technology and Services Research, Faculty of Medicine, Universidade do Porto, Porto, Portugal
| | - José Carlos Valente
- MEDIDA – Serviços em Medicina, Educação, Investigação, Desenvolvimento e Avaliação, Porto, Portugal
| | - Rute Almeida
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
| | - João A Fonseca
- MEDIDA – Serviços em Medicina, Educação, Investigação, Desenvolvimento e Avaliação, Porto, Portugal
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
| | - Cristina Santos
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
| | - Inês Azevedo
- Department of Pediatrics, Centro Hospitalar Universitário de São João, Porto, Portugal
- Department of Obstetrics, Gynecology and Pediatrics, Faculty of Medicine, Universidade do Porto, Porto, Portugal
- EpiUnit, Institute of Public Health, Universidade do Porto, Porto, Portugal
| | - Cristina Jácome
- CINTESIS@RISE, Department of Community Medicine, Information and Health Decision Sciences (MEDCIDS), Faculty of Medicine, University of Porto, Porto, Portugal
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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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Bhatt SP, Agusti A, Bafadhel M, Christenson SA, Bon J, Donaldson GC, Sin DD, Wedzicha JA, Martinez FJ. Phenotypes, Etiotypes, and Endotypes of Exacerbations of Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med 2023; 208:1026-1041. [PMID: 37560988 PMCID: PMC10867924 DOI: 10.1164/rccm.202209-1748so] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 08/04/2023] [Indexed: 08/11/2023] Open
Abstract
Chronic obstructive pulmonary disease is a major health problem with a high prevalence, a rising incidence, and substantial morbidity and mortality. Its course is punctuated by acute episodes of increased respiratory symptoms, termed exacerbations of chronic obstructive pulmonary disease (ECOPD). ECOPD are important events in the natural history of the disease, as they are associated with lung function decline and prolonged negative effects on quality of life. The present-day therapy for ECOPD with short courses of antibiotics and steroids and escalation of bronchodilators has resulted in only modest improvements in outcomes. Recent data indicate that ECOPD are heterogeneous, raising the need to identify distinct etioendophenotypes, incorporating traits of the acute event and of patients who experience recurrent events, to develop novel and targeted therapies. These characterizations can provide a complete clinical picture, the severity of which will dictate acute pharmacological treatment, and may also indicate whether a change in maintenance therapy is needed to reduce the risk of future exacerbations. In this review we discuss the latest knowledge of ECOPD types on the basis of clinical presentation, etiology, natural history, frequency, severity, and biomarkers in an attempt to characterize these events.
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Affiliation(s)
- Surya P. Bhatt
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Alvar Agusti
- Institut Respiratori (Clinic Barcelona), Càtedra Salut Respiratoria (Universitat de Barcelona), Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS-Barcelona), Centro de Investigación en Red de Enfermedades Respiratorias (CIBERES), España
| | - Mona Bafadhel
- Faculty of Life Sciences and Medicine, School of Immunology and Microbial Sciences, King’s College London, London, United Kingdom
| | - Stephanie A. Christenson
- Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, University of California, San Francisco, San Francisco, California
| | - Jessica Bon
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
- VA Pittsburgh Healthcare System, Pittsburgh, Pennsylvania
| | - Gavin C. Donaldson
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Don D. Sin
- Centre for Heart Lung Innovation and
- Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
- St. Paul’s Hospital, Vancouver, British Columbia, Canada; and
| | - Jadwiga A. Wedzicha
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
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5
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Xu S, Deo RC, Soar J, Barua PD, Faust O, Homaira N, Jaffe A, Kabir AL, Acharya UR. Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107746. [PMID: 37660550 DOI: 10.1016/j.cmpb.2023.107746] [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: 04/02/2023] [Revised: 07/07/2023] [Accepted: 08/02/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality. METHODS We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance. RESULTS We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent. CONCLUSIONS Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.
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Affiliation(s)
- Shuting Xu
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia; Cogninet Australia, Sydney, NSW 2010, Australia
| | - Ravinesh C Deo
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia
| | - Jeffrey Soar
- School of Business, University of Southern Queensland, Australia
| | - Prabal Datta Barua
- Cogninet Australia, Sydney, NSW 2010, Australia; School of Business, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia; Australian International Institute of Higher Education, Sydney, NSW 2000, Australia; School of Science Technology, University of New England, Australia; School of Biosciences, Taylor's University, Malaysia; School of Computing, SRM Institute of Science and Technology, India; School of Science and Technology, Kumamoto University, Japan; Sydney School of Education and Social Work, University of Sydney, Australia.
| | - Oliver Faust
- School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, UK
| | - Nusrat Homaira
- School of Clinical Medicine, University of New South Wales, Australia; Sydney Children's Hospital, Sydney, Australia; James P. Grant School of Public Health, Dhaka, Bangladesh
| | - Adam Jaffe
- School of Clinical Medicine, University of New South Wales, Australia; Sydney Children's Hospital, Sydney, Australia
| | | | - U Rajendra Acharya
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia; School of Science and Technology, Kumamoto University, Japan
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6
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Hartman M, Mináriková J, Batalik L, Pepera G, Su JJ, Formiga MF, Cahalin L, Dosbaba F. Effects of Home-Based Training with Internet Telehealth Guidance in COPD Patients Entering Pulmonary Rehabilitation: A Systematic Review. Int J Chron Obstruct Pulmon Dis 2023; 18:2305-2319. [PMID: 37876660 PMCID: PMC10591652 DOI: 10.2147/copd.s425218] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 09/21/2023] [Indexed: 10/26/2023] Open
Abstract
Objective Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Telehealth rehabilitation may offer new opportunities in patient therapy. This systematic review aimed to evaluate the effects of internet-mediated telerehabilitation and compare them with the outcomes of conventional pulmonary rehabilitation in COPD patients. Methods Electronic databases PubMed, Prospero, Scopus, and Cochrane were searched for randomized controlled trials from January 2005 to December 2021. Two investigators reviewed studies for relevance and extracted study population, methods, and results data. Results Ten studies were eligible for systematic review from the initial selection (n = 1492). There was considerable heterogeneity in telerehabilitation approaches. Functional exercise capacity and quality of life were assessed in all studies. None of the results were inferior to conventional care. High adherence and high levels of safety were observed. Conclusion Telerehabilitation in COPD patients is a safe therapy approach that increases and maintains functional exercise capacity and quality of life, making it an equivalent option to conventional outpatient rehabilitation. However, there is currently a lack of a unified approach to the composition of therapy and the use of technology, which needs to be addressed in the future.
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Affiliation(s)
- Martin Hartman
- Department of Rehabilitation and Sports Medicine, 2nd Faculty of Medicine, Charles University, University Hospital Motol, Prague, Czech Republic
| | - Jitka Mináriková
- Department of Rehabilitation and Sports Medicine, 2nd Faculty of Medicine, Charles University, University Hospital Motol, Prague, Czech Republic
| | - Ladislav Batalik
- Department of Rehabilitation, University Hospital Brno, Brno, 62500, Czech Republic
- Department of Public Health, Faculty of Medicine, Masaryk University Brno, Brno, 62500, Czech Republic
| | - Garyfallia Pepera
- Clinical Exercise Physiology and Rehabilitation Research Laboratory, Physiotherapy Department, School of Health Sciences, University of Thessaly, Lamia, Greece
| | - Jing Jing Su
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China
| | - Magno F Formiga
- Universidade Federal do Ceará, Faculdade de Medicina, Departamento de Fisioterapia, Fortaleza, Ceará, Brazil
| | - Lawrence Cahalin
- Department of Physical Therapy, University of Miami Miller School of Medicine, Coral Gables, FL, USA
| | - Filip Dosbaba
- Department of Rehabilitation and Sports Medicine, 2nd Faculty of Medicine, Charles University, University Hospital Motol, Prague, Czech Republic
- Department of Rehabilitation, University Hospital Brno, Brno, 62500, Czech Republic
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Kim Y, Hyon Y, Woo SD, Lee S, Lee SI, Ha T, Chung C. Evolution of the Stethoscope: Advances with the Adoption of Machine Learning and Development of Wearable Devices. Tuberc Respir Dis (Seoul) 2023; 86:251-263. [PMID: 37592751 PMCID: PMC10555525 DOI: 10.4046/trd.2023.0065] [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: 05/09/2023] [Revised: 08/04/2023] [Accepted: 08/15/2023] [Indexed: 08/19/2023] Open
Abstract
The stethoscope has long been used for the examination of patients, but the importance of auscultation has declined due to its several limitations and the development of other diagnostic tools. However, auscultation is still recognized as a primary diagnostic device because it is non-invasive and provides valuable information in real-time. To supplement the limitations of existing stethoscopes, digital stethoscopes with machine learning (ML) algorithms have been developed. Thus, now we can record and share respiratory sounds and artificial intelligence (AI)-assisted auscultation using ML algorithms distinguishes the type of sounds. Recently, the demands for remote care and non-face-to-face treatment diseases requiring isolation such as coronavirus disease 2019 (COVID-19) infection increased. To address these problems, wireless and wearable stethoscopes are being developed with the advances in battery technology and integrated sensors. This review provides the history of the stethoscope and classification of respiratory sounds, describes ML algorithms, and introduces new auscultation methods based on AI-assisted analysis and wireless or wearable stethoscopes.
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Affiliation(s)
- Yoonjoo Kim
- Division of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Republic of Korea
| | - YunKyong Hyon
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of Korea
| | - Seong-Dae Woo
- Division of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Republic of Korea
| | - Sunju Lee
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of Korea
| | - Song-I Lee
- Division of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Republic of Korea
| | - Taeyoung Ha
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon, Republic of Korea
| | - Chaeuk Chung
- Division of Pulmonology, Department of Internal Medicine, Chungnam National University College of Medicine, Daejeon, Republic of Korea
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Jacobson PK, Lind L, Persson HL. Unleashing the Power of Very Small Data to Predict Acute Exacerbations of Chronic Obstructive Pulmonary Disease. Int J Chron Obstruct Pulmon Dis 2023; 18:1457-1473. [PMID: 37485052 PMCID: PMC10362872 DOI: 10.2147/copd.s412692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 06/20/2023] [Indexed: 07/25/2023] Open
Abstract
Introduction In this article, we explore to what extent it is possible to leverage on very small data to build machine learning (ML) models that predict acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Methods We build ML models using the small data collected during the eHealth Diary telemonitoring study between 2013 and 2017 in Sweden. This data refers to a group of multimorbid patients, namely 18 patients with chronic obstructive pulmonary disease (COPD) as the major reason behind previous hospitalisations. The telemonitoring was supervised by a specialised hospital-based home care (HBHC) unit, which also was responsible for the medical actions needed. Results We implement two different ML approaches, one based on time-dependent covariates and the other one based on time-independent covariates. We compare the first approach with standard COX Proportional Hazards (CPH). For the second one, we use different proportions of synthetic data to build models and then evaluate the best model against authentic data. Discussion To the best of our knowledge, the present ML study shows for the first time that the most important variable for an increased risk of future AECOPDs is "maintenance medication changes by HBHC". This finding is clinically relevant since a sub-optimal maintenance treatment, requiring medication changes, puts the patient in risk for future AECOPDs. Conclusion The experiments return useful insights about the use of small data for ML.
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Affiliation(s)
- Petra Kristina Jacobson
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Department of Respiratory Medicine in Linköping, Linköping University, Linköping, Sweden
| | - Leili Lind
- Department of Biomedical Engineering/Health Informatics, Linköping University, Linköping, Sweden
- Digital Systems Division, Unit Digital Health, RISE Research Institutes of Sweden, Linköping, Sweden
| | - Hans Lennart Persson
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
- Department of Respiratory Medicine in Linköping, Linköping University, Linköping, Sweden
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9
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Sethi AK, Muddaloor P, Anvekar P, Agarwal J, Mohan A, Singh M, Gopalakrishnan K, Yadav A, Adhikari A, Damani D, Kulkarni K, Aakre CA, Ryu AJ, Iyer VN, Arunachalam SP. Digital Pulmonology Practice with Phonopulmography Leveraging Artificial Intelligence: Future Perspectives Using Dual Microwave Acoustic Sensing and Imaging. SENSORS (BASEL, SWITZERLAND) 2023; 23:5514. [PMID: 37420680 DOI: 10.3390/s23125514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/01/2023] [Accepted: 06/05/2023] [Indexed: 07/09/2023]
Abstract
Respiratory disorders, being one of the leading causes of disability worldwide, account for constant evolution in management technologies, resulting in the incorporation of artificial intelligence (AI) in the recording and analysis of lung sounds to aid diagnosis in clinical pulmonology practice. Although lung sound auscultation is a common clinical practice, its use in diagnosis is limited due to its high variability and subjectivity. We review the origin of lung sounds, various auscultation and processing methods over the years and their clinical applications to understand the potential for a lung sound auscultation and analysis device. Respiratory sounds result from the intra-pulmonary collision of molecules contained in the air, leading to turbulent flow and subsequent sound production. These sounds have been recorded via an electronic stethoscope and analyzed using back-propagation neural networks, wavelet transform models, Gaussian mixture models and recently with machine learning and deep learning models with possible use in asthma, COVID-19, asbestosis and interstitial lung disease. The purpose of this review was to summarize lung sound physiology, recording technologies and diagnostics methods using AI for digital pulmonology practice. Future research and development in recording and analyzing respiratory sounds in real time could revolutionize clinical practice for both the patients and the healthcare personnel.
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Affiliation(s)
- Arshia K Sethi
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Pratyusha Muddaloor
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Joshika Agarwal
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Anmol Mohan
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | | | - Keerthy Gopalakrishnan
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Ashima Yadav
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Aakriti Adhikari
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Devanshi Damani
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Internal Medicine, Texas Tech University Health Science Center, El Paso, TX 79995, USA
| | - Kanchan Kulkarni
- INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, University of Bordeaux, U1045, F-33000 Bordeaux, France
- IHU Liryc, Heart Rhythm Disease Institute, Fondation Bordeaux Université, F-33600 Pessac, France
| | | | - Alexander J Ryu
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Vivek N Iyer
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Shivaram P Arunachalam
- GIH Artificial Intelligence Laboratory (GAIL), Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
- Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA
- Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
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Masoumian Hosseini M, Masoumian Hosseini ST, Qayumi K, Ahmady S, Koohestani HR. The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review. ARCHIVES OF ACADEMIC EMERGENCY MEDICINE 2023; 11:e38. [PMID: 37215232 PMCID: PMC10197918 DOI: 10.22037/aaem.v11i1.1974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Introduction Artificial Inteligence (AI) application in emergency medicine is subject to ethical and legal inconsistencies. The purposes of this study were to map the extent of AI applications in emergency medicine, to identify ethical issues related to the use of AI, and to propose an ethical framework for its use. Methods A comprehensive literature collection was compiled through electronic databases/internet search engines (PubMed, Web of Science Platform, MEDLINE, Scopus, Google Scholar/Academia, and ERIC) and reference lists. We considered studies published between 1 January 2014 and 6 October 2022. Articles that did not self-classify as studies of an AI intervention, those that were not relevant to Emergency Departments (EDs), and articles that did not report outcomes or evaluations were excluded. Descriptive and thematic analyses of data extracted from the included articles were conducted. Results A total of 137 out of the 2175 citations in the original database were eligible for full-text evaluation. Of these articles, 47 were included in the scoping review and considered for theme extraction. This review covers seven main areas of AI techniques in emergency medicine: Machine Learning (ML) Algorithms (10.64%), prehospital emergency management (12.76%), triage, patient acuity and disposition of patients (19.15%), disease and condition prediction (23.40%), emergency department management (17.03%), the future impact of AI on Emergency Medical Services (EMS) (8.51%), and ethical issues (8.51%). Conclusion There has been a rapid increase in AI research in emergency medicine in recent years. Several studies have demonstrated the potential of AI in diverse contexts, particularly when improving patient outcomes through predictive modelling. According to the synthesis of studies in our review, AI-based decision-making lacks transparency. This feature makes AI decision-making opaque.
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Affiliation(s)
| | | | - Karim Qayumi
- Centre of Excellence for Simulation Education and Innovation, Department of Surgery, University of British Columbia, Vancouver, BC, Canada
| | - Soleiman Ahmady
- Department of Medical Education, Virtual School of Medical Education & Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamid Reza Koohestani
- Department of Nursing, Social Determinants of Health Research Center, Saveh University of Medical Sciences, Saveh, Iran
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11
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Sinha K, Uddin Z, Kawsar H, Islam S, Deen M, Howlader M. Analyzing chronic disease biomarkers using electrochemical sensors and artificial neural networks. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2022.116861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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12
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Reliability and Validity of Computerized Adventitious Respiratory Sounds in People with Bronchiectasis. J Clin Med 2022; 11:jcm11247509. [PMID: 36556124 PMCID: PMC9787476 DOI: 10.3390/jcm11247509] [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: 11/17/2022] [Revised: 12/08/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
Background: Computerized adventitious respiratory sounds (ARS), such as crackles and wheezes, have been poorly explored in bronchiectasis, especially their measurement properties. This study aimed to test the reliability and validity of ARS in bronchiectasis. Methods: Respiratory sounds were recorded twice at 4 chest locations on 2 assessment sessions (7 days apart) in people with bronchiectasis and daily sputum expectoration. The total number of crackles, number of wheezes and wheeze occupation rate (%) were the parameters extracted. Results: 28 participants (9 men; 62 ± 12 y) were included. Total number of crackles and wheezes showed moderate within-day (ICC 0.87, 95% CI 0.74−0.94; ICC 0.86, 95% CI 0.71−0.93) and between-day reliability (ICC 0.70, 95% CI 0.43−0.86; ICC 0.78, 95% CI 0.56−0.90) considering all chest locations and both respiratory phases; wheeze occupation rate showed moderate within-day reliability (ICC 0.86, 95% CI 0.71−0.93), but poor between-day reliability (ICC 0.71, 95% CI 0.33−0.87). Bland−Altman plots revealed no systematic bias, but wide limits of agreement, particularly in the between-days analysis. All ARS parameters correlated moderately with the amount of daily sputum expectoration (r > 0.4; p < 0.05). No other significant correlations were observed. Conclusion: ARS presented moderate reliability and were correlated with the daily sputum expectoration in bronchiectasis. The use of sequential measurements may be an option to achieve greater accuracy when ARS are used to monitor or assess the effects of physiotherapy interventions in this population.
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13
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Park JH, Yoon J, Park I, Sim Y, Kim SJ, Won JY, Han K. A deep learning algorithm to quantify AVF stenosis and predict 6-month primary patency: a pilot study. Clin Kidney J 2022; 16:560-570. [PMID: 36865006 PMCID: PMC9972839 DOI: 10.1093/ckj/sfac254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Indexed: 12/12/2022] Open
Abstract
Background A deep convolutional neural network (DCNN) model that predicts the degree of arteriovenous fistula (AVF) stenosis and 6-month primary patency (PP) based on AVF shunt sounds was developed, and was compared with various machine learning (ML) models trained on patients' clinical data. Methods Forty dysfunctional AVF patients were recruited prospectively, and AVF shunt sounds were recorded before and after percutaneous transluminal angioplasty using a wireless stethoscope. The audio files were converted to melspectrograms to predict the degree of AVF stenosis and 6-month PP. The diagnostic performance of the melspectrogram-based DCNN model (ResNet50) was compared with that of other ML models [i.e. logistic regression (LR), decision tree (DT) and support vector machine (SVM)], as well as the DCNN model (ResNet50) trained on patients' clinical data. Results Melspectrograms qualitatively reflected the degree of AVF stenosis by exhibiting a greater amplitude at mid-to-high frequency in the systolic phase with a more severe degree of stenosis, corresponding to a high-pitched bruit. The proposed melspectrogram-based DCNN model successfully predicted the degree of AVF stenosis. In predicting the 6-month PP, the area under the receiver operating characteristic curve of the melspectrogram-based DCNN model (ResNet50) (≥0.870) outperformed that of various ML models based on clinical data (LR, 0.783; DT, 0.766; SVM, 0.733) and that of the spiral-matrix DCNN model (0.828). Conclusion The proposed melspectrogram-based DCNN model successfully predicted the degree of AVF stenosis and outperformed ML-based clinical models in predicting 6-month PP.
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Affiliation(s)
| | | | - Insun Park
- Department of Anesthesiology and Pain Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea
| | - Yongsik Sim
- Department of Radiology, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Soo Jin Kim
- Department of Surgery, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jong Yun Won
- Department of Radiology, Yonsei University College of Medicine, Seoul, Republic of Korea
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14
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Hawthorne G, Richardson M, Greening NJ, Esliger D, Briggs-Price S, Chaplin EJ, Clinch L, Steiner MC, Singh SJ, Orme MW. A proof of concept for continuous, non-invasive, free-living vital signs monitoring to predict readmission following an acute exacerbation of COPD: a prospective cohort study. Respir Res 2022; 23:102. [PMID: 35473718 PMCID: PMC9044843 DOI: 10.1186/s12931-022-02018-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 03/29/2022] [Indexed: 11/10/2022] Open
Abstract
Background The use of vital signs monitoring in the early recognition of an acute exacerbation of chronic obstructive pulmonary disease (AECOPD) post-hospital discharge is limited. This study investigated whether continuous vital signs monitoring could predict an AECOPD and readmission. Methods 35 people were recruited at discharge following hospitalisation for an AECOPD. Participants were asked to wear an Equivital LifeMonitor during waking hours for 6 weeks and to complete the Exacerbations of Chronic Pulmonary Disease Tool (EXACT), a 14-item symptom diary, daily. The Equivital LifeMonitor recorded respiratory rate (RR), heart rate (HR), skin temperature (ST) and physical activity (PA) every 15-s. An AECOPD was classified as mild (by EXACT score), moderate (prescribed oral steroids/antibiotics) or severe (hospitalisation). Results Over the 6-week period, 31 participants provided vital signs and symptom data and 14 participants experienced an exacerbation, of which, 11 had sufficient data to predict an AECOPD. HR and PA were associated with EXACT score (p < 0.001). Three days prior to an exacerbation, RR increased by mean ± SD 2.0 ± 0.2 breaths/min for seven out of 11 exacerbations and HR increased by 8.1 ± 0.7 bpm for nine of these 11 exacerbations. Conclusions Increased heart rate and reduced physical activity were associated with worsening symptoms. Even with high-resolution data, the variation in vital signs data remains a challenge for predicting AECOPDs. Respiratory rate and heart rate should be further explored as potential predictors of an impending AECOPD. Trial registration: ISRCTN registry; ISRCTN12855961. Registered 07 November 2018—Retrospectively registered, https://www.isrctn.com/ISRCTN12855961 Supplementary Information The online version contains supplementary material available at 10.1186/s12931-022-02018-5.
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Affiliation(s)
- Grace Hawthorne
- Centre for Exercise and Rehabilitation Science, NIHR Leicester Biomedical Research Centre-Respiratory, University Hospitals of Leicester NHS Trust, Glenfield Hospital, Groby Road, Leicester, LE3 9QP, UK.
| | - Matthew Richardson
- Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Neil J Greening
- Centre for Exercise and Rehabilitation Science, NIHR Leicester Biomedical Research Centre-Respiratory, University Hospitals of Leicester NHS Trust, Glenfield Hospital, Groby Road, Leicester, LE3 9QP, UK.,Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Dale Esliger
- School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK
| | - Samuel Briggs-Price
- Centre for Exercise and Rehabilitation Science, NIHR Leicester Biomedical Research Centre-Respiratory, University Hospitals of Leicester NHS Trust, Glenfield Hospital, Groby Road, Leicester, LE3 9QP, UK
| | - Emma J Chaplin
- Centre for Exercise and Rehabilitation Science, NIHR Leicester Biomedical Research Centre-Respiratory, University Hospitals of Leicester NHS Trust, Glenfield Hospital, Groby Road, Leicester, LE3 9QP, UK
| | - Lisa Clinch
- Centre for Exercise and Rehabilitation Science, NIHR Leicester Biomedical Research Centre-Respiratory, University Hospitals of Leicester NHS Trust, Glenfield Hospital, Groby Road, Leicester, LE3 9QP, UK
| | - Michael C Steiner
- Centre for Exercise and Rehabilitation Science, NIHR Leicester Biomedical Research Centre-Respiratory, University Hospitals of Leicester NHS Trust, Glenfield Hospital, Groby Road, Leicester, LE3 9QP, UK.,Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Sally J Singh
- Centre for Exercise and Rehabilitation Science, NIHR Leicester Biomedical Research Centre-Respiratory, University Hospitals of Leicester NHS Trust, Glenfield Hospital, Groby Road, Leicester, LE3 9QP, UK.,Department of Respiratory Sciences, University of Leicester, Leicester, UK
| | - Mark W Orme
- Centre for Exercise and Rehabilitation Science, NIHR Leicester Biomedical Research Centre-Respiratory, University Hospitals of Leicester NHS Trust, Glenfield Hospital, Groby Road, Leicester, LE3 9QP, UK.,Department of Respiratory Sciences, University of Leicester, Leicester, UK
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15
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Cook J, Umar M, Khalili F, Taebi A. Body Acoustics for the Non-Invasive Diagnosis of Medical Conditions. Bioengineering (Basel) 2022; 9:bioengineering9040149. [PMID: 35447708 PMCID: PMC9032059 DOI: 10.3390/bioengineering9040149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 03/27/2022] [Accepted: 03/30/2022] [Indexed: 11/16/2022] Open
Abstract
In the past few decades, many non-invasive monitoring methods have been developed based on body acoustics to investigate a wide range of medical conditions, including cardiovascular diseases, respiratory problems, nervous system disorders, and gastrointestinal tract diseases. Recent advances in sensing technologies and computational resources have given a further boost to the interest in the development of acoustic-based diagnostic solutions. In these methods, the acoustic signals are usually recorded by acoustic sensors, such as microphones and accelerometers, and are analyzed using various signal processing, machine learning, and computational methods. This paper reviews the advances in these areas to shed light on the state-of-the-art, evaluate the major challenges, and discuss future directions. This review suggests that rigorous data analysis and physiological understandings can eventually convert these acoustic-based research investigations into novel health monitoring and point-of-care solutions.
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Affiliation(s)
- Jadyn Cook
- Department of Agricultural and Biological Engineering, Mississippi State University, 130 Creelman Street, Starkville, MS 39762, USA;
| | - Muneebah Umar
- Department of Biological Sciences, Mississippi State University, 295 Lee Blvd, Starkville, MS 39762, USA;
| | - Fardin Khalili
- Department of Mechanical Engineering, Embry-Riddle Aeronautical University, 1 Aerospace Blvd, Daytona Beach, FL 32114, USA;
| | - Amirtahà Taebi
- Department of Agricultural and Biological Engineering, Mississippi State University, 130 Creelman Street, Starkville, MS 39762, USA;
- Correspondence: ; Tel.: +1-(662)-325-5987
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16
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Kim Y, Hyon Y, Lee S, Woo SD, Ha T, Chung C. The coming era of a new auscultation system for analyzing respiratory sounds. BMC Pulm Med 2022; 22:119. [PMID: 35361176 PMCID: PMC8969404 DOI: 10.1186/s12890-022-01896-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 03/20/2022] [Indexed: 01/28/2023] Open
Abstract
Auscultation with stethoscope has been an essential tool for diagnosing the patients with respiratory disease. Although auscultation is non-invasive, rapid, and inexpensive, it has intrinsic limitations such as inter-listener variability and subjectivity, and the examination must be performed face-to-face. Conventional stethoscope could not record the respiratory sounds, so it was impossible to share the sounds. Recent innovative digital stethoscopes have overcome the limitations and enabled clinicians to store and share the sounds for education and discussion. In particular, the recordable stethoscope made it possible to analyze breathing sounds using artificial intelligence, especially based on neural network. Deep learning-based analysis with an automatic feature extractor and convoluted neural network classifier has been applied for the accurate analysis of respiratory sounds. In addition, the current advances in battery technology, embedded processors with low power consumption, and integrated sensors make possible the development of wearable and wireless stethoscopes, which can help to examine patients living in areas of a shortage of doctors or those who need isolation. There are still challenges to overcome, such as the analysis of complex and mixed respiratory sounds and noise filtering, but continuous research and technological development will facilitate the transition to a new era of a wearable and smart stethoscope.
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Affiliation(s)
- Yoonjoo Kim
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Korea
| | - YunKyong Hyon
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, 70, Yuseong-daero 1689 beon-gil, Yuseong-gu, Daejeon, 34047, Republic of Korea
| | - Sunju Lee
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, 70, Yuseong-daero 1689 beon-gil, Yuseong-gu, Daejeon, 34047, Republic of Korea
| | - Seong-Dae Woo
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Korea
| | - Taeyoung Ha
- Division of Industrial Mathematics, National Institute for Mathematical Sciences, 70, Yuseong-daero 1689 beon-gil, Yuseong-gu, Daejeon, 34047, Republic of Korea.
| | - Chaeuk Chung
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon, 34134, Korea. .,Infection Control Convergence Research Center, Chungnam National University School of Medicine, Daejeon, 35015, Republic of Korea.
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17
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Classification of Pulmonary Crackle and Normal Lung Sound Using Spectrogram and Support Vector Machine. JOURNAL OF BIOMIMETICS BIOMATERIALS AND BIOMEDICAL ENGINEERING 2022. [DOI: 10.4028/p-tf63b7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Crackles is one of the types of adventitious lung sound heard in patients with interstitial pulmonary fibrosis or cystic fibrosis. Pulmonary crackles of discontinuous short duration appear on inspiration, expiration, or both. To differentiate these pulmonary crackles, the medical staff usually uses a manual method, called auscultation. Various methods were developed to recognize pulmonary crackles and distinguish them from normal pulmonary sounds to be applied in digital signal processing technology. This paper demonstrates a feature extraction method to classify pulmonary crackle and normal lung sounds using Support Vector Machine (SVM) method using several kernels by performing spectrograms of the pulmonary sound to generate the frequency profile. Spectrograms with various resolutions and 3-fold cross-validation were used to divide the training data and the test data in the testing process. The resulting accuracy ranges from 81.4% - 100%. More accuracy values of 100% are generated by a feature extraction in several SVM kernels using 256 points FFT with three variations of windowing parameters compared to 512 points, where the best accuracy of 100% was produced by STFT-SVM method. This method has a potential to be used in the classification of other biomedical signals. The advantages of that are that the number of features produced is the same as the N-point FFT used for any signal length, the flexibility in the STFT parameters changes, such as the type of window and the window's length. In this study, only the Keiser window was tested with specific parameters. Exploration with different window types with various parameters is fascinating to do in further research.
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18
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Castelyn G, Laranjo L, Schreier G, Gallego B. Predictive performance and impact of algorithms in remote monitoring of chronic conditions: A systematic review and meta-analysis. Int J Med Inform 2021; 156:104620. [PMID: 34700194 DOI: 10.1016/j.ijmedinf.2021.104620] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 09/27/2021] [Accepted: 10/09/2021] [Indexed: 12/28/2022]
Abstract
BACKGROUND The use of telehealth interventions, such as the remote monitoring of patient clinical data (e.g. blood pressure, blood glucose, heart rate, medication use), has been proposed as a strategy to better manage chronic conditions and to reduce the impact on patients and healthcare systems. The use of algorithms for data acquisition, analysis, transmission, communication and visualisation are now common in remote patient monitoring. However, their use and impact on chronic disease management has not been systematically investigated. OBJECTIVES To investigate the use, impact, and performance of remote monitoring algorithms across various types of chronic conditions. METHODS A literature search of MEDLINE complete, CINHAL complete, and EMBASE was performed using search terms relating to the concepts of remote monitoring, chronic conditions, and data processing algorithms. Comparable outcomes from studies describing the impact on process measures and clinical and patient-reported outcomes were pooled for a summary effect and meta-analyses. A comparison of studies reporting the predictive performance of algorithms was also conducted using the Youden Index. RESULTS A total of 89 articles were included in the review. There was no evidence of a positive impact on healthcare utilisation [OR 1.09 (0.90 to 1.31); P = .35] and mortality [OR 0.83 (0.63 to 1.10); P = .208], but there was a positive effect on generic health status [SDM 0.2912 (0.06 to 0.51); P = .010] and diabetes control [SDM -0.53 (-0.74 to -0.33); P < .001; I2 = 15.71] (with two of the three diabetes studies being identified as having a high risk of bias). While the majority of impact studies made use of heuristic threshold-based algorithms (n = 27,87%), most performance studies (n = 36, 62%) analysed non-sequential machine learning methods. There was considerable variance in the quality, sample size and performance amongst these studies. Overall, algorithms involved in diagnosis (n = 22, 47%) had superior performance to those involved in predicting a future event (n = 25, 53%). Detection of arrythmia and ischaemia utilising ECG data showed particularly promising results. CONCLUSION The performance of data processing algorithms for the diagnosis of a current condition, particularly those related to the detection of arrythmia and ischaemia, is promising. However, there appears to exist minimal testing in experimental studies, with only two included impact studies citing a performance study as support for the intervention algorithm used. Because of the disconnect between performance and impact studies, there is currently limited evidence of the effect of integrating advanced inference algorithms in remote monitoring interventions. If the field of remote patient monitoring is to progress, future impact studies should address this disconnect by evaluating high performance validated algorithms in robust clinical trials.
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Affiliation(s)
| | - Liliana Laranjo
- Westmead Applied Research Centre, Sydney Medical School, The University of Sydney, Sydney, Australia; NOVA National School of Public Health, Public Health Research Centre, Universidade NOVA de Lisboa, Lisbon, Portugal.
| | - Günter Schreier
- Digital Health Information Systems, Center for Health and Bioresources, AIT Austrian Institute of Technology GmbH, Graz, Austria.
| | - Blanca Gallego
- Centre for Big Data Research in Health (CBDRH), Faculty of Medicine & Health, University of New South Wales, Sydney, Australia.
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Wang Z, An J, Lin H, Zhou J, Liu F, Chen J, Duan H, Deng N. Pathway-Driven Coordinated Telehealth System for Management of Patients With Single or Multiple Chronic Diseases in China: System Development and Retrospective Study. JMIR Med Inform 2021; 9:e27228. [PMID: 33998999 PMCID: PMC8167615 DOI: 10.2196/27228] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 02/22/2021] [Accepted: 04/16/2021] [Indexed: 11/13/2022] Open
Abstract
Background Integrated care enhanced with information technology has emerged as a means to transform health services to meet the long-term care needs of patients with chronic diseases. However, the feasibility of applying integrated care to the emerging “three-manager” mode in China remains to be explored. Moreover, few studies have attempted to integrate multiple types of chronic diseases into a single system. Objective The aim of this study was to develop a coordinated telehealth system that addresses the existing challenges of the “three-manager” mode in China while supporting the management of single or multiple chronic diseases. Methods The system was designed based on a tailored integrated care model. The model was constructed at the individual scale, mainly focusing on specifying the involved roles and responsibilities through a universal care pathway. A custom ontology was developed to represent the knowledge contained in the model. The system consists of a service engine for data storage and decision support, as well as different forms of clients for care providers and patients. Currently, the system supports management of three single chronic diseases (hypertension, type 2 diabetes mellitus, and chronic obstructive pulmonary disease) and one type of multiple chronic conditions (hypertension with type 2 diabetes mellitus). A retrospective study was performed based on the long-term observational data extracted from the database to evaluate system usability, treatment effect, and quality of care. Results The retrospective analysis involved 6964 patients with chronic diseases and 249 care providers who have registered in our system since its deployment in 2015. A total of 519,598 self-monitoring records have been submitted by the patients. The engine could generate different types of records regularly based on the specific care pathway. Results of the comparison tests and causal inference showed that a part of patient outcomes improved after receiving management through the system, especially the systolic blood pressure of patients with hypertension (P<.001 in all comparison tests and an approximately 5 mmHg decrease after intervention via causal inference). A regional case study showed that the work efficiency of care providers differed among individuals. Conclusions Our system has potential to provide effective management support for single or multiple chronic conditions simultaneously. The tailored closed-loop care pathway was feasible and effective under the “three-manager” mode in China. One direction for future work is to introduce advanced artificial intelligence techniques to construct a more personalized care pathway.
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Affiliation(s)
- Zheyu Wang
- Ministry of Education Key Laboratory of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Jiye An
- Ministry of Education Key Laboratory of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Hui Lin
- Ministry of Education Key Laboratory of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Jiaqiang Zhou
- Sir Run-Run Shaw Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China
| | - Fang Liu
- General Hospital of Ningxia Medical University, Yinchuan, China
| | - Juan Chen
- General Hospital of Ningxia Medical University, Yinchuan, China
| | - Huilong Duan
- Ministry of Education Key Laboratory of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Ning Deng
- Ministry of Education Key Laboratory of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
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20
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Hussain A, Choi HE, Kim HJ, Aich S, Saqlain M, Kim HC. Forecast the Exacerbation in Patients of Chronic Obstructive Pulmonary Disease with Clinical Indicators Using Machine Learning Techniques. Diagnostics (Basel) 2021; 11:829. [PMID: 34064395 PMCID: PMC8147791 DOI: 10.3390/diagnostics11050829] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 04/26/2021] [Accepted: 05/01/2021] [Indexed: 12/26/2022] Open
Abstract
Preventing exacerbation and seeking to determine the severity of the disease during the hospitalization of chronic obstructive pulmonary disease (COPD) patients is a crucial global initiative for chronic obstructive lung disease (GOLD); this option is available only for stable-phase patients. Recently, the assessment and prediction techniques that are used have been determined to be inadequate for acute exacerbation of chronic obstructive pulmonary disease patients. To magnify the monitoring and treatment of acute exacerbation COPD patients, we need to rely on the AI system, because traditional methods take a long time for the prognosis of the disease. Machine-learning techniques have shown the capacity to be effectively used in crucial healthcare applications. In this paper, we propose a voting ensemble classifier with 24 features to identify the severity of chronic obstructive pulmonary disease patients. In our study, we applied five machine-learning classifiers, namely random forests (RF), support vector machine (SVM), gradient boosting machine (GBM), XGboost (XGB), and K-nearest neighbor (KNN). These classifiers were trained with a set of 24 features. After that, we combined their results with a soft voting ensemble (SVE) method. Consequently, we found performance measures with an accuracy of 91.0849%, a precision of 90.7725%, a recall of 91.3607%, an F-measure of 91.0656%, and an AUC score of 96.8656%, respectively. Our result shows that the SVE classifier with the proposed twenty-four features outperformed regular machine-learning-based methods for chronic obstructive pulmonary disease (COPD) patients. The SVE classifier helps respiratory physicians to estimate the severity of COPD patients in the early stage, consequently guiding the cure strategy and helps the prognosis of COPD patients.
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Affiliation(s)
- Ali Hussain
- Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Korea; (A.H.); (S.A.)
| | - Hee-Eun Choi
- Department of Physical Medicine and Rehabilitation, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Korea;
| | - Hyo-Jung Kim
- Department of Internal Medicine, Haeundae Paik Hospital, Inje University College of Medicine, Busan 48108, Korea;
| | - Satyabrata Aich
- Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Korea; (A.H.); (S.A.)
| | - Muhammad Saqlain
- Department of Computer Science & Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea;
| | - Hee-Cheol Kim
- College of AI Convergence/Institute of Digital Anti-Aging Healthcare/u-HARC, Inje University, Gimhae 50834, Korea
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21
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Jiang W, Chao Y, Wang X, Chen C, Zhou J, Song Y. Day-to-Day Variability of Parameters Recorded by Home Noninvasive Positive Pressure Ventilation for Detection of Severe Acute Exacerbations in COPD. Int J Chron Obstruct Pulmon Dis 2021; 16:727-737. [PMID: 33790549 PMCID: PMC7997417 DOI: 10.2147/copd.s299819] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 03/04/2021] [Indexed: 11/23/2022] Open
Abstract
Background Home noninvasive positive pressure ventilation (NPPV) can be considered not only as an evidence-based treatment for stable hypercapnic chronic obstructive pulmonary disease (COPD) patients, but also as a predictor for detecting severe acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Methods In this retrospective observational study, we collected clinical exacerbations information and daily NPPV-related data in a cohort of COPD patients with home NPPV for 6 months. Daily changes in NPPV-related parameters' variability prior to AECOPD were examined using two-way repeated measures ANOVA and individual abnormal values (>75th or <25th percentile of individual baseline parameters) were calculated during 7-day pre-AECOPD period. Multivariate logistic regression was used to identify the independent risk factors associated with AECOPD that then were incorporated into the nomogram. Results Between January 1, 2018, and January 1, 2020, a total of 102 patients were included and 31 (30.4%) participants experienced hospitalization (AECOPD group) within 6 months. Respiratory rate changed significantly from baseline at 1, 2 or 3 days prior to admission (p<0.001, respectively) in the AECOPD group. The number of days with abnormal values of daily usage, leaks, or tidal volume during the 7-day pre-AECOPD period in the AECOPD group was higher than in the stable group (p<0.001, respectively). On multivariate analysis, 7-day mean respiratory rate (OR 1.756, 95% CI 1.249-2.469), abnormal values of daily use (OR 1.918, 95% CI 1.253-2.934) and tidal volume (OR 2.081, 95% CI 1.380-3.140) within 7 days were independently associated with the risk of AECOPD. Incorporating these factors, the nomogram achieved good concordance indexes of 0.962. Conclusion Seven-day mean respiratory rate, abnormal values of daily usage, leaks, and tidal volume within the 7-day pre-AECOPD period may be biomarkers for detection of AECOPD.
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Affiliation(s)
- Weipeng Jiang
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China
| | - Yencheng Chao
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xiaoyue Wang
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cuicui Chen
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian Zhou
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China
| | - Yuanlin Song
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, People's Republic of China.,Department of Pulmonary Medicine, Shanghai Respiratory Research Institute, Shanghai, 200032, People's Republic of China.,National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200000, People's Republic of China.,Department of Pulmonary Medicine, Zhongshan Hospital, Qingpu Branch, Fudan University, Shanghai, 201700, People's Republic of China.,Department of Pulmonary Medicine, Jinshan Hospital of Fudan University, Shanghai, 201508, People's Republic of China
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22
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Battineni G, Sagaro GG, Chinatalapudi N, Amenta F. Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis. J Pers Med 2020; 10:jpm10020021. [PMID: 32244292 PMCID: PMC7354442 DOI: 10.3390/jpm10020021] [Citation(s) in RCA: 79] [Impact Index Per Article: 19.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 03/09/2020] [Accepted: 03/23/2020] [Indexed: 02/07/2023] Open
Abstract
This paper reviews applications of machine learning (ML) predictive models in the diagnosis of chronic diseases. Chronic diseases (CDs) are responsible for a major portion of global health costs. Patients who suffer from these diseases need lifelong treatment. Nowadays, predictive models are frequently applied in the diagnosis and forecasting of these diseases. In this study, we reviewed the state-of-the-art approaches that encompass ML models in the primary diagnosis of CD. This analysis covers 453 papers published between 2015 and 2019, and our document search was conducted from PubMed (Medline), and Cumulative Index to Nursing and Allied Health Literature (CINAHL) libraries. Ultimately, 22 studies were selected to present all modeling methods in a precise way that explains CD diagnosis and usage models of individual pathologies with associated strengths and limitations. Our outcomes suggest that there are no standard methods to determine the best approach in real-time clinical practice since each method has its advantages and disadvantages. Among the methods considered, support vector machines (SVM), logistic regression (LR), clustering were the most commonly used. These models are highly applicable in classification, and diagnosis of CD and are expected to become more important in medical practice in the near future.
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Affiliation(s)
- Gopi Battineni
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
- Correspondence: ; Tel.: +39-333-172-8206
| | - Getu Gamo Sagaro
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
| | - Nalini Chinatalapudi
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
| | - Francesco Amenta
- Center for Telemedicine and Tele pharmacy, School of Medicinal and Health Sciences Products, University of Camerino, Via Madonna Della carceri 9, 62032 Camerino, Italy; (G.G.S.); (N.C.); (F.A.)
- Research Department, International Medical Radio Center Foundation (C.I.R.M.), 00144 Roma, Italy
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23
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Joyashiki T, Wada C. Validation of a Body-Conducted Sound Sensor for Respiratory Sound Monitoring and a Comparison with Several Sensors. SENSORS 2020; 20:s20030942. [PMID: 32050716 PMCID: PMC7038963 DOI: 10.3390/s20030942] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 01/23/2020] [Accepted: 02/05/2020] [Indexed: 11/16/2022]
Abstract
The ideal respiratory sound sensor exhibits high sensitivity, wide-band frequency characteristics, and excellent anti-noise properties. We investigated the body-conducted sound sensor (BCS) and verified its usefulness in respiratory sound monitoring through comparison with an air-coupled microphone (ACM) and acceleration sensor (B & K: 8001). We conducted four experiments for comparison: (1) estimation by equivalent circuit model of sensors and measurement by a sensitivity evaluation system; (2) measurement of tissue-borne sensitivity-to-air-noise sensitivity ratio (SRTA); (3) respiratory sound measurement through a simulator; and (4) actual respiratory sound measurement using human subjects. For (1), the simulation and measured values of all the sensors showed good agreement; BCS demonstrated sensitivity ~10 dB higher than ACM and higher sensitivity in the high-frequency segments compared with 8001. In (2), BCS showed high SRTA in the 600–1000 and 1200–2000-Hz frequency segments. In (3), BCS detected wheezes in the high-frequency segments of the respiratory sound. Finally, in (4), the sensors showed similar characteristics and features in the high-frequency segments as the simulators, where typical breathing sound detection was possible. BCS displayed a higher sensitivity and anti-noise property in high-frequency segments compared with the other sensors and is a useful respiratory sound sensor.
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Affiliation(s)
- Takeshi Joyashiki
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2–4 Hibikino, Wakamatsu-ku, Kitakyushu 808−0196, Japan;
- Saiseikai Yahata General Hospital Department of Clinical Engineering, Harunomachi5-9-27, Yahatahigasi-ku, Kitakyusyu 805-0050, Japan
- Correspondence: ; Tel.: +81-93-695-6058
| | - Chikamune Wada
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, 2–4 Hibikino, Wakamatsu-ku, Kitakyushu 808−0196, Japan;
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24
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Mlodzinski E, Stone DJ, Celi LA. Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review. Pulm Ther 2020; 6:67-77. [PMID: 32048244 PMCID: PMC7229087 DOI: 10.1007/s41030-020-00110-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Indexed: 01/22/2023] Open
Abstract
Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes.
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Affiliation(s)
- Eric Mlodzinski
- Department of Internal Medicine, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA.
| | - David J Stone
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Departments of Anesthesiology and Neurosurgery, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA.,Center for Advanced Medical Analytics, University of Virginia School of Medicine, Charlottesville, VA, 22908, USA
| | - Leo A Celi
- MIT Critical Data, Laboratory for Computational Physiology, Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.,Division of Pulmonary Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, 02215, USA
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25
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Franssen FME, Alter P, Bar N, Benedikter BJ, Iurato S, Maier D, Maxheim M, Roessler FK, Spruit MA, Vogelmeier CF, Wouters EFM, Schmeck B. Personalized medicine for patients with COPD: where are we? Int J Chron Obstruct Pulmon Dis 2019; 14:1465-1484. [PMID: 31371934 PMCID: PMC6636434 DOI: 10.2147/copd.s175706] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2019] [Accepted: 06/05/2019] [Indexed: 12/19/2022] Open
Abstract
Chronic airflow limitation is the common denominator of patients with chronic obstructive pulmonary disease (COPD). However, it is not possible to predict morbidity and mortality of individual patients based on the degree of lung function impairment, nor does the degree of airflow limitation allow guidance regarding therapies. Over the last decades, understanding of the factors contributing to the heterogeneity of disease trajectories, clinical presentation, and response to existing therapies has greatly advanced. Indeed, diagnostic assessment and treatment algorithms for COPD have become more personalized. In addition to the pulmonary abnormalities and inhaler therapies, extra-pulmonary features and comorbidities have been studied and are considered essential components of comprehensive disease management, including lifestyle interventions. Despite these advances, predicting and/or modifying the course of the disease remains currently impossible, and selection of patients with a beneficial response to specific interventions is unsatisfactory. Consequently, non-response to pharmacologic and non-pharmacologic treatments is common, and many patients have refractory symptoms. Thus, there is an ongoing urgency for a more targeted and holistic management of the disease, incorporating the basic principles of P4 medicine (predictive, preventive, personalized, and participatory). This review describes the current status and unmet needs regarding personalized medicine for patients with COPD. Also, it proposes a systems medicine approach, integrating genetic, environmental, (micro)biological, and clinical factors in experimental and computational models in order to decipher the multilevel complexity of COPD. Ultimately, the acquired insights will enable the development of clinical decision support systems and advance personalized medicine for patients with COPD.
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Affiliation(s)
- Frits ME Franssen
- Department of Research and Education, CIRO, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht, The Netherlands
| | - Peter Alter
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Nadav Bar
- Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Birke J Benedikter
- Institute for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps-University Marburg, Member of the German Center for Lung Research (DZL), Marburg, Germany
- Department of Medical Microbiology, Maastricht University Medical Center (MUMC+), Maastricht, The Netherlands
| | | | | | - Michael Maxheim
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Fabienne K Roessler
- Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Martijn A Spruit
- Department of Research and Education, CIRO, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht, The Netherlands
- REVAL - Rehabilitation Research Center, BIOMED - Biomedical Research Institute, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium
| | - Claus F Vogelmeier
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Emiel FM Wouters
- Department of Research and Education, CIRO, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht, The Netherlands
| | - Bernd Schmeck
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps University of Marburg (UMR), Member of the German Center for Lung Research (DZL), Marburg, Germany
- Institute for Lung Research, Universities of Giessen and Marburg Lung Centre, Philipps-University Marburg, Member of the German Center for Lung Research (DZL), Marburg, Germany
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26
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Kruse C, Pesek B, Anderson M, Brennan K, Comfort H. Telemonitoring to Manage Chronic Obstructive Pulmonary Disease: Systematic Literature Review. JMIR Med Inform 2019; 7:e11496. [PMID: 30892276 PMCID: PMC6446156 DOI: 10.2196/11496] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2018] [Revised: 10/11/2018] [Accepted: 10/12/2018] [Indexed: 01/08/2023] Open
Abstract
Background Chronic obstructive pulmonary disease (COPD) is a leading cause of death throughout the world. Telemedicine has been utilized for many diseases and its prevalence is increasing in the United States. Telemonitoring of patients with COPD has the potential to help patients manage disease and predict exacerbations. Objective The objective of this review is to evaluate the effectiveness of telemonitoring to manage COPD. Researchers want to determine how telemonitoring has been used to observe COPD and we are hoping this will lead to more research in telemonitoring of this disease. Methods This review was conducted in accordance with the Assessment for Multiple Systematic Reviews (AMSTAR) and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Authors performed a systematic review of the PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) databases to obtain relevant articles. Articles were then accepted or rejected by group consensus. Each article was read and authors identified barriers and facilitators to effectiveness of telemonitoring of COPD. Results Results indicate that conflicting information exists for the effectiveness of telemonitoring of patients with COPD. Primarily, 13 out of 29 (45%) articles stated that patient outcomes were improved overall with telemonitoring, while 11 of 29 (38%) indicated no improvement. Authors identified the following facilitators: reduced need for in-person visits, better disease management, and bolstered patient-provider relationship. Important barriers included low-quality data, increased workload for providers, and cost. Conclusions The high variability between the articles and the ways they provided telemonitoring services created conflicting results from the literature review. Future research should emphasize standardization of telemonitoring services and predictability of exacerbations.
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Affiliation(s)
- Clemens Kruse
- School of Health Administration, Texas State University, San Marcos, TX, United States
| | - Brandon Pesek
- School of Health Administration, Texas State University, San Marcos, TX, United States
| | - Megan Anderson
- School of Health Administration, Texas State University, San Marcos, TX, United States
| | - Kacey Brennan
- School of Health Administration, Texas State University, San Marcos, TX, United States
| | - Hilary Comfort
- School of Health Administration, Texas State University, San Marcos, TX, United States
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27
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Tan MKH, Wong JKL, Bakrania K, Abdullahi Y, Harling L, Casula R, Rowlands AV, Athanasiou T, Jarral OA. Can activity monitors predict outcomes in patients with heart failure? A systematic review. EUROPEAN HEART JOURNAL. QUALITY OF CARE & CLINICAL OUTCOMES 2019; 5:11-21. [PMID: 30215706 DOI: 10.1093/ehjqcco/qcy038] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Accepted: 09/11/2018] [Indexed: 12/12/2022]
Abstract
Actigraphy is increasingly incorporated into clinical practice to monitor intervention effectiveness and patient health in congestive heart failure (CHF). We explored the prognostic impact of actigraphy-quantified physical activity (AQPA) on CHF outcomes. PubMed and Medline databases were systematically searched for cross-sectional studies, cohort studies or randomised controlled trials from January 2007 to December 2017. We included studies that used validated actigraphs to predict outcomes in adult HF patients. Study selection and data extraction were performed by two independent reviewers. A total of 17 studies (15 cohort, 1 cross-sectional, 1 randomised controlled trial) were included, reporting on 2,759 CHF patients (22-89 years, 27.7% female). Overall, AQPA showed a strong inverse relationship with mortality and predictive utility when combined with established risk scores, and prognostic roles in morbidity, predicting cognitive function, New York Heart Association functional class and intercurrent events (e.g. hospitalisation), but weak relationships with health-related quality of life scores. Studies lacked consensus regarding device choice, time points and thresholds of PA measurement, which rendered quantitative comparisons between studies difficult. AQPA has a strong prognostic role in CHF. Multiple sampling time points would allow calculation of AQPA changes for incorporation into risk models. Consensus is needed regarding device choice and AQPA thresholds, while data management strategies are required to fully utilise generated data. Big data and machine learning strategies will potentially yield better predictive value of AQPA in CHF patients.
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Affiliation(s)
- Matthew K H Tan
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Joanna K L Wong
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Kishan Bakrania
- Diabetes Research Centre, University of Leceister, Leicester General Hospital, Gwendolen Road, Leicester, UK.,NIHR Leicester Biomedical Research Centre, Gwendolen Road, Leicester, UK
| | - Yusuf Abdullahi
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Leanne Harling
- Diabetes Research Centre, University of Leceister, Leicester General Hospital, Gwendolen Road, Leicester, UK.,NIHR Leicester Biomedical Research Centre, Gwendolen Road, Leicester, UK.,Division of Health Sciences, Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health Research, University of South Australia, City East Campus, Adelaide SA, Australia
| | - Roberto Casula
- Diabetes Research Centre, University of Leceister, Leicester General Hospital, Gwendolen Road, Leicester, UK.,NIHR Leicester Biomedical Research Centre, Gwendolen Road, Leicester, UK.,Division of Health Sciences, Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health Research, University of South Australia, City East Campus, Adelaide SA, Australia
| | - Alex V Rowlands
- Diabetes Research Centre, University of Leceister, Leicester General Hospital, Gwendolen Road, Leicester, UK.,NIHR Leicester Biomedical Research Centre, Gwendolen Road, Leicester, UK.,Division of Health Sciences, Alliance for Research in Exercise, Nutrition and Activity (ARENA), Sansom Institute for Health Research, University of South Australia, City East Campus, Adelaide SA, Australia
| | - Thanos Athanasiou
- Department of Surgery and Cancer, Imperial College London, London, UK
| | - Omar A Jarral
- Department of Surgery and Cancer, Imperial College London, London, UK
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28
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Abstract
Recent developments in sensor technology and computational analysis methods enable new strategies to measure and interpret lung acoustic signals that originate internally, such as breathing or vocal sounds, or are externally introduced, such as in chest percussion or airway insonification. A better understanding of these sounds has resulted in a new instrumentation that allows for highly accurate as well as portable options for measurement in the hospital, in the clinic, and even at home. This review outlines the instrumentation for acoustic stimulation and measurement of the lungs. We first review the fundamentals of acoustic lung signals and the pathophysiology of the diseases that these signals are used to detect. Then, we focus on different methods of measuring and creating signals that have been used in recent research for pulmonary disease diagnosis. These new methods, combined with signal processing and modeling techniques, lead to a reduction in noise and allow improved feature extraction and signal classification. We conclude by presenting the results of human subject studies taking advantage of both the instrumentation and signal processing tools to accurately diagnose common lung diseases. This paper emphasizes the active areas of research within modern lung acoustics and encourages the standardization of future work in this field.
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29
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Stewart J, Sprivulis P, Dwivedi G. Artificial intelligence and machine learning in emergency medicine. Emerg Med Australas 2018; 30:870-874. [PMID: 30014578 DOI: 10.1111/1742-6723.13145] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Accepted: 06/21/2018] [Indexed: 01/01/2023]
Abstract
Interest in artificial intelligence (AI) research has grown rapidly over the past few years, in part thanks to the numerous successes of modern machine learning techniques such as deep learning, the availability of large datasets and improvements in computing power. AI is proving to be increasingly applicable to healthcare and there is a growing list of tasks where algorithms have matched or surpassed physician performance. Despite the successes there remain significant concerns and challenges surrounding algorithm opacity, trust and patient data security. Notwithstanding these challenges, AI technologies will likely become increasingly integrated into emergency medicine in the coming years. This perspective presents an overview of current AI research relevant to emergency medicine.
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Affiliation(s)
| | | | - Girish Dwivedi
- Royal Perth Hospital, Perth, Western Australia, Australia
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30
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Fernandez-Granero MA, Sanchez-Morillo D, Leon-Jimenez A. An artificial intelligence approach to early predict symptom-based exacerbations of COPD. BIOTECHNOL BIOTEC EQ 2018. [DOI: 10.1080/13102818.2018.1437568] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Affiliation(s)
| | - Daniel Sanchez-Morillo
- Biomedical Engineering and Telemedicine Lab, School of Engineering, University of Cádiz, Cádiz, Spain
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31
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MacQueen IT, Maggard-Gibbons M, Capra G, Raaen L, Ulloa JG, Shekelle PG, Miake-Lye I, Beroes JM, Hempel S. Recruiting Rural Healthcare Providers Today: a Systematic Review of Training Program Success and Determinants of Geographic Choices. J Gen Intern Med 2018; 33:191-199. [PMID: 29181791 PMCID: PMC5789104 DOI: 10.1007/s11606-017-4210-z] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 09/22/2017] [Accepted: 09/28/2017] [Indexed: 11/29/2022]
Abstract
BACKGROUND Rural areas have historically struggled with shortages of healthcare providers; however, advanced communication technologies have transformed rural healthcare, and practice in underserved areas has been recognized as a policy priority. This systematic review aims to assess reasons for current providers' geographic choices and the success of training programs aimed at increasing rural provider recruitment. METHODS This systematic review (PROSPERO: CRD42015025403) searched seven databases for published and gray literature on the current cohort of US rural healthcare practitioners (2005 to March 2017). Two reviewers independently screened citations for inclusion; one reviewer extracted data and assessed risk of bias, with a senior systematic reviewer checking the data; quality of evidence was assessed using the GRADE approach. RESULTS Of 7276 screened citations, we identified 31 studies exploring reasons for geographic choices and 24 studies documenting the impact of training programs. Growing up in a rural community is a key determinant and is consistently associated with choosing rural practice. Most existing studies assess physicians, and only a few are based on multivariate analyses that take competing and potentially correlated predictors into account. The success rate of placing providers-in-training in rural practice after graduation, on average, is 44% (range 20-84%; N = 31 programs). We did not identify program characteristics that are consistently associated with program success. Data are primarily based on rural tracks for medical residents. DISCUSSION The review provides insight into the relative importance of demographic characteristics and motivational factors in determining which providers should be targeted to maximize return on recruitment efforts. Existing programs exposing students to rural practice during their training are promising but require further refining. Public policy must include a specific focus on the trajectory of the healthcare workforce and must consider alternative models of healthcare delivery that promote a more diverse, interdisciplinary combination of providers.
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Affiliation(s)
- Ian T MacQueen
- Department of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Melinda Maggard-Gibbons
- Department of Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Veterans Affairs/Robert Wood Johnson Clinical Scholars Program, UCLA, Los Angeles, CA, USA
| | - Gina Capra
- National Association of Community Health Centers, Bethesda, MD, USA
| | - Laura Raaen
- Evidence-Based Practice Center, RAND Corporation, Santa Monica, CA, 90407, USA
| | - Jesus G Ulloa
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Veterans Affairs/Robert Wood Johnson Clinical Scholars Program, UCLA, Los Angeles, CA, USA
- Department of Surgery, UCSF Medical School, San Francisco, CA, USA
| | - Paul G Shekelle
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
- Evidence-Based Practice Center, RAND Corporation, Santa Monica, CA, 90407, USA
| | - Isomi Miake-Lye
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Jessica M Beroes
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Susanne Hempel
- Evidence-Based Practice Center, RAND Corporation, Santa Monica, CA, 90407, USA.
- Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, CA, USA.
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32
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Oliveira A, Lage S, Rodrigues J, Marques A. Reliability, validity and minimal detectable change of computerized respiratory sounds in patients with chronic obstructive pulmonary disease. CLINICAL RESPIRATORY JOURNAL 2017; 12:1838-1848. [PMID: 29148182 DOI: 10.1111/crj.12745] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 10/17/2017] [Accepted: 11/14/2017] [Indexed: 01/12/2023]
Abstract
INTRODUCTION Computerized respiratory sounds (CRS) are closely related to the movement of air within the tracheobronchial tree and are promising outcome measures in patients with chronic obstructive pulmonary disease (COPD). However, CRS measurement properties have been poorly tested. OBJECTIVE The aim of this study was to assess the reliability, validity and the minimal detectable changes (MDC) of CRS in patients with stable COPD. METHODS Fifty patients (36♂, 67.26 ± 9.31y, FEV1 49.52 ± 19.67%predicted) were enrolled. CRS were recorded simultaneously at seven anatomic locations (trachea; right and left anterior, lateral and posterior chest). The number of crackles, wheeze occupation rate, median frequency (F50) and maximum intensity (Imax) were processed using validated algorithms. Within-day and between-days reliability, criterion and construct validity, validity to predict exacerbations and MDC were established. RESULTS CRS presented moderate-to-excellent within-day reliability (ICC1,3 ≥ 0.51; P < .05) and moderate-to-good between-days reliability (ICC1,2 ≥ 0.47; P < .05) for most locations. Negligible-to-moderate correlations with FEV1 %predicted were found (-0.53 < rs < -0.28; P < .05), and the inspiratory number of crackles were the best discriminator between mild-to-moderate and severe-to-very severe airflow limitations (area under the curve >0.78). CRS correlated poorly with patient-reported outcomes (rs < 0.48; P < .05) and did not predict exacerbations. Inspiratory number of crackles at posterior right chest, inspiratory F50 at trachea and anterior left chest and expiratory Imax at anterior right chest were simultaneously reliable and valid, and their MDC were 2.41, 55.27, 29.55 and 3.98, respectively. CONCLUSION CRS are reliable and valid. Their use, integrated with other clinical and patient-reported measures, may fill the gap of assessing small airways and contribute toward a patient's comprehensive evaluation.
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Affiliation(s)
- Ana Oliveira
- Faculty of Sports, University of Porto, Porto, Portugal.,Lab 3R-Respiratory Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal.,Institute for Research in Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
| | - Susan Lage
- Rehabilitation Sciences Program, School of Physical Education, Physiotherapy and Occupational Therapy (EEFFTO), Federal University of Minas Gerais (UFMG), Belo Horizonte, Minas Gerais, Brazil
| | - João Rodrigues
- Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal
| | - Alda Marques
- Lab 3R-Respiratory Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal.,Institute for Research in Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
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Baroi S, McNamara RJ, McKenzie DK, Gandevia S, Brodie MA. Advances in Remote Respiratory Assessments for People with Chronic Obstructive Pulmonary Disease: A Systematic Review. Telemed J E Health 2017; 24:415-424. [PMID: 29083268 DOI: 10.1089/tmj.2017.0160] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality. Advances in remote technologies and telemedicine provide new ways to monitor respiratory function and improve chronic disease management. However, telemedicine does not always include remote respiratory assessments, and the current state of knowledge for people with COPD has not been evaluated. OBJECTIVE Systematically review the use of remote respiratory assessments in people with COPD, including the following questions: What devices have been used? Can acute exacerbations of chronic obstructive pulmonary disease (AECOPD) be predicted by using remote devices? Do remote respiratory assessments improve health-related outcomes? MATERIALS AND METHODS The review protocol was registered (PROSPERO 2016:CRD42016049333). MEDLINE, EMBASE, and COMPENDEX databases were searched for studies that included remote respiratory assessments in people with COPD. A narrative synthesis was then conducted by two reviewers according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. RESULTS Fifteen studies met the inclusion criteria. Forced expiratory volume assessed daily by using a spirometer was the most common modality. Other measurements included resting respiratory rate, respiratory sounds, and end-tidal carbon dioxide level. Remote assessments had high user satisfaction. Benefits included early detection of AECOPD, improved health-related outcomes, and the ability to replace hospital care with a virtual ward. CONCLUSION Remote respiratory assessments are feasible and when combined with sufficient organizational backup can improve health-related outcomes in some but not all cohorts. Future research should focus on the early detection, intervention, and rehabilitation for AECOPD in high-risk people who have limited access to best care and investigate continuous as well as intermittent monitoring.
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Affiliation(s)
- Sidney Baroi
- 1 Graduate School of Biomedical Engineering, University of New South Wales , Kensington, Australia
| | - Renae J McNamara
- 2 Department of Physiotherapy, Prince of Wales Hospital , Randwick, Australia .,3 Department of Respiratory and Sleep Medicine, Prince of Wales Hospital , Randwick, Australia
| | - David K McKenzie
- 3 Department of Respiratory and Sleep Medicine, Prince of Wales Hospital , Randwick, Australia .,4 Faculty of Medicine, University of New South Wales , Kensington, Australia
| | - Simon Gandevia
- 4 Faculty of Medicine, University of New South Wales , Kensington, Australia .,5 Neuroscience Research Australia , Randwick, Australia
| | - Matthew A Brodie
- 1 Graduate School of Biomedical Engineering, University of New South Wales , Kensington, Australia .,5 Neuroscience Research Australia , Randwick, Australia
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Xu L, Fang WY, Zhu F, Zhang HG, Liu K. A coordinated PCP-Cardiologist Telemedicine Model (PCTM) in China's community hypertension care: study protocol for a randomized controlled trial. Trials 2017; 18:236. [PMID: 28545514 PMCID: PMC5445306 DOI: 10.1186/s13063-017-1970-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2016] [Accepted: 05/04/2017] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Hypertension is a major risk factor for cardiovascular disease, and its control rate has remained low worldwide. Studies have found that telemonitoring blood pressure (BP) helped control hypertension in randomized controlled trials. However, little is known about its effect in a structured primary care model in which primary care physicians (PCPs) are partnering with cardiology specialists in electronic healthcare data sharing and medical interventions. This study aims to identify the effects of a coordinated PCP-cardiologist model that applies telemedicine tools to facilitate community hypertension control in China. METHODS/DESIGN Patients with hypertension receiving care at four community healthcare centers that are academically affiliated to Shanghai Chest Hospital, Shanghai JiaoTong University are eligible if they have had uncontrolled BP in the previous 3 months and access to mobile Internet. Study subjects are randomly assigned to three interventional groups: (1) usual care; (2) home-based BP telemonitor with embedded Global System for Mobile Communications (GSM) module and unlimited data plan, an app to access personal healthcare record and receive personalized lifestyle coaching contents, and proficiency training of their use; or (3) this plus coordinated PCP-cardiologist care in which PCPs and cardiologists share data via a secure CareLinker website to determine interventional approaches. The primary outcome is mean change in systolic blood pressure over a 12-month period. Secondary outcomes are changes of diastolic blood pressure, HbA1C, blood lipids, and medication adherence measured by the eight-item Morisky Medication Adherence Scale. DISCUSSION This study will determine whether a coordinated PCP-Cardiologist Telemedicine Model that incorporates the latest telemedicine technologies will improve hypertension care. Success of the model would help streamline the present community healthcare processes and impact a greater number of patients with uncontrolled hypertension. TRIAL REGISTRATION ClinicalTrials.gov, NCT02919033 . Registered on 23 September 2016.
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Affiliation(s)
- Lei Xu
- Department of Cardiology, Shanghai Chest Hospital, Shanghai JiaoTong University, Shanghai, China
| | - Wei-Yi Fang
- Department of Cardiology, Shanghai Chest Hospital, Shanghai JiaoTong University, Shanghai, China.
| | - Fu Zhu
- Department of Cardiology, Shanghai XuHui Hospital, Zhongshan Hospital, FuDan University, Shanghai, China
| | | | - Kai Liu
- CareLinker Co., Ltd., Shanghai, China
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Segrelles-Calvo G, López-Padilla D, de Granda-Orive JI. Pros and Cons of Telemedicine in the Management of Patients with Chronic Respiratory Diseases. ACTA ACUST UNITED AC 2016. [DOI: 10.1016/j.arbr.2016.05.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Al Rajeh AM, Hurst JR. Monitoring of Physiological Parameters to Predict Exacerbations of Chronic Obstructive Pulmonary Disease (COPD): A Systematic Review. J Clin Med 2016; 5:jcm5120108. [PMID: 27897995 PMCID: PMC5184781 DOI: 10.3390/jcm5120108] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 11/14/2016] [Accepted: 11/19/2016] [Indexed: 11/16/2022] Open
Abstract
INTRODUCTION The value of monitoring physiological parameters to predict chronic obstructive pulmonary disease (COPD) exacerbations is controversial. A few studies have suggested benefit from domiciliary monitoring of vital signs, and/or lung function but there is no existing systematic review. OBJECTIVES To conduct a systematic review of the effectiveness of monitoring physiological parameters to predict COPD exacerbation. METHODS An electronic systematic search compliant with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted. The search was updated to April 6, 2016. Five databases were examined: Medical Literature Analysis and Retrieval System Online, or MEDLARS Online (Medline), Excerpta Medica dataBASE (Embase), Allied and Complementary Medicine Database (AMED), Cumulative Index of Nursing and Allied Health Literature (CINAHL) and the Cochrane clinical trials database. RESULTS Sixteen articles met the pre-specified inclusion criteria. Fifteen of these articules reported positive results in predicting COPD exacerbation via monitoring of physiological parameters. Nine studies showed a reduction in peripheral oxygen saturation (SpO₂%) prior to exacerbation onset. Three studies for peak flow, and two studies for respiratory rate reported a significant variation prior to or at exacerbation onset. A particular challenge is accounting for baseline heterogeneity in parameters between patients. CONCLUSION There is currently insufficient information on how physiological parameters vary prior to exacerbation to support routine domiciliary monitoring for the prediction of exacerbations in COPD. However, the method remains promising.
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Affiliation(s)
- Ahmed M Al Rajeh
- UCL Respiratory, Royal Free Campus, University College London, London NW3 2PF, UK.
| | - John R Hurst
- UCL Respiratory, Royal Free Campus, University College London, London NW3 2PF, UK.
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Pros and Cons of Telemedicine in the Management of Patients with Chronic Respiratory Diseases. Arch Bronconeumol 2016; 52:575-576. [PMID: 27374281 DOI: 10.1016/j.arbres.2016.05.016] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2016] [Revised: 04/28/2016] [Accepted: 05/18/2016] [Indexed: 11/20/2022]
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