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Chan AHY, Te Ao B, Baggott C, Cavadino A, Eikholt AA, Harwood M, Hikaka J, Gibbs D, Hudson M, Mirza F, Naeem MA, Semprini R, Chang CL, Tsang KCH, Shah SA, Jeremiah A, Abeysinghe BN, Roy R, Wall C, Wood L, Dalziel S, Pinnock H, van Boven JFM, Roop P, Harrison J. DIGIPREDICT: physiological, behavioural and environmental predictors of asthma attacks-a prospective observational study using digital markers and artificial intelligence-study protocol. BMJ Open Respir Res 2024; 11:e002275. [PMID: 38777583 PMCID: PMC11116853 DOI: 10.1136/bmjresp-2023-002275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 04/11/2024] [Indexed: 05/25/2024] Open
Abstract
INTRODUCTION Asthma attacks are a leading cause of morbidity and mortality but are preventable in most if detected and treated promptly. However, the changes that occur physiologically and behaviourally in the days and weeks preceding an attack are not always recognised, highlighting a potential role for technology. The aim of this study 'DIGIPREDICT' is to identify early digital markers of asthma attacks using sensors embedded in smart devices including watches and inhalers, and leverage health and environmental datasets and artificial intelligence, to develop a risk prediction model to provide an early, personalised warning of asthma attacks. METHODS AND ANALYSIS A prospective sample of 300 people, 12 years or older, with a history of a moderate or severe asthma attack in the last 12 months will be recruited in New Zealand. Each participant will be given a smart watch (to assess physiological measures such as heart and respiratory rate), peak flow meter, smart inhaler (to assess adherence and inhalation) and a cough monitoring application to use regularly over 6 months with fortnightly questionnaires on asthma control and well-being. Data on sociodemographics, asthma control, lung function, dietary intake, medical history and technology acceptance will be collected at baseline and at 6 months. Asthma attacks will be measured by self-report and confirmed with clinical records. The collected data, along with environmental data on weather and air quality, will be analysed using machine learning to develop a risk prediction model for asthma attacks. ETHICS AND DISSEMINATION Ethical approval has been obtained from the New Zealand Health and Disability Ethics Committee (2023 FULL 13541). Enrolment began in August 2023. Results will be presented at local, national and international meetings, including dissemination via community groups, and submission for publication to peer-reviewed journals. TRIAL REGISTRATION NUMBER Australian New Zealand Clinical Trials Registry ACTRN12623000764639; Australian New Zealand Clinical Trials Registry.
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Affiliation(s)
- Amy Hai Yan Chan
- School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand
| | - Braden Te Ao
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Christina Baggott
- Department of Respiratory Medicine and Respiratory research unit, Waikato Hospital, Hamilton, New Zealand
| | - Alana Cavadino
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Amber A Eikholt
- University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, Netherlands
- Medication Adherence Expertise Center of the northern Netherlands (MAECON), Groningen, Netherlands
| | - Matire Harwood
- School of Population Health, University of Auckland, Auckland, New Zealand
| | - Joanna Hikaka
- Te Kupenga Hauora Māori, University of Auckland, Auckland, New Zealand
| | - Dianna Gibbs
- Pinnacle Midlands Health Network, Hamilton, New Zealand
| | - Mariana Hudson
- School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand
| | - Farhaan Mirza
- Department of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand
| | - Muhammed Asif Naeem
- Department of IT and Software Engineering, Auckland University of Technology, Auckland, New Zealand
- National University of Computer and Emerging Sciences, Islamabad, Pakistan
| | - Ruth Semprini
- Medical Research Institute of New Zealand, Wellington, New Zealand
| | - Catherina L Chang
- Department of Respiratory Medicine and Respiratory research unit, Waikato Hospital, Hamilton, New Zealand
| | - Kevin C H Tsang
- University College London, London, UK
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK
| | - Syed Ahmar Shah
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK
| | - Aron Jeremiah
- Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand
| | - Binu Nisal Abeysinghe
- Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand
| | - Rajshri Roy
- Department of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand
| | - Clare Wall
- Department of Nutrition and Dietetics, University of Auckland, Auckland, New Zealand
| | - Lisa Wood
- Biomedical Sciences and Pharmacy, University of Newcastle, Newcastle, New South Wales, Australia
| | - Stuart Dalziel
- Children's Emergency Department, Starship Children's Hospital, Auckland, New Zealand
| | - Hilary Pinnock
- The University of Edinburgh Usher Institute of Population Health Sciences and Informatics, Edinburgh, Edinburgh, UK
| | - Job F M van Boven
- University Medical Centre Groningen, Groningen Research Institute for Asthma and COPD, Groningen, Netherlands
- Medication Adherence Expertise Center of the northern Netherlands (MAECON), Groningen, Netherlands
| | - Partha Roop
- Department of Electrical, Computer and Software Engineering, University of Auckland, Auckland, New Zealand
| | - Jeff Harrison
- School of Pharmacy, The University of Auckland Faculty of Medical and Health Sciences, Auckland, Region, New Zealand
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Li J, Hong X, Jiang M, Kho AT, Tiwari A, Wang AL, Chase RP, Celedón JC, Weiss ST, McGeachie MJ, Tantisira KG. A novel piwi-interacting RNA associates with type 2-high asthma phenotypes. J Allergy Clin Immunol 2024; 153:695-704. [PMID: 38056635 DOI: 10.1016/j.jaci.2023.10.032] [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: 02/23/2023] [Revised: 10/14/2023] [Accepted: 10/25/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND Piwi-interacting RNAs (piRNAs), comprising the largest noncoding RNA group, regulate transcriptional processes. Whether piRNAs are associated with type 2 (T2)-high asthma is unknown. OBJECTIVE We sought to investigate the association between piRNAs and T2-high asthma in childhood asthma. METHODS We sequenced plasma samples from 462 subjects in the Childhood Asthma Management Program (CAMP) as the discovery cohort and 1165 subjects in the Genetics of Asthma in Costa Rica Study (GACRS) as a replication cohort. Sequencing reads were filtered first, and piRNA reads were annotated and normalized. Linear regression was used for the association analysis of piRNAs and peripheral blood eosinophil count, total serum IgE level, and long-term asthma exacerbation in children with asthma. Mediation analysis was performed to investigate the effect direction. We then ascertained if the circulating piRNAs were present in asthmatic airway epithelial cells in a Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo) public data set. RESULTS Fifteen piRNAs were significantly associated with eosinophil count in CAMP (P ≤ .05), and 3 were successfully replicated in GACRS. Eleven piRNAs were associated with total IgE in CAMP, and one of these was replicated in GACRS. All 22 significant piRNAs were identified in epithelial cells in vitro, and 6 of these were differentially expressed between subjects with asthma and healthy controls. Fourteen piRNAs were associated with long-term asthma exacerbation, and effect of piRNAs on long-term asthma exacerbation are mediated through eosinophil count and serum IgE level. CONCLUSION piRNAs are associated with peripheral blood eosinophils and total serum IgE in childhood asthma and may play important roles in T2-high asthma.
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Affiliation(s)
- Jiang Li
- Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass; Shenzhen Key Laboratory of Chinese Medicine Active Substance Screening and Translational Research, Shenzhen, China
| | - Xiaoning Hong
- Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Mingye Jiang
- Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen, China
| | - Alvin T Kho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass; Computational Health Informatics Program, Boston Children's Hospital, Boston, Mass
| | - Anshul Tiwari
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass
| | - Alberta L Wang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass
| | - Robert P Chase
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass
| | - Juan C Celedón
- Division of Pulmonary Medicine, Department of Pediatrics, University of Pittsburgh, Pittsburgh, Pa
| | - Scott T Weiss
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass; Partners Personalized Medicine, Partners Healthcare, Boston, Mass
| | - Michael J McGeachie
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass
| | - Kelan G Tantisira
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Mass; Department of Pediatrics, Division of Respiratory Medicine, University of California-San Diego, La Jolla, Calif.
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Kang N, Lee K, Byun S, Lee JY, Choi DC, Lee BJ. Novel Artificial Intelligence-Based Technology to Diagnose Asthma Using Methacholine Challenge Tests. ALLERGY, ASTHMA & IMMUNOLOGY RESEARCH 2024; 16:42-54. [PMID: 38262390 PMCID: PMC10823143 DOI: 10.4168/aair.2024.16.1.42] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Revised: 08/11/2023] [Accepted: 10/06/2023] [Indexed: 01/25/2024]
Abstract
PURPOSE The methacholine challenge test (MCT) has high sensitivity but relatively low specificity for asthma diagnosis. This study aimed to develop and validate machine learning (ML) models to improve the diagnostic performance of MCT for asthma. METHODS Data from 1,501 patients with asthma symptoms who underwent MCT between 2015 and 2020 were analyzed. The patients were grouped as either the training (80%, n = 1,265) and test sets (20%, n = 236) depending on the time of referral. The conventional model (provocative concentration that causes a 20% decrease in forced expiratory volume in one second [FEV1]; PC20 ≤ 16 mg/mL) was compared with the prediction models derived from five ML methods: logistic regression, support vector machine, random forest, extreme gradient boosting, and artificial neural network. The area under the receiver operator characteristic curves (AUROC) and area under the precision-recall curves (AUPRC) of each model were compared. The prediction models were further analyzed using different input combinations of FEV1, forced vital capacity (FVC), and forced expiratory flow at 25%-75% of forced vital capacity (FEF25%-75%) values obtained during MCT. RESULTS In total, 545 patients (36.3%) were diagnosed with asthma. The AUROC of the conventional model was 0.856 (95% confidence interval [CI], 0.852-0.861), and the AUPRC was 0.759 (95% CI, 0.751-0.766). All the five ML prediction models had higher AUROC and AUPRC values than those of the conventional model, and random forest showed both highest AUROC (0.950; 95% CI, 0.948-0.952) and AUROC (0.909; 95% CI, 0.905-0.914) when FEV1, FVC, and FEF25%-75% were included as inputs. CONCLUSIONS Artificial intelligence-based models showed excellent performance in asthma prediction compared to using PC20 ≤ 16 mg/mL. The novel technology could be used to enhance the clinical diagnosis of asthma.
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Affiliation(s)
- Noeul Kang
- Division of Allergy, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - KyungHyun Lee
- Department of Electronics Engineering, Incheon National University, Incheon, Korea
| | - Sangwon Byun
- Department of Electronics Engineering, Incheon National University, Incheon, Korea
| | - Jin-Young Lee
- Health Promotion Center, Samsung Medical Center, Seoul, Korea
| | - Dong-Chull Choi
- Division of Allergy, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Byung-Jae Lee
- Division of Allergy, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
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Budiarto A, Tsang KCH, Wilson AM, Sheikh A, Shah SA. Machine Learning-Based Asthma Attack Prediction Models From Routinely Collected Electronic Health Records: Systematic Scoping Review. JMIR AI 2023; 2:e46717. [PMID: 38875586 PMCID: PMC11041490 DOI: 10.2196/46717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 09/28/2023] [Accepted: 10/09/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND An early warning tool to predict attacks could enhance asthma management and reduce the likelihood of serious consequences. Electronic health records (EHRs) providing access to historical data about patients with asthma coupled with machine learning (ML) provide an opportunity to develop such a tool. Several studies have developed ML-based tools to predict asthma attacks. OBJECTIVE This study aims to critically evaluate ML-based models derived using EHRs for the prediction of asthma attacks. METHODS We systematically searched PubMed and Scopus (the search period was between January 1, 2012, and January 31, 2023) for papers meeting the following inclusion criteria: (1) used EHR data as the main data source, (2) used asthma attack as the outcome, and (3) compared ML-based prediction models' performance. We excluded non-English papers and nonresearch papers, such as commentary and systematic review papers. In addition, we also excluded papers that did not provide any details about the respective ML approach and its result, including protocol papers. The selected studies were then summarized across multiple dimensions including data preprocessing methods, ML algorithms, model validation, model explainability, and model implementation. RESULTS Overall, 17 papers were included at the end of the selection process. There was considerable heterogeneity in how asthma attacks were defined. Of the 17 studies, 8 (47%) studies used routinely collected data both from primary care and secondary care practices together. Extreme imbalanced data was a notable issue in most studies (13/17, 76%), but only 38% (5/13) of them explicitly dealt with it in their data preprocessing pipeline. The gradient boosting-based method was the best ML method in 59% (10/17) of the studies. Of the 17 studies, 14 (82%) studies used a model explanation method to identify the most important predictors. None of the studies followed the standard reporting guidelines, and none were prospectively validated. CONCLUSIONS Our review indicates that this research field is still underdeveloped, given the limited body of evidence, heterogeneity of methods, lack of external validation, and suboptimally reported models. We highlighted several technical challenges (class imbalance, external validation, model explanation, and adherence to reporting guidelines to aid reproducibility) that need to be addressed to make progress toward clinical adoption.
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Affiliation(s)
- Arif Budiarto
- Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
- Bioinformatics and Data Science Research Center, Bina Nusantara University, Jakarta, Indonesia
| | - Kevin C H Tsang
- Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Andrew M Wilson
- Norwich Medical School, University of East Anglia, Norwich, United Kingdom
- Norfolk and Norwich University Hospital NHS Foundation Trust, Norwich, United Kingdom
| | - Aziz Sheikh
- Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
| | - Syed Ahmar Shah
- Asthma UK Center for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, United Kingdom
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Schuler CL, Kercsmar C, Mansour M, McDowell KM, Huang G, Hossain MM, Robinette ED, Beck AF. Identifying asthma-related risks during hospitalization using the child asthma risk assessment tool. J Asthma 2023; 60:2189-2197. [PMID: 37345884 DOI: 10.1080/02770903.2023.2228897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 06/20/2023] [Indexed: 06/23/2023]
Abstract
Objective: The Child Asthma Risk Assessment Tool (CARAT) identifies risk factors for asthma morbidity. We hypothesized that CARAT-identified risk factors (using a CARAT adapted for inpatient use) would be associated with future healthcare utilization and would identify areas for intervention.Methods: We reviewed CARAT data collected during pediatric asthma admissions from 2010-2015, assessing for risk factors in environmental, medical, and social domains and providing prompts for inpatient (specialist consultation or social services engagement) and post-discharge interventions (home care visit or home environmental assessment). Confirmatory factor analysis identified groups of CARAT-identified risk factors with similar effects on healthcare utilization (latent factors). Structural equation models then evaluated relationships between latent factors and future utilization.Results: There were 2731 unique patients admitted for asthma exacerbations; 1015 (37%) had complete CARAT assessments and were included in analyses. Those with incomplete CARAT assessments were more often younger and privately-insured. CARAT-identified risk factors across domains were common in children hospitalized for exacerbations. Risks in the environmental domain were most common. Inpatient asthma consults by pulmonologists or allergists and home care referrals were the most frequent interventions indicated (62%, 628/1015, and 50%, 510/1015, respectively). Two latent factors were positively associated with healthcare utilization in the year after index stay - social stressors and known/suspected allergies (both p < 0.05). Stratified analyses analyzing data just from those children with prior healthcare utilization also indicated known/suspected allergies to be positively associated with future utilization.Conclusions: Inpatient interventions to address social stressors and allergic profiles may be warranted to reduce subsequent asthma morbidity.
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Affiliation(s)
- Christine L Schuler
- Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Carolyn Kercsmar
- Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Mona Mansour
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of General and Community Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Karen M McDowell
- Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Guixia Huang
- Division of Epidemiology and Biostatistics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Md Monir Hossain
- Division of Pulmonary Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Epidemiology and Biostatistics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Eric D Robinette
- Division of Infectious Disease, Akron Children's Hospital, Akron, OH, USA
| | - Andrew F Beck
- Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of General and Community Pediatrics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
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Antão J, de Mast J, Marques A, Franssen FME, Spruit MA, Deng Q. Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases. Expert Rev Respir Med 2023; 17:1207-1219. [PMID: 38270524 DOI: 10.1080/17476348.2024.2302940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 01/04/2024] [Indexed: 01/26/2024]
Abstract
INTRODUCTION Asthma and chronic obstructive pulmonary disease (COPD) are leading causes of morbidity and mortality worldwide. Despite all available diagnostics and treatments, these conditions pose a significant individual, economic and social burden. Artificial intelligence (AI) promises to support clinical decision-making processes by optimizing diagnosis and treatment strategies of these heterogeneous and complex chronic respiratory diseases. Its capabilities extend to predicting exacerbation risk, disease progression and mortality, providing healthcare professionals with valuable insights for more effective care. Nevertheless, the knowledge gap between respiratory clinicians and data scientists remains a major constraint for wide application of AI and may hinder future progress. This narrative review aims to bridge this gap and encourage AI deployment by explaining its methodology and added value in asthma and COPD diagnosis and treatment. AREAS COVERED This review offers an overview of the fundamental concepts of AI and machine learning, outlines the key steps in building a model, provides examples of their applicability in asthma and COPD care, and discusses barriers to their implementation. EXPERT OPINION Machine learning can advance our understanding of asthma and COPD, enabling personalized therapy and better outcomes. Further research and validation are needed to ensure the development of clinically meaningful and generalizable models.
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Affiliation(s)
- Joana Antão
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Jeroen de Mast
- Economics and Business, University of Amsterdam, Amsterdam, The Netherlands
| | - Alda Marques
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Frits M E Franssen
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Martijn A Spruit
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Qichen Deng
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
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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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Hamid N, Portnoy JM, Pandya A. Computer-Assisted Clinical Diagnosis and Treatment. Curr Allergy Asthma Rep 2023; 23:509-517. [PMID: 37351722 DOI: 10.1007/s11882-023-01097-8] [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] [Accepted: 06/06/2023] [Indexed: 06/24/2023]
Abstract
PURPOSE OF REVIEW Computer-assisted diagnosis and treatment (CAD/CAT) is a rapidly growing field of medicine that uses computer technology and telehealth to aid in the diagnosis and treatment of various diseases. The purpose of this paper is to provide a review on computer-assisted diagnosis and treatment. This technology gives providers access to diagnostic tools and treatment options so that they can make more informed decisions leading to improved patient outcomes. RECENT FINDINGS CAD/CAT has expanded in allergy and immunology in the form of digital tools that enable remote patient monitoring such as digital inhalers, pulmonary function tests, and E-diaries. By incorporating this information into electronic medical records (EMRs), providers can use this information to make the best, evidence-based diagnosis and to recommend treatment that is likely to be most effective. A major benefit of CAD/CAT is that by analyzing large amounts of data, tailored recommendations can be made to improve patient outcomes and reduce the risk of adverse events. Machine learning can assist with medical data acquisition, feature extraction, interpretation, and decision support. It is important to note that this technology is not meant to replace human professionals. Instead, it is designed to assist healthcare professionals to better diagnose and treat patients.
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Affiliation(s)
- Nadia Hamid
- Department of Internal Medicine, University of Kansas Hospital, 3901 Rainbow Blvd, Kansas City, KS, 66160, USA
| | - Jay M Portnoy
- Division of Allergy, Immunology, Pulmonary and Sleep Medicine, Children's Mercy Hospital and University of Missouri-Kansas City, 2401 Gillham Road, Kansas City, MO, 64108, USA
| | - Aarti Pandya
- Division of Allergy, Immunology, Pulmonary and Sleep Medicine, Children's Mercy Hospital and University of Missouri-Kansas City, 2401 Gillham Road, Kansas City, MO, 64108, USA.
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Romero-Tapia SDJ, Becerril-Negrete JR, Castro-Rodriguez JA, Del-Río-Navarro BE. Early Prediction of Asthma. J Clin Med 2023; 12:5404. [PMID: 37629446 PMCID: PMC10455492 DOI: 10.3390/jcm12165404] [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: 06/30/2023] [Revised: 07/26/2023] [Accepted: 08/03/2023] [Indexed: 08/27/2023] Open
Abstract
The clinical manifestations of asthma in children are highly variable, are associated with different molecular and cellular mechanisms, and are characterized by common symptoms that may diversify in frequency and intensity throughout life. It is a disease that generally begins in the first five years of life, and it is essential to promptly identify patients at high risk of developing asthma by using different prediction models. The aim of this review regarding the early prediction of asthma is to summarize predictive factors for the course of asthma, including lung function, allergic comorbidity, and relevant data from the patient's medical history, among other factors. This review also highlights the epigenetic factors that are involved, such as DNA methylation and asthma risk, microRNA expression, and histone modification. The different tools that have been developed in recent years for use in asthma prediction, including machine learning approaches, are presented and compared. In this review, emphasis is placed on molecular mechanisms and biomarkers that can be used as predictors of asthma in children.
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Affiliation(s)
- Sergio de Jesus Romero-Tapia
- Health Sciences Academic Division (DACS), Juarez Autonomous University of Tabasco (UJAT), Villahermosa 86040, Mexico
| | - José Raúl Becerril-Negrete
- Department of Clinical Immunopathology, Universidad Autónoma del Estado de México, Toluca 50000, Mexico;
| | - Jose A. Castro-Rodriguez
- Department of Pediatric Pulmonology, School of Medicine, Pontificia Universidad Católica de Chile, Santiago 8330077, Chile;
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10
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Pinnock H, Hui CY, van Boven JF. Implementation of digital home monitoring and management of respiratory disease. Curr Opin Pulm Med 2023; 29:302-312. [PMID: 37132298 PMCID: PMC10241431 DOI: 10.1097/mcp.0000000000000965] [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] [Indexed: 05/04/2023]
Abstract
PURPOSE OF REVIEW Digital respiratory monitoring interventions (e.g. smart inhalers and digital spirometers) can improve clinical outcomes and/or organizational efficiency, and the focus is shifting to sustainable implementation as an approach to delivering respiratory care. This review considers key aspects of the technology infrastructure, discusses the regulatory, financial and policy context that influence implementation, and highlights the over-arching societal themes of equity, trust and communication. RECENT FINDINGS Technological requirements include developing interoperable and connected systems; establishing stable, wide internet coverage; addressing data accuracy and monitoring adherence; realising the potential of artificial intelligence; and avoiding clinician data overload. Policy challenges include concerns about quality assurance and increasingly complex regulatory systems. Financial barriers include lack of clarity over cost-effectiveness, budget impact and reimbursement. Societal concerns focus on the potential to increase inequities because of poor e-health literacy, deprivation or lack of available infrastructure, the need to understand the implications for patient/professional interactions of shifting care to remote delivery and ensuring confidentiality of personal data. SUMMARY Understanding and addressing the implementation challenges posed by gaps in policy, regulatory, financial, and technical infrastructure is essential to support delivery of equitable respiratory care that is acceptable to patients and professionals.
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Affiliation(s)
| | - Chi Yan Hui
- Usher Institute, The University of Edinburgh, UK
| | - Job F.M. van Boven
- Department of Clinical Pharmacy and Pharmacology, Groningen Research Institute for Asthma and COPD (GRIAC), University Medical Center Groningen, University of Groningen, The Netherlands
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11
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Drummond D, Roukema J, Pijnenburg M. Home monitoring in asthma: towards digital twins. Curr Opin Pulm Med 2023; 29:270-276. [PMID: 37102597 DOI: 10.1097/mcp.0000000000000963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
PURPOSE OF REVIEW We highlight the recent advances in home monitoring of patients with asthma, and show that these advances converge towards the implementation of digital twin systems. RECENT FINDINGS Connected devices for asthma are increasingly numerous, reliable and effective: new electronic monitoring devices extend to nebulizers and spacers, are able to assess the quality of the inhalation technique, and to identify asthma attack triggers when they include a geolocation function; environmental data can be acquired from databases and refined by wearable air quality sensors; smartwatches are better validated. Connected devices are increasingly integrated into global monitoring systems. At the same time, machine learning techniques open up the possibility of using the large amount of data collected to obtain a holistic assessment of asthma patients, and social robots and virtual assistants can help patients in the daily management of their asthma. SUMMARY Advances in the internet of things, machine learning techniques and digital patient support tools for asthma are paving the way for a new era of research on digital twins in asthma.
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Affiliation(s)
- David Drummond
- Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, Université Paris Cité, Inserm UMR 1138, HeKA team, Centre de Recherche des Cordeliers, Paris, France
| | - Jolt Roukema
- Department of Paediatrics/Paediatric Pulmonology, Radboud University Medical Centre, Amalia Children's Hospital, Nijmegen
| | - Mariëlle Pijnenburg
- Department of Paediatrics/Paediatric Respiratory Medicine and Allergology, Erasmus University Medical Centre - Sophia Children's Hospital, Rotterdam, The Netherlands
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12
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Pinnock H, Noble M, Lo D, McClatchey K, Marsh V, Hui CY. Personalised management and supporting individuals to live with their asthma in a primary care setting. Expert Rev Respir Med 2023; 17:577-596. [PMID: 37535011 DOI: 10.1080/17476348.2023.2241357] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 07/24/2023] [Indexed: 08/04/2023]
Abstract
INTRODUCTION Complementing recognition of biomedical phenotypes, a primary care approach to asthma care recognizes diversity of disease, health beliefs, and lifestyle at a population and individual level. AREAS COVERED We review six aspects of personalized care particularly pertinent to primary care management of asthma: personalizing support for individuals living with asthma; targeting asthma care within populations; managing phenotypes of wheezy pre-school children; personalizing management to the individual; meeting individual preferences for provision of asthma care; optimizing digital approaches to enhance personalized care. EXPERT OPINION In a primary care setting, personalized management and supporting individuals to live with asthma extend beyond the contemporary concepts of biological phenotypes and pharmacological 'treatable traits' to encompass evidence-based tailored support for self-management, and delivery of patient-centered care including motivational interviewing. It extends to how we organize clinical practiceand the choices provided in mode of consultation. Diagnostic uncertainty due to recognition of phenotypes of pre-school wheeze remains a challenge for primary care. Digital health can support personalized management, but there are concerns about increasing inequities. This broad approach reflects the traditionally holistic ethos of primary care ('knowing their patients and understanding their communities'), but the core concepts resonate with all healthcare.
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Affiliation(s)
- Hilary Pinnock
- Usher Institute, The University of Edinburgh, Edinburgh, UK
- Whitstable Medical Practice, Whitstable, Kent, UK
| | - Mike Noble
- Primary Care Research Group, Institute of Health Research, University of Exeter Medical School, Exeter, UK
- Acle Medical Centre, Norfolk, UK
| | - David Lo
- Department of Respiratory Sciences, College of Life Sciences, NIHR Biomedical Research Centre (Respiratory Theme), University of Leicester, Leicester, UK
- Department of Paediatric Respiratory Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK
| | | | - Viv Marsh
- Usher Institute, The University of Edinburgh, Edinburgh, UK
- CYP Asthma Transformation Black Country Integrated Care Board, Wolverhampton, UK
| | - Chi Yan Hui
- Usher Institute, The University of Edinburgh, Edinburgh, UK
- Deanery of Molecular, Genetic and Population Health Sciences, The University of Edinburgh, Edinburgh, UK
- The UK Engineering Council, London, UK
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13
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Pompe E, Kwee AK, Tejwani V, Siddharthan T, Mohamed Hoesein FA. Imaging-derived biomarkers in Asthma: Current status and future perspectives. Respir Med 2023; 208:107130. [PMID: 36702169 DOI: 10.1016/j.rmed.2023.107130] [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: 10/10/2022] [Revised: 01/20/2023] [Accepted: 01/21/2023] [Indexed: 01/24/2023]
Abstract
Asthma is a common disorder affecting around 315 million individuals worldwide. The heterogeneity of asthma is becoming increasingly important in the era of personalized treatment and response assessment. Several radiological imaging modalities are available in asthma including chest x-ray, computed tomography (CT) and magnetic resonance imaging (MRI) scanning. In addition to qualitative imaging, quantitative imaging could play an important role in asthma imaging to identify phenotypes with distinct disease course and response to therapy, including biologics. MRI in asthma is mainly performed in research settings given cost, technical challenges, and there is a need for standardization. Imaging analysis applications of artificial intelligence (AI) to subclassify asthma using image analysis have demonstrated initial feasibility, though additional work is necessary to inform the role of AI in clinical practice.
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Affiliation(s)
- Esther Pompe
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Anastasia Kal Kwee
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands.
| | | | - Trishul Siddharthan
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Miami (TS), USA.
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14
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Iqbal MA, Devarajan K, Ahmed SM. Optimal convolutional neural network classifier for asthma disease detection using speech signals. INTERNATIONAL JOURNAL OF HEALTHCARE MANAGEMENT 2023. [DOI: 10.1080/20479700.2023.2173774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Affiliation(s)
- Md. Asim Iqbal
- Department of E.C.E, Annamalai University, Tamil Nadu, India
| | - K. Devarajan
- Department of E.C.E, Annamalai University, Tamil Nadu, India
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15
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Khanam UA, Gao Z, Adamko D, Kusalik A, Rennie DC, Goodridge D, Chu L, Lawson JA. A scoping review of asthma and machine learning. J Asthma 2023; 60:213-226. [PMID: 35171725 DOI: 10.1080/02770903.2022.2043364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
OBJECTIVE The objective of this study was to determine the extent of machine learning (ML) application in asthma research and to identify research gaps while mapping the existing literature. DATA SOURCES We conducted a scoping review. PubMed, ProQuest, and Embase Scopus databases were searched with an end date of September 18, 2020. STUDY SELECTION DistillerSR was used for data management. Inclusion criteria were an asthma focus, human participants, ML techniques, and written in English. Exclusion criteria were abstract only, simulation-based, not human based, or were reviews or commentaries. Descriptive statistics were presented. RESULTS A total of 6,317 potential articles were found. After removing duplicates, and reviewing the titles and abstracts, 102 articles were included for the full text analysis. Asthma episode prediction (24.5%), asthma phenotype classification (16.7%), and genetic profiling of asthma (12.7%) were the top three study topics. Cohort (52.9%), cross-sectional (20.6%), and case-control studies (11.8%) were the study designs most frequently used. Regarding the ML techniques, 34.3% of the studies used more than one technique. Neural networks, clustering, and random forests were the most common ML techniques used where they were used in 20.6%, 18.6%, and 17.6% of studies, respectively. Very few studies considered location of residence (i.e. urban or rural status). CONCLUSIONS The use of ML in asthma studies has been increasing with most of this focused on the three major topics (>50%). Future research using ML could focus on gaps such as a broader range of study topics and focus on its use in additional populations (e.g. location of residence). Supplemental data for this article is available online at http://dx.doi.org/ .
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Affiliation(s)
- Ulfat A Khanam
- Health Sciences Program, College of Medicine, Canadian Centre for Health and Safety in Agriculture, Respiratory Research Centre, University of Saskatchewan, Saskatoon, SK, Canada
| | - Zhiwei Gao
- Department of Medicine, Memorial University of Newfoundland, St. John's, NL, Canada
| | - Darryl Adamko
- Department of Paediatrics, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Anthony Kusalik
- Department of Computer Science, College of Arts and Science, University of Saskatchewan, Saskatoon, SK, Canada
| | - Donna C Rennie
- College of Nursing and Canadian Centre for Health and Safety in Agriculture, University of Saskatchewan, Saskatoon, SK, Canada
| | - Donna Goodridge
- Department of Medicine, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada
| | - Luan Chu
- Provincial Research Data Services, Alberta Health Service, Calgary, AB, Canada
| | - Joshua A Lawson
- Department of Medicine, Canadian Centre for Health and Safety in Agriculture, and Respiratory Research Centre, College of Medicine, University of Saskatchewan, Saskatoon, SK, Canada
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Calcaterra V, Pagani V, Zuccotti G. Digital Twin: A Future Health Challenge in Prevention, Early Diagnosis and Personalisation of Medical Care in Paediatrics. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:2181. [PMID: 36767547 PMCID: PMC9916261 DOI: 10.3390/ijerph20032181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Modern medicine must move from a wait-and-see and remedial system to a preventive and interdisciplinary science that aims to provide patients with personalised and precise treatment planning [...].
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Affiliation(s)
- Valeria Calcaterra
- Department of Internal Medicine and Therapeutics, University of Pavia, 27100 Pavia, Italy
- Pediatric Department, Buzzi Children’s Hospital, 20154 Milano, Italy
| | - Valter Pagani
- Grant & Research Department-LJA-2021, Asomi College of Sciences, 2080 Marsa, Malta
| | - Gianvincenzo Zuccotti
- Pediatric Department, Buzzi Children’s Hospital, 20154 Milano, Italy
- Department of Biomedical and Clinical Science, University of Milano, 20157 Milano, Italy
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17
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Al Meslamani AZ. How AI is advancing asthma management? Insights into economic and clinical aspects. J Med Econ 2023; 26:1489-1494. [PMID: 37902681 DOI: 10.1080/13696998.2023.2277072] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 10/26/2023] [Indexed: 10/31/2023]
Abstract
Asthma, an increasingly prevalent chronic respiratory condition, incurs significant economic costs worldwide. Artificial Intelligence (AI), particularly Machine Learning (ML), has been widely recognized as transformative when applied to asthma care. This commentary investigates how AI and ML may improve clinical outcomes while alleviating some of the costs associated with asthma care. AI's powerful analytical abilities could usher in an unprecedented era of preventive measures, particularly by identifying at-risk populations and anticipating environmental triggers. ML shows promise for enhancing real-time monitoring, early detection, and tailored treatment strategies in paediatric asthma, potentially reducing hospitalizations and emergency care costs. Emerging AI-powered wearable technologies are catalysing a revolutionary shift in patient monitoring, providing proactive interventions. Although optimistic, this commentary highlights a gap in empirical studies evaluating the cost-effectiveness of AI in asthma care and stresses the need for larger datasets to accurately represent the economic benefits of AI solutions. Additionally, this paper emphasizes the ethical considerations surrounding data privacy and algorithmic bias, which are vital for the successful and equitable integration of AI into healthcare settings. This editorial underscores the urgent necessity of conducting thorough analyses to assess all economic implications, facilitate optimized resource allocation, and foster a nuanced understanding of AI/ML technologies in asthma management that may reduce costs to healthcare systems.
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Affiliation(s)
- Ahmad Z Al Meslamani
- College of Pharmacy, Al Ain University, Abu Dhabi, United Arab Emirates
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, United Arab Emirates
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18
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Gonsard A, AbouTaam R, Prévost B, Roy C, Hadchouel A, Nathan N, Taytard J, Pirojoc A, Delacourt C, Wanin S, Drummond D. Children's views on artificial intelligence and digital twins for the daily management of their asthma: a mixed-method study. Eur J Pediatr 2023; 182:877-888. [PMID: 36512148 PMCID: PMC9745267 DOI: 10.1007/s00431-022-04754-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 11/30/2022] [Accepted: 12/05/2022] [Indexed: 12/15/2022]
Abstract
New technologies enable the creation of digital twin systems (DTS) combining continuous data collection from children's home and artificial intelligence (AI)-based recommendations to adapt their care in real time. The objective was to assess whether children and adolescents with asthma would be ready to use such DTS. A mixed-method study was conducted with 104 asthma patients aged 8 to 17 years. The potential advantages and disadvantages associated with AI and the use of DTS were collected in semi-structured interviews. Children were then asked whether they would agree to use a DTS for the daily management of their asthma. The strength of their decision was assessed as well as the factors determining their choice. The main advantages of DTS identified by children were the possibility to be (i) supported in managing their asthma (ii) from home and (iii) in real time. Technical issues and the risk of loss of humanity were the main drawbacks reported. Half of the children (56%) were willing to use a DTS for the daily management of their asthma if it was as effective as current care, and up to 93% if it was more effective. Those with the best computer skills were more likely to choose the DTS, while those who placed a high value on the physician-patient relationship were less likely to do so. Conclusions: The majority of children were ready to use a DTS for the management of their asthma, particularly if it was more effective than current care. The results of this study support the development of DTS for childhood asthma and the evaluation of their effectiveness in clinical trials. What is Known: • New technologies enable the creation of digital twin systems (DTS) for children with asthma. • Acceptance of these DTSs by children with asthma is unknown. What is New: • Half of the children (56%) were willing to use a DTS for the daily management of their asthma if it was as effective as current care, and up to 93% if it was more effective. •Children identified the ability to be supported from home and in real time as the main benefits of DTS.
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Affiliation(s)
- Apolline Gonsard
- Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 Rue de Sèvres, 75015 Paris, France
| | - Rola AbouTaam
- Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 Rue de Sèvres, 75015 Paris, France
| | - Blandine Prévost
- Department of Pediatric Pulmonology, University Hospital Armand Trousseau, AP-HP Paris, France
| | - Charlotte Roy
- Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 Rue de Sèvres, 75015 Paris, France
| | - Alice Hadchouel
- Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 Rue de Sèvres, 75015 Paris, France
- Université Paris Cité, Paris, France
| | - Nadia Nathan
- Department of Pediatric Pulmonology, University Hospital Armand Trousseau, AP-HP Paris, France
| | - Jessica Taytard
- Department of Pediatric Pulmonology, University Hospital Armand Trousseau, AP-HP Paris, France
- UMRS1158 Neurophysiologie Respiratoire Expérimentale Et Clinique, Sorbonne Université, INSERM, Paris, France
| | | | - Christophe Delacourt
- Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 Rue de Sèvres, 75015 Paris, France
- Université Paris Cité, Paris, France
| | - Stéphanie Wanin
- Department of Pediatric Allergology, University Hospital Armand Trousseau, APHP, Paris, France
| | - David Drummond
- Department of Pediatric Pulmonology and Allergology, University Hospital Necker-Enfants Malades, AP-HP, 149 Rue de Sèvres, 75015 Paris, France
- Université Paris Cité, Paris, France
- Inserm UMR 1138, Centre de Recherche Des Cordeliers, HeKA Team, 75006 Paris, France
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Kim K, Lee MK, Shin HK, Lee H, Kim B, Kang S. Development and application of survey-based artificial intelligence for clinical decision support in managing infectious diseases: A pilot study on a hospital in central Vietnam. Front Public Health 2022; 10:1023098. [PMID: 36438286 PMCID: PMC9683382 DOI: 10.3389/fpubh.2022.1023098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 10/17/2022] [Indexed: 11/11/2022] Open
Abstract
Introduction In this study, we developed a simplified artificial intelligence to support the clinical decision-making of medical personnel in a resource-limited setting. Methods We selected seven infectious disease categories that impose a heavy disease burden in the central Vietnam region: mosquito-borne disease, acute gastroenteritis, respiratory tract infection, pulmonary tuberculosis, sepsis, primary nervous system infection, and viral hepatitis. We developed a set of questionnaires to collect information on the current symptoms and history of patients suspected to have infectious diseases. We used data collected from 1,129 patients to develop and test a diagnostic model. We used XGBoost, LightGBM, and CatBoost algorithms to create artificial intelligence for clinical decision support. We used a 4-fold cross-validation method to validate the artificial intelligence model. After 4-fold cross-validation, we tested artificial intelligence models on a separate test dataset and estimated diagnostic accuracy for each model. Results We recruited 1,129 patients for final analyses. Artificial intelligence developed by the CatBoost algorithm showed the best performance, with 87.61% accuracy and an F1-score of 87.71. The F1-score of the CatBoost model by disease entity ranged from 0.80 to 0.97. Diagnostic accuracy was the lowest for sepsis and the highest for central nervous system infection. Conclusion Simplified artificial intelligence could be helpful in clinical decision support in settings with limited resources.
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Affiliation(s)
- Kwanghyun Kim
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, South Korea,Department of Public Health, Graduate School, Yonsei University, Seoul, South Korea,*Correspondence: Kwanghyun Kim
| | - Myung-ken Lee
- Graduate School of Public Health, Kosin University College of Medicine, Busan, South Korea
| | - Hyun Kyung Shin
- Acryl, Seoul, South Korea,FineHealthcare, Seoul, South Korea
| | | | | | - Sunjoo Kang
- Graduate School of Public Health, Yonsei University, Seoul, South Korea,Sunjoo Kang
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20
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Drummond D, Coulet A. Technical, Ethical, Legal, and Societal Challenges With Digital Twin Systems for the Management of Chronic Diseases in Children and Young People. J Med Internet Res 2022; 24:e39698. [DOI: 10.2196/39698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/11/2022] [Accepted: 10/11/2022] [Indexed: 11/06/2022] Open
Abstract
Advances in digital medicine now make it possible to use digital twin systems (DTS), which combine (1) extensive patient monitoring through the use of multiple sensors and (2) personalized adaptation of patient care through the use of software. After the artificial pancreas system already operational in children with type 1 diabetes, new DTS could be developed for real-time monitoring and management of children with chronic diseases. Just as providing care for children is a specific discipline—pediatrics—because of their particular characteristics and needs, providing digital care for children also presents particular challenges. This article reviews the technical challenges, mainly related to the problem of data collection in children; the ethical challenges, including the need to preserve the child's place in their care when using DTS; the legal challenges and the dual need to guarantee the safety of DTS for children and to ensure their access to DTS; and the societal challenges, including the needs to maintain human contact and trust between the child and the pediatrician and to limit DTS to specific uses to avoid contributing to a surveillance society and, at another level, to climate change.
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21
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Tsang KCH, Pinnock H, Wilson AM, Salvi D, Shah SA. Predicting asthma attacks using connected mobile devices and machine learning: the AAMOS-00 observational study protocol. BMJ Open 2022; 12:e064166. [PMID: 36192103 PMCID: PMC9535155 DOI: 10.1136/bmjopen-2022-064166] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
INTRODUCTION Supported self-management empowering people with asthma to detect early deterioration and take timely action reduces the risk of asthma attacks. Smartphones and smart monitoring devices coupled with machine learning could enhance self-management by predicting asthma attacks and providing tailored feedback.We aim to develop and assess the feasibility of an asthma attack predictor system based on data collected from a range of smart devices. METHODS AND ANALYSIS A two-phase, 7-month observational study to collect data about asthma status using three smart monitoring devices, and daily symptom questionnaires. We will recruit up to 100 people via social media and from a severe asthma clinic, who are at risk of attacks and who use a pressurised metered dose relief inhaler (that fits the smart inhaler device).Following a preliminary month of daily symptom questionnaires, 30 participants able to comply with regular monitoring will complete 6 months of using smart devices (smart peak flow meter, smart inhaler and smartwatch) and daily questionnaires to monitor asthma status. The feasibility of this monitoring will be measured by the percentage of task completion. The occurrence of asthma attacks (definition: American Thoracic Society/European Respiratory Society Task Force 2009) will be detected by self-reported use (or increased use) of oral corticosteroids. Monitoring data will be analysed to identify predictors of asthma attacks. At the end of the monitoring, we will assess users' perspectives on acceptability and utility of the system with an exit questionnaire. ETHICS AND DISSEMINATION Ethics approval was provided by the East of England - Cambridge Central Research Ethics Committee. IRAS project ID: 285 505 with governance approval from ACCORD (Academic and Clinical Central Office for Research and Development), project number: AC20145. The study sponsor is ACCORD, the University of Edinburgh.Results will be reported through peer-reviewed publications, abstracts and conference posters. Public dissemination will be centred around blogs and social media from the Asthma UK network and shared with study participants.
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Affiliation(s)
- Kevin Cheuk Him Tsang
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Hilary Pinnock
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Andrew M Wilson
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Norwich Medical School, University of East Anglia, Norwich, UK
- Norwich University Hospital Foundation Trust, Colney Lane, Norwich, UK
| | - Dario Salvi
- Internet of Things and People Research Centre, Malmo University, Malmo, Sweden
| | - Syed Ahmar Shah
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
- Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
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22
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Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare (Basel) 2022; 10:healthcare10071269. [PMID: 35885796 PMCID: PMC9320442 DOI: 10.3390/healthcare10071269] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 06/25/2022] [Accepted: 06/30/2022] [Indexed: 12/29/2022] Open
Abstract
This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.
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Patel D, Hall GL, Broadhurst D, Smith A, Schultz A, Foong RE. Does machine learning have a role in the prediction of asthma in children? Paediatr Respir Rev 2022; 41:51-60. [PMID: 34210588 DOI: 10.1016/j.prrv.2021.06.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 06/03/2021] [Indexed: 02/07/2023]
Abstract
Asthma is the most common chronic lung disease in childhood. There has been a significant worldwide effort to develop tools/methods to identify children's risk for asthma as early as possible for preventative and early management strategies. Unfortunately, most childhood asthma prediction tools using conventional statistical models have modest accuracy, sensitivity, and positive predictive value. Machine learning is an approach that may improve on conventional models by finding patterns and trends from large and complex datasets. Thus far, few studies have utilized machine learning to predict asthma in children. This review aims to critically assess these studies, describe their limitations, and discuss future directions to move from proof-of-concept to clinical application.
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Affiliation(s)
- Dimpalben Patel
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia; School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Australia.
| | - Graham L Hall
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia; School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Australia.
| | - David Broadhurst
- Centre for Integrative Metabolomics & Computational Biology, Edith Cowan University, Joondalup, Australia.
| | - Anne Smith
- School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Australia.
| | - André Schultz
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia; Department of Respiratory Medicine, Child and Adolescent Health Service, Perth, Australia; Division of Paediatrics, Faculty of Medicine, University of Western Australia, Perth, Australia.
| | - Rachel E Foong
- Wal-yan Respiratory Research Centre, Telethon Kids Institute, University of Western Australia, Perth, Australia; School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Australia.
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Exarchos K, Aggelopoulou A, Oikonomou A, Biniskou T, Beli V, Antoniadou E, Kostikas K. Review of Artificial Intelligence techniques in Chronic Obstructive Lung Disease. IEEE J Biomed Health Inform 2021; 26:2331-2338. [PMID: 34914601 DOI: 10.1109/jbhi.2021.3135838] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Artificial Intelligence (AI) has proven to be an invaluable asset in the healthcare domain, where massive amounts of data are produced. Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous chronic condition with multiscale manifestations and complex interactions that represents an ideal target for AI. OBJECTIVE The aim of this review article is to appraise the adoption of AI in COPD research, and more specifically its applications to date along with reported results, potential challenges and future prospects. METHODS We performed a review of the literature from PubMed and DBLP and assembled studies published up to 2020, yielding 156 articles relevant to the scope of this review. RESULTS The resulting articles were assessed and organized into four basic contextual categories, namely: i) COPD diagnosis, ii) COPD prognosis, iii) Patient classification, iv) COPD management, and subsequently presented in an orderly manner based on a set of qualitative and quantitative criteria. CONCLUSIONS We observed considerable acceleration of research activity utilizing AI techniques in COPD research, especially in the last couple of years, nevertheless, the massive production of large and complex data in COPD calls for broader adoption of AI and more advanced techniques.
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Social robots and therapeutic adherence: A new challenge in pediatric asthma? Paediatr Respir Rev 2021; 40:46-51. [PMID: 33386280 DOI: 10.1016/j.prrv.2020.11.001] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 11/19/2020] [Accepted: 11/20/2020] [Indexed: 02/06/2023]
Abstract
Social Robots are used in different contexts and, in healthcare, they are better known as Socially Assistive Robots. In the context of asthma, the use of Socially Assistive Robots has the potential to increase motivation and engagement to treatment. Other positive roles proposed for Socially Assistive Robots are to provide education, training regarding treatments, and feedback to patients. This review evaluates emerging interventions for improving treatment adherence in pediatric asthma, focusing on the possible future role of social robots in the clinical practice.
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Mariani S, Metting E, Lahr MMH, Vargiu E, Zambonelli F. Developing an ML pipeline for asthma and COPD: The case of a Dutch primary care service. INT J INTELL SYST 2021. [DOI: 10.1002/int.22568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Stefano Mariani
- Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Reggio Emilia Italy
| | - Esther Metting
- Health Technology Assessment, Department of Epidemiology, University of Groningen University Medical Center Groningen The Netherlands
| | - Maarten M. H. Lahr
- Health Technology Assessment, Department of Epidemiology, University of Groningen University Medical Center Groningen The Netherlands
| | - Eloisa Vargiu
- EURECAT Technology Centre Digital Health Unit Barcelona Spain
| | - Franco Zambonelli
- Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Reggio Emilia Italy
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Luo Q, Jiang W, Su J, Ai J, Yang C. Smoothing Complete Feature Pyramid Networks for Roll Mark Detection of Steel Strips. SENSORS 2021; 21:s21217264. [PMID: 34770572 PMCID: PMC8588222 DOI: 10.3390/s21217264] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/24/2021] [Accepted: 10/28/2021] [Indexed: 02/07/2023]
Abstract
Steel strip acts as a fundamental material for the steel industry. Surface defects threaten the steel quality and cause substantial economic and reputation losses. Roll marks, always occurring periodically in a large area, are put on the top of the list of the most serious defects by steel mills. Essentially, the online roll mark detection is a tiny target inspection task in high-resolution images captured under harsh environment. In this paper, a novel method—namely, Smoothing Complete Feature Pyramid Networks (SCFPN)—is proposed for the above focused task. In particular, the concept of complete intersection over union (CIoU) is applied in feature pyramid networks to obtain faster fitting speed and higher prediction accuracy by suppressing the vanishing gradient in training process. Furthermore, label smoothing is employed to promote the generalization ability of model. In view of lack of public surface image database of steel strips, a raw defect database of hot-rolled steel strip surface, CSU_STEEL, is opened for the first time. Experiments on two public databases (DeepPCB and NEU) and one fresh texture database (CSU_STEEL) indicate that our SCFPN yields more competitive results than several prestigious networks—including Faster R-CNN, SSD, YOLOv3, YOLOv4, FPN, DIN, DDN, and CFPN.
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Affiliation(s)
- Qiwu Luo
- School of Automation, Central South University, Changsha 430006, China; (Q.L.); (W.J.); (J.S.); (C.Y.)
| | - Weiqiang Jiang
- School of Automation, Central South University, Changsha 430006, China; (Q.L.); (W.J.); (J.S.); (C.Y.)
| | - Jiaojiao Su
- School of Automation, Central South University, Changsha 430006, China; (Q.L.); (W.J.); (J.S.); (C.Y.)
| | - Jiaqiu Ai
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
- Correspondence:
| | - Chunhua Yang
- School of Automation, Central South University, Changsha 430006, China; (Q.L.); (W.J.); (J.S.); (C.Y.)
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Ananth S, Navarra A, Vancheeswaran R. Obese, non-eosinophilic asthma: frequent exacerbators in a real-world setting. J Asthma 2021; 59:2267-2275. [PMID: 34669527 DOI: 10.1080/02770903.2021.1996598] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
OBJECTIVE In the UK, asthma deaths are at their highest level this century. Increased recognition of at-risk patients is needed. This study phenotyped frequent asthma exacerbators and used machine learning to predict frequent exacerbators. METHODS Patients admitted to a district general hospital with an asthma exacerbation between 1st March 2018 and 1st March 2020 were included. Patients were organized into two groups: "Infrequent Exacerbators" (1 admission in the previous 12 months) and "Frequent Exacerbators" (≥2 admissions in the previous 12 months). Patient data were retrospectively collected from hospital and primary care records. Machine learning models were used to predict frequent exacerbators. RESULTS 200 patients admitted for asthma exacerbations were randomly selected (73% female; mean age 47.8 years). Peripheral eosinophilia was uncommon in either group (21% vs 19%). More frequent exacerbators were being treated with high-dose ICS than infrequent exacerbators (46.5% vs 23.2%; P < 0.001), and frequent exacerbators used more SABA inhalers (10.9 vs 7.40; P = 0.01) in the year preceding the current admission. BMI was raised in both groups (34.2 vs 30.9). Logistic regression was the most accurate machine learning model for predicting frequent exacerbators (AUC = 0.80). CONCLUSIONS Patients admitted for asthma are predominately female, obese and non-eosinophilic. Patients who require multiple admissions per year have poorer asthma control at baseline. Machine learning algorithms can predict frequent exacerbators using clinical data available in primary care. Instead of simply increasing the dose of corticosteroids, multidisciplinary management targeting Th2-low inflammation should be considered for these patients.
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Affiliation(s)
- Sachin Ananth
- West Hertfordshire Hospitals NHS Trust, Watford, Hertfordshire, UK
| | - Alessio Navarra
- West Hertfordshire Hospitals NHS Trust, Watford, Hertfordshire, UK
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Alqudaihi KS, Aslam N, Khan IU, Almuhaideb AM, Alsunaidi SJ, Ibrahim NMAR, Alhaidari FA, Shaikh FS, Alsenbel YM, Alalharith DM, Alharthi HM, Alghamdi WM, Alshahrani MS. Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2021; 9:102327-102344. [PMID: 34786317 PMCID: PMC8545201 DOI: 10.1109/access.2021.3097559] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 07/09/2021] [Indexed: 06/02/2023]
Abstract
Coughing is a common symptom of several respiratory diseases. The sound and type of cough are useful features to consider when diagnosing a disease. Respiratory infections pose a significant risk to human lives worldwide as well as a significant economic downturn, particularly in countries with limited therapeutic resources. In this study we reviewed the latest proposed technologies that were used to control the impact of respiratory diseases. Artificial Intelligence (AI) is a promising technology that aids in data analysis and prediction of results, thereby ensuring people's well-being. We conveyed that the cough symptom can be reliably used by AI algorithms to detect and diagnose different types of known diseases including pneumonia, pulmonary edema, asthma, tuberculosis (TB), COVID19, pertussis, and other respiratory diseases. We also identified different techniques that produced the best results for diagnosing respiratory disease using cough samples. This study presents the most recent challenges, solutions, and opportunities in respiratory disease detection and diagnosis, allowing practitioners and researchers to develop better techniques.
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Affiliation(s)
- Kawther S. Alqudaihi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Nida Aslam
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Irfan Ullah Khan
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Abdullah M. Almuhaideb
- Department of Networks and CommunicationsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Shikah J. Alsunaidi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Nehad M. Abdel Rahman Ibrahim
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Fahd A. Alhaidari
- Department of Networks and CommunicationsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Fatema S. Shaikh
- Department of Computer Information SystemsCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Yasmine M. Alsenbel
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Dima M. Alalharith
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Hajar M. Alharthi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Wejdan M. Alghamdi
- Department of Computer ScienceCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
| | - Mohammed S. Alshahrani
- Department of Emergency MedicineCollege of MedicineImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia
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Huang D, Bai H, Wang L, Hou Y, Li L, Xia Y, Yan Z, Chen W, Chang L, Li W. The Application and Development of Deep Learning in Radiotherapy: A Systematic Review. Technol Cancer Res Treat 2021; 20:15330338211016386. [PMID: 34142614 PMCID: PMC8216350 DOI: 10.1177/15330338211016386] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.
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Affiliation(s)
- Danju Huang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Han Bai
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Li Wang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Yu Hou
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Lan Li
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Yaoxiong Xia
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Zhirui Yan
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Wenrui Chen
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Li Chang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Wenhui Li
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
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31
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Hartl D, de Luca V, Kostikova A, Laramie J, Kennedy S, Ferrero E, Siegel R, Fink M, Ahmed S, Millholland J, Schuhmacher A, Hinder M, Piali L, Roth A. Translational precision medicine: an industry perspective. J Transl Med 2021; 19:245. [PMID: 34090480 PMCID: PMC8179706 DOI: 10.1186/s12967-021-02910-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 05/25/2021] [Indexed: 02/08/2023] Open
Abstract
In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective.
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Affiliation(s)
- Dominik Hartl
- Novartis Institutes for BioMedical Research, Basel, Switzerland.
- Department of Pediatrics I, University of Tübingen, Tübingen, Germany.
| | - Valeria de Luca
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Anna Kostikova
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Jason Laramie
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Scott Kennedy
- Novartis Institutes for BioMedical Research, Cambridge, MA, USA
| | - Enrico Ferrero
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Richard Siegel
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Martin Fink
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | | | | | | | - Markus Hinder
- Novartis Institutes for BioMedical Research, Basel, Switzerland
| | - Luca Piali
- Roche Innovation Center Basel, Basel, Switzerland
| | - Adrian Roth
- Roche Innovation Center Basel, Basel, Switzerland
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32
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Affiliation(s)
- Konstantinos Kostikas
- Respiratory Medicine Department, University of Ioannina School of Medicine, Ioannina, Greece.,Respiratory Medicine Department, University Hospital of Ioannina, Ioannina, Greece
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33
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Kartoun U. Enhancing Clinical Prediction Performance by Incorporating Intuition. J Med Syst 2021; 45:57. [PMID: 33783646 DOI: 10.1007/s10916-021-01733-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 03/16/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Uri Kartoun
- Center for Computational Health, IBM Research, Cambridge, MA, USA.
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34
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Kaplan A, Cao H, FitzGerald JM, Iannotti N, Yang E, Kocks JWH, Kostikas K, Price D, Reddel HK, Tsiligianni I, Vogelmeier CF, Pfister P, Mastoridis P. Artificial Intelligence/Machine Learning in Respiratory Medicine and Potential Role in Asthma and COPD Diagnosis. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY-IN PRACTICE 2021; 9:2255-2261. [PMID: 33618053 DOI: 10.1016/j.jaip.2021.02.014] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 02/11/2021] [Accepted: 02/13/2021] [Indexed: 02/09/2023]
Abstract
Artificial intelligence (AI) and machine learning, a subset of AI, are increasingly used in medicine. AI excels at performing well-defined tasks, such as image recognition; for example, classifying skin biopsy lesions, determining diabetic retinopathy severity, and detecting brain tumors. This article provides an overview of the use of AI in medicine and particularly in respiratory medicine, where it is used to evaluate lung cancer images, diagnose fibrotic lung disease, and more recently is being developed to aid the interpretation of pulmonary function tests and the diagnosis of a range of obstructive and restrictive lung diseases. The development and validation of AI algorithms requires large volumes of well-structured data, and the algorithms must work with variable levels of data quality. It is important that clinicians understand how AI can function in the context of heterogeneous conditions such as asthma and chronic obstructive pulmonary disease where diagnostic criteria overlap, how AI use fits into everyday clinical practice, and how issues of patient safety should be addressed. AI has a clear role in providing support for doctors in the clinical workplace, but its relatively recent introduction means that confidence in its use still has to be fully established. Overall, AI is expected to play a key role in aiding clinicians in the diagnosis and management of respiratory diseases in the future, and it will be exciting to see the benefits that arise for patients and doctors from its use in everyday clinical practice.
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Affiliation(s)
- Alan Kaplan
- Family Physician Airways Group of Canada, University of Toronto, Toronto, Canada.
| | - Hui Cao
- Novartis Pharmaceuticals Corporation, East Hanover, NJ
| | - J Mark FitzGerald
- Division of Respiratory Medicine, Department of Medicine, The University of British Columbia, Vancouver, BC, Canada
| | - Nick Iannotti
- Novartis Institutes for Biomedical Research, Cambridge, Mass
| | - Eric Yang
- Novartis Institutes for Biomedical Research, Cambridge, Mass
| | - Janwillem W H Kocks
- General Practitioners Research Institute, Groningen, The Netherlands; University of Groningen, University Medical Center Groningen, GRIAC Research Institute, Groningen, The Netherlands; Observational and Pragmatic Research Institute, Singapore, Singapore
| | - Konstantinos Kostikas
- Respiratory Medicine Department, University of Ioannina School of Medicine, Ioannina, Greece
| | - David Price
- Observational and Pragmatic Research Institute, Singapore, Singapore; Centre of Academic Primary Care, Division of Applied Health Sciences, University of Aberdeen, Aberdeen, United Kingdom
| | - Helen K Reddel
- Woolcock Institute of Medical Research, University of Sydney, Sydney, NSW, Australia
| | - Ioanna Tsiligianni
- Department of Social Medicine, Faculty of Medicine, University of Crete, Heraklion, Greece
| | - Claus F Vogelmeier
- Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Philipps-Universität Marburg, Member of the German Center for Lung Research (DZL), Marburg, Germany
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35
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Feng Y, Wang Y, Zeng C, Mao H. Artificial Intelligence and Machine Learning in Chronic Airway Diseases: Focus on Asthma and Chronic Obstructive Pulmonary Disease. Int J Med Sci 2021; 18:2871-2889. [PMID: 34220314 PMCID: PMC8241767 DOI: 10.7150/ijms.58191] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 05/20/2021] [Indexed: 02/05/2023] Open
Abstract
Chronic airway diseases are characterized by airway inflammation, obstruction, and remodeling and show high prevalence, especially in developing countries. Among them, asthma and chronic obstructive pulmonary disease (COPD) show the highest morbidity and socioeconomic burden worldwide. Although there are extensive guidelines for the prevention, early diagnosis, and rational treatment of these lifelong diseases, their value in precision medicine is very limited. Artificial intelligence (AI) and machine learning (ML) techniques have emerged as effective methods for mining and integrating large-scale, heterogeneous medical data for clinical practice, and several AI and ML methods have recently been applied to asthma and COPD. However, very few methods have significantly contributed to clinical practice. Here, we review four aspects of AI and ML implementation in asthma and COPD to summarize existing knowledge and indicate future steps required for the safe and effective application of AI and ML tools by clinicians.
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Affiliation(s)
- Yinhe Feng
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China.,Department of Respiratory and Critical Care Medicine, People's Hospital of Deyang City, Affiliated Hospital of Chengdu College of Medicine, Deyang, Sichuan Province, China
| | - Yubin Wang
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
| | - Chunfang Zeng
- Department of Respiratory and Critical Care Medicine, People's Hospital of Deyang City, Affiliated Hospital of Chengdu College of Medicine, Deyang, Sichuan Province, China
| | - Hui Mao
- Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan Province, China
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