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Moreno Mendez R, Marín A, Ferrando JR, Rissi Castro G, Cepeda Madrigal S, Agostini G, Catalan Serra P. Artificial Intelligence Applied to Forced Spirometry in Primary Care. OPEN RESPIRATORY ARCHIVES 2024; 6:100313. [PMID: 38828405 PMCID: PMC11137334 DOI: 10.1016/j.opresp.2024.100313] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 02/19/2024] [Indexed: 06/05/2024] Open
Abstract
Introduction This study aims to create an artificial intelligence (AI) based machine learning (ML) model capable of predicting a spirometric obstructive pattern using variables with the highest predictive power derived from an active case-finding program for COPD in primary care. Material and methods A total of 1190 smokers, aged 30-80 years old with no prior history of respiratory disease, underwent spirometry with bronchodilation. The sample was analyzed using AI tools. Based on an exploratory data analysis (EDA), independent variables (according to mutual information analysis) were trained using a gradient boosting algorithm (GBT) and validated through cross-validation. Results With an area under the curve close to unity, the model predicted a spirometric obstructive pattern using variables with the highest predictive power: FEV1_theoretical_pre values. Sensitivity: 93%. Positive predictive value: 94%. Specificity: 97%. Negative predictive value: 96%. Accuracy: 95%. Precision: 94%. Conclusion An ML model can predict the presence of an obstructive pattern in spirometry in a primary care smoking population with no prior diagnosis of respiratory disease using the FEV1_theoretical_pre values with an accuracy and precision exceeding 90%. Further studies including clinical data and strategies for integrating AI into clinical workflow are needed.
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Affiliation(s)
| | | | | | | | | | - Gabriela Agostini
- Otolaryngology Department, Vida Clinic, Santa Cruz de Tenerife, Spain
| | - Pablo Catalan Serra
- Department of Internal Medicine, Kristiansund Hospital, Møre og Romsdal, Norway
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Brems JH, McCormack MC. Testing the Limits: Alternative Definitions of Spirometry Thresholds. Ann Am Thorac Soc 2024; 21:1247-1248. [PMID: 39212423 PMCID: PMC11376351 DOI: 10.1513/annalsats.202405-555ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024] Open
Affiliation(s)
- J Henry Brems
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Meredith C McCormack
- Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
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Ismail MK, Araki T, Gefter WB, Suzuki Y, Raevsky A, Saleh A, Yusuf S, Marquis A, Alcudia A, Duncan I, Schaubel DE, Cantu E, Rizi R. AI-Driven Automated Lung Sizing from Chest Radiographs. Am J Transplant 2024:S1600-6135(24)00524-0. [PMID: 39182615 DOI: 10.1016/j.ajt.2024.08.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Revised: 08/13/2024] [Accepted: 08/14/2024] [Indexed: 08/27/2024]
Abstract
Lung size measurements play an important role in transplantation, as optimal donor-recipient size matching is necessary to ensure the best possible outcome. While several strategies for size matching are currently used, all have limitations, and none has proven superior. In this pilot study, we leveraged deep learning and computer vision to develop an automated system for generating standardized lung size measurements using portable chest radiographs to improve accuracy, reduce variability, and streamline donor/recipient matching. We developed a two-step framework involving lung mask extraction from chest radiographs followed by feature points detection to generate six distinct lung height and width measurements, which we validated against measurements reported by two radiologists for 50 lung transplant recipients. Our system demonstrated <2.5% error (< 7 mm) with robust inter- and intra-rater agreement compared to expert radiologist review. This is especially promising given that the radiographs used in this study were purposely chosen to include images with technical challenges such as consolidations, effusions, and patient rotation. While validation in a larger cohort is necessary, this study highlights AI's potential to both provide reproducible lung size assessment in real patients and enable studies on the effect of lung size matching on transplant outcomes in large datasets.
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Affiliation(s)
- Mostafa K Ismail
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Tetsuro Araki
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02130
| | - Warren B Gefter
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Yoshikazu Suzuki
- Department of Surgery, Division of Cardiovascular Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia
| | - Allie Raevsky
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02130
| | - Aya Saleh
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02130
| | - Sophia Yusuf
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02130
| | - Abigail Marquis
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02130
| | - Alyster Alcudia
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02130
| | - Ian Duncan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
| | - Douglas E Schaubel
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104
| | - Edward Cantu
- Department of Surgery, Division of Cardiovascular Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia.
| | - Rahim Rizi
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104
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Xu WJ, Shang WY, Feng JM, Song XY, Li LY, Xie XP, Wang YM, Liang BM. Machine learning for accurate detection of small airway dysfunction-related respiratory changes: an observational study. Respir Res 2024; 25:286. [PMID: 39048993 PMCID: PMC11270925 DOI: 10.1186/s12931-024-02911-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 07/08/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND The use of machine learning(ML) methods would improve the diagnosis of small airway dysfunction(SAD) in subjects with chronic respiratory symptoms and preserved pulmonary function(PPF). This paper evaluated the performance of several ML algorithms associated with the impulse oscillometry(IOS) analysis to aid in the diagnostic of respiratory changes in SAD. We also find out the best configuration for this task. METHODS IOS and spirometry were measured in 280 subjects, including a healthy control group (n = 78), a group with normal spirometry (n = 158) and a group with abnormal spirometry (n = 44). Various supervised machine learning (ML) algorithms and feature selection strategies were examined, such as Support Vector Machines (SVM), Random Forests (RF), Adaptive Boosting (ADABOOST), Navie Bayesian (BAYES), and K-Nearest Neighbors (KNN). RESULTS The first experiment of this study demonstrated that the best oscillometric parameter (BOP) was R5, with an AUC value of 0.642, when comparing a healthy control group(CG) with patients in the group without lung volume-defined SAD(PPFN). The AUC value of BOP in the control group was 0.769 compared with patients with spirometry defined SAD(PPFA) in the PPF population. In the second experiment, the ML technique was used. In CGvsPPFN, RF and ADABOOST had the best diagnostic results (AUC = 0.914, 0.915), with significantly higher accuracy compared to BOP (p < 0.01). In CGvsPPFA, RF and ADABOOST had the best diagnostic results (AUC = 0.951, 0.971) and significantly higher diagnostic accuracy (p < 0.01). In the third, fourth and fifth experiments, different feature selection techniques allowed us to find the best IOS parameters (R5, (R5-R20)/R5 and Fres). The results demonstrate that the performance of ADABOOST remained essentially unaltered following the application of the feature selector, whereas the diagnostic accuracy of the remaining four classifiers (RF, SVM, BAYES, and KNN) is marginally enhanced. CONCLUSIONS IOS combined with ML algorithms provide a new method for diagnosing SAD in subjects with chronic respiratory symptoms and PPF. The present study's findings provide evidence that this combination may help in the early diagnosis of respiratory changes in these patients.
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Affiliation(s)
- Wen-Jing Xu
- Department of Respiratory and Critical Care Medicine, West China School of Medicine and West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Wen-Yi Shang
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, China
| | - Jia-Ming Feng
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, P. R. China
| | - Xin-Yue Song
- Department of Respiratory and Critical Care Medicine, West China School of Medicine and West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Liang-Yuan Li
- Department of Respiratory and Critical Care Medicine, West China School of Medicine and West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China
| | - Xin-Peng Xie
- College of Electrical Engineering and Automation, Sichuan University, Chengdu, 610065, China
| | - Yan-Mei Wang
- Institute of Traditional Chinese Medicine of Sichuan Academy of Chinese Medicine Sciences(Sichuan Second Hospital of T.C.M), Chengdu, 610000, China
| | - Bin-Miao Liang
- Department of Respiratory and Critical Care Medicine, West China School of Medicine and West China Hospital, Sichuan University, No. 37 Guoxue Alley, Chengdu, 610041, China.
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Astărăstoae V, Rogozea LM, Leaşu F, Ioan BG. Ethical Dilemmas of Using Artificial Intelligence in Medicine. Am J Ther 2024; 31:e388-e397. [PMID: 38662923 DOI: 10.1097/mjt.0000000000001693] [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: 07/09/2024]
Abstract
BACKGROUND Artificial intelligence (AI) is considered the fourth industrial revolution that will change the evolution of humanity technically and relationally. Although the term has been around since 1956, it has only recently become apparent that AI can revolutionize technologies and has many applications in the medical field. AREAS OF UNCERTAINTY The ethical dilemmas posed by the use of AI in medicine revolve around issues related to informed consent, respect for confidentiality, protection of personal data, and last but not least the accuracy of the information it uses. DATA SOURCES A literature search was conducted through PubMed, MEDLINE, Plus, Scopus, and Web of Science (2015-2022) using combinations of keywords, including: AI, future in medicine, and machine learning plus ethical dilemma. ETHICS AND THERAPEUTIC ADVANCES The ethical analysis of the issues raised by AI used in medicine must mainly address nonmaleficence and beneficence, both in correlation with patient safety risks, ability versus inability to detect correct information from inadequate or even incorrect information. The development of AI tools that can support medical practice can increase people's access to medical information, to obtain a second opinion, for example, but it is also a source of concern among health care professionals and especially bioethicists about how confidentiality is maintained and how to maintain cybersecurity. Another major risk may be related to the dehumanization of the medical act, given that, at least for now, empathy and compassion are accessible only to human beings. CONCLUSIONS AI has not yet managed to overcome certain limits, lacking moral subjectivity, empathy, the level of critical thinking is still insufficient, but no matter who will practice preventive or curative medicine in the next period, they will not be able to ignore AI, which under human control can be an important tool in medical practice.
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Affiliation(s)
- Vasile Astărăstoae
- Faculty of Medicine, Grigore T Popa University of Medicine & Pharmacy, Iasi, Romania; and
| | - Liliana M Rogozea
- Basic, Preventive and Clinical Sciences Department, Transilvania University, Brasov, Romania
| | - Florin Leaşu
- Basic, Preventive and Clinical Sciences Department, Transilvania University, Brasov, Romania
| | - Beatrice Gabriela Ioan
- Faculty of Medicine, Grigore T Popa University of Medicine & Pharmacy, Iasi, Romania; and
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Doe G, El-Emir E, Edwards GD, Topalovic M, Evans RA, Russell R, Sylvester KP, Van Orshoven K, Sunjaya AP, Scott DA, Prevost AT, Harvey J, Taylor SJ, Hopkinson NS, Kon SS, Jarrold I, Spain N, Banya W, Man WDC. Comparing performance of primary care clinicians in the interpretation of SPIROmetry with or without Artificial Intelligence Decision support software (SPIRO-AID): a protocol for a randomised controlled trial. BMJ Open 2024; 14:e086736. [PMID: 38950987 PMCID: PMC11218008 DOI: 10.1136/bmjopen-2024-086736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Accepted: 06/12/2024] [Indexed: 07/03/2024] Open
Abstract
INTRODUCTION Spirometry is a point-of-care lung function test that helps support the diagnosis and monitoring of chronic lung disease. The quality and interpretation accuracy of spirometry is variable in primary care. This study aims to evaluate whether artificial intelligence (AI) decision support software improves the performance of primary care clinicians in the interpretation of spirometry, against reference standard (expert interpretation). METHODS AND ANALYSIS A parallel, two-group, statistician-blinded, randomised controlled trial of primary care clinicians in the UK, who refer for, or interpret, spirometry. People with specialist training in respiratory medicine to consultant level were excluded. A minimum target of 228 primary care clinician participants will be randomised with a 1:1 allocation to assess fifty de-identified, real-world patient spirometry sessions through an online platform either with (intervention group) or without (control group) AI decision support software report. Outcomes will cover primary care clinicians' spirometry interpretation performance including measures of technical quality assessment, spirometry pattern recognition and diagnostic prediction, compared with reference standard. Clinicians' self-rated confidence in spirometry interpretation will also be evaluated. The primary outcome is the proportion of the 50 spirometry sessions where the participant's preferred diagnosis matches the reference diagnosis. Unpaired t-tests and analysis of covariance will be used to estimate the difference in primary outcome between intervention and control groups. ETHICS AND DISSEMINATION This study has been reviewed and given favourable opinion by Health Research Authority Wales (reference: 22/HRA/5023). Results will be submitted for publication in peer-reviewed journals, presented at relevant national and international conferences, disseminated through social media, patient and public routes and directly shared with stakeholders. TRIAL REGISTRATION NUMBER NCT05933694.
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Affiliation(s)
- Gillian Doe
- NIHR Biomedical Research Centre, University of Leicester, Leicester, UK
| | - Ethaar El-Emir
- Harefield Respiratory Research Group, Royal Brompton & Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, UK, London, UK
| | - George D Edwards
- Harefield Respiratory Research Group, Royal Brompton & Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, UK, London, UK
| | | | - Rachael A Evans
- NIHR Biomedical Research Centre, University of Leicester, Leicester, UK
| | - Richard Russell
- Kings Centre for Lung Health, King's College London, London, UK
| | - Karl P Sylvester
- Cambridge Respiratory Physiology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | | | - Anthony P Sunjaya
- Harefield Respiratory Research Group, Royal Brompton & Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, UK, London, UK
- The George Institute for Global Health, Sydney, New South Wales, Australia
| | - David A Scott
- Southampton Health Technology Assessments Centre, University of Southampton, Southampton, UK
| | - A Toby Prevost
- Nightingale-Saunders Clinical Trials and Epidemiology Unit, King's College London, London, UK
| | - Jennifer Harvey
- Harefield Respiratory Research Group, Royal Brompton & Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, UK, London, UK
| | - Stephanie Jc Taylor
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Samantha S Kon
- Harefield Respiratory Research Group, Royal Brompton & Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, UK, London, UK
- Department of Respiratory Medicine, Hillingdon Hospitals NHS Foundation Trust, Uxbridge, UK
| | | | | | - Winston Banya
- Harefield Respiratory Research Group, Royal Brompton & Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, UK, London, UK
- Medical Statistics, Research & Development, Royal Brompton & Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - William D-C Man
- Harefield Respiratory Research Group, Royal Brompton & Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust, UK, London, UK
- Kings Centre for Lung Health, King's College London, London, UK
- National Heart & Lung Institute, Imperial College London, UK, London, UK
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Imtiaz A, King J, Holmes S, Gupta A, Bafadhel M, Melcher ML, Hurst JR, Farewell D, Bolton CE, Duckers J. ChatGPT versus Bing: a clinician assessment of the accuracy of AI platforms when responding to COPD questions. Eur Respir J 2024; 63:2400163. [PMID: 38811043 DOI: 10.1183/13993003.00163-2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/30/2024] [Indexed: 05/31/2024]
Affiliation(s)
| | - Joanne King
- Respiratory Department, Frimley Health NHS Foundation Trust, Frimley, UK
| | - Steve Holmes
- General Practitioner, The Park Medical Practice, Shepton Mallet, UK
| | - Ayushman Gupta
- Respiratory Department, Nottingham University Hospital NHS Trust, Nottingham, UK
| | - Mona Bafadhel
- King's Centre for Lung Health, School of Immunology and Microbial Sciences, King's College London, London, UK
| | - Marc L Melcher
- Department of Surgery, Stanford University, Stanford, CA, USA
| | - John R Hurst
- UCL Respiratory, University College London, London, UK
| | - Daniel Farewell
- Division of Population Medicine, School of Medicine, Cardiff University, Cardiff, UK
| | - Charlotte E Bolton
- Centre for Respiratory Research, NIHR Nottingham Biomedical Research Centre, School of Medicine, University of Nottingham, Nottingham, UK
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Yang X. Application and Prospects of Artificial Intelligence Technology in Early Screening of Chronic Obstructive Pulmonary Disease at Primary Healthcare Institutions in China. Int J Chron Obstruct Pulmon Dis 2024; 19:1061-1067. [PMID: 38765765 PMCID: PMC11102166 DOI: 10.2147/copd.s458935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 04/25/2024] [Indexed: 05/22/2024] Open
Abstract
Chronic Obstructive Pulmonary Disease (COPD), as one of the major global health threat diseases, particularly in China, presents a high prevalence and mortality rate. Early diagnosis is crucial for controlling disease progression and improving patient prognosis. However, due to the lack of significant early symptoms, the awareness and diagnosis rates of COPD remain low. Against this background, primary healthcare institutions play a key role in identifying high-risk groups and early diagnosis. With the development of Artificial Intelligence (AI) technology, its potential in enhancing the efficiency and accuracy of COPD screening is evident. This paper discusses the characteristics of high-risk groups for COPD, current screening methods, and the application of AI technology in various aspects of screening. It also highlights challenges in AI application, such as data privacy, algorithm accuracy, and interpretability. Suggestions for improvement, such as enhancing AI technology dissemination, improving data quality, promoting interdisciplinary cooperation, and strengthening policy and financial support, aim to further enhance the effectiveness and prospects of AI technology in COPD screening at primary healthcare institutions in China.
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Affiliation(s)
- Xu Yang
- Department of General Practice, Donghuashi Community Health Service Center, Beijing, People’s Republic of China
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Isangula KG, Haule RJ. Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania. JMIR Res Protoc 2024; 13:e54388. [PMID: 38652526 PMCID: PMC11077412 DOI: 10.2196/54388] [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: 11/08/2023] [Revised: 02/14/2024] [Accepted: 02/21/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. OBJECTIVE This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. METHODS This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values. RESULTS This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns. CONCLUSIONS Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/54388.
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Affiliation(s)
- Kahabi Ganka Isangula
- School of Nursing and Midwifery, Aga Khan University, Dar Es Salaam, United Republic of Tanzania
| | - Rogers John Haule
- School of Nursing and Midwifery, Aga Khan University, Dar Es Salaam, United Republic of Tanzania
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Huang CH, Chou KT, Perng DW, Hsiao YH, Huang CW. Using Machine Learning with Impulse Oscillometry Data to Develop a Predictive Model for Chronic Obstructive Pulmonary Disease and Asthma. J Pers Med 2024; 14:398. [PMID: 38673025 PMCID: PMC11051459 DOI: 10.3390/jpm14040398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 04/04/2024] [Accepted: 04/05/2024] [Indexed: 04/28/2024] Open
Abstract
We aimed to develop and validate a machine learning model using impulse oscillometry system (IOS) profiles for accurately classifying patients into three assessment-based categories: no airflow obstruction, asthma, and chronic obstructive pulmonary disease (COPD). Our research questions were as follows: (1) Can machine learning methods accurately classify obstructive disease states based solely on multidimensional IOS data? (2) Which IOS parameters and modeling algorithms provide the best discrimination? We used data for 480 patients (240 with COPD and 240 with asthma) and 84 healthy individuals for training. Physiological and IOS parameters were combined into six feature combinations. The classification algorithms tested were logistic regression, random forest, neural network, k-nearest neighbor, and support vector machine. The optimal feature combination for identifying individuals without pulmonary obstruction, with asthma, or with COPD included 15 IOS and physiological features. The neural network classifier achieved the highest accuracy (0.786). For discriminating between healthy and unhealthy individuals, two combinations of twenty-three features performed best in the neural network algorithm (accuracy of 0.929). When distinguishing COPD from asthma, the best combination included 15 features and the neural network algorithm achieved an accuracy of 0.854. This study provides compelling technical evidence and clinical justifications for advancing IOS data-driven models to aid in COPD and asthma management.
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Affiliation(s)
- Chien-Hua Huang
- Department of Eldercare, College of Nursing, Central Taiwan University of Science and Technology, Taichung 406053, Taiwan;
| | - Kun-Ta Chou
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112201, Taiwan; (K.-T.C.); (D.-W.P.); (Y.-H.H.)
- Faculty of Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Diahn-Warng Perng
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112201, Taiwan; (K.-T.C.); (D.-W.P.); (Y.-H.H.)
- Faculty of Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Yi-Han Hsiao
- Department of Chest Medicine, Taipei Veterans General Hospital, Taipei 112201, Taiwan; (K.-T.C.); (D.-W.P.); (Y.-H.H.)
- Faculty of Medicine, School of Medicine, National Yang-Ming Chiao Tung University, Taipei 112304, Taiwan
| | - Chien-Wen Huang
- Division of Chest Medicine, Department of Internal Medicine, Asia University Hospital, Taichung 413505, Taiwan
- Department of Medical Laboratory Science and Biotechnology, College of Medical and Health Science, Asia University, Taichung 413305, Taiwan
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Kraemer R, Baty F, Smith HJ, Minder S, Gallati S, Brutsche MH, Matthys H. Assessment of functional diversities in patients with Asthma, COPD, Asthma-COPD overlap, and Cystic Fibrosis (CF). PLoS One 2024; 19:e0292270. [PMID: 38377145 PMCID: PMC10878531 DOI: 10.1371/journal.pone.0292270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/17/2023] [Indexed: 02/22/2024] Open
Abstract
The objectives of the present study were to evaluate the discriminating power of spirometric and plethysmographic lung function parameters to differenciate the diagnosis of asthma, ACO, COPD, and to define functional characteristics for more precise classification of obstructive lung diseases. From the databases of 4 centers, a total of 756 lung function tests (194 healthy subjects, 175 with asthma, 71 with ACO, 78 with COPD and 238 with CF) were collected, and gradients among combinations of target parameters from spirometry (forced expiratory volume one second: FEV1; FEV1/forced vital capacity: FEV1/FVC; forced expiratory flow between 25-75% FVC: FEF25-75), and plethysmography (effective, resistive airway resistance: sReff; aerodynamic work of breathing at rest: sWOB), separately for in- and expiration (sReffIN, sReffEX, sWOBin, sWOBex) as well as static lung volumes (total lung capacity: TLC; functional residual capacity: FRCpleth; residual volume: RV), the control of breathing (mouth occlusion pressure: P0.1; mean inspiratory flow: VT/TI; the inspiratory to total time ratio: TI/Ttot) and the inspiratory impedance (Zinpleth = P0.1/VT/TI) were explored. Linear discriminant analyses (LDA) were applied to identify discriminant functions and classification rules using recursive partitioning decision trees. LDA showed a high classification accuracy (sensitivity and specificity > 90%) for healthy subjects, COPD and CF. The accuracy dropped for asthma (~70%) and even more for ACO (~60%). The decision tree revealed that P0.1, sRtot, and VT/TI differentiate most between healthy and asthma (68.9%), COPD (82.1%), and CF (60.6%). Moreover, using sWOBex and Zinpleth ACO can be discriminated from asthma and COPD (60%). Thus, the functional complexity of obstructive lung diseases can be understood, if specific spirometric and plethysmographic parameters are used. Moreover, the newly described parameters of airway dynamics and the central control of breathing including Zinpleth may well serve as promising functional marker in the field of precision medicine.
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Affiliation(s)
- Richard Kraemer
- Centre of Pulmonary Medicine, Hirslanden Hospital Group, Salem-Hospital, Bern, Switzerland
- Department of Paediatrics, University of Bern, Bern, Switzerland
- School of Biomedical and Precision Engineering (SBPE), University of Bern, Bern, Switzerland
| | - Florent Baty
- Department of Pneumology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Hans-Jürgen Smith
- Medical Development, Research in Respiratory Diagnostics, Berlin, Germany
| | - Stefan Minder
- Centre of Pulmonary Medicine, Hirslanden Hospital Group, Salem-Hospital, Bern, Switzerland
| | - Sabina Gallati
- Department of Paediatrics, University of Bern, Bern, Switzerland
- Hirslanden Precise, Genomic Medicine, Hirslanden Hospital Group, Zollikon/Zürich, Switzerland
| | - Martin H. Brutsche
- Department of Pneumology, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Heinrich Matthys
- Department of Pneumology, University Hospital of Freiburg, Freiburg, Germany
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12
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Vidal-Alaball J, Panadés Zafra R, Escalé-Besa A, Martinez-Millana A. The artificial intelligence revolution in primary care: Challenges, dilemmas and opportunities. Aten Primaria 2024; 56:102820. [PMID: 38056048 PMCID: PMC10714322 DOI: 10.1016/j.aprim.2023.102820] [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: 11/06/2023] [Accepted: 11/08/2023] [Indexed: 12/08/2023] Open
Abstract
Artificial intelligence (AI) can be a valuable tool for primary care (PC), as, among other things, it can help healthcare professionals improve diagnostic accuracy, chronic disease management and the overall efficiency of the care they provide. It is important to emphasise that AI should not be seen as a replacement tool, but as an aid to PC professionals. Although AI is capable of processing large amounts of data and generating accurate predictions, it cannot replace the skill and expertise of professionals in clinical decision making. AI still requires the interpretation and clinical judgement of a trained healthcare professional and cannot provide the empathy and emotional support often required in healthcare.
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Affiliation(s)
- Josep Vidal-Alaball
- Unitat de Suport a la Recerca de la Catalunya Central, Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina, Sant Fruitós de Bages, Barcelona, Spain; Grup de Recerca Promoció de la Salut en l'Àmbit Rural, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Barcelona, Spain; Facultat de Medicina, Universitat de Vic-Universitat Central de Catalunya, Vic, Barcelona, Spain; Grup de Salut Digital CAMFIC, Barcelona, Spain
| | - Robert Panadés Zafra
- Grup de Recerca Promoció de la Salut en l'Àmbit Rural, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Barcelona, Spain; Grup de Salut Digital CAMFIC, Barcelona, Spain; Equip d'Atenció Primària d'Anoia Rural, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Jorba i Copons, Barcelona, Spain
| | - Anna Escalé-Besa
- Grup de Recerca Promoció de la Salut en l'Àmbit Rural, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Sant Fruitós de Bages, Barcelona, Spain; Grup de Salut Digital CAMFIC, Barcelona, Spain; Equip d'Atenció Primària Navàs-Balsareny, Gerència d'Atenció Primària i a la Comunitat de Catalunya Central, Institut Català de la Salut, Navàs, Barcelona, Spain.
| | - Antonio Martinez-Millana
- Grup de Salut Digital CAMFIC, Barcelona, Spain; Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas, Universitat Politècnica de València, Valencia, Spain
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13
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Akyüz K, Cano Abadía M, Goisauf M, Mayrhofer MT. Unlocking the potential of big data and AI in medicine: insights from biobanking. Front Med (Lausanne) 2024; 11:1336588. [PMID: 38357641 PMCID: PMC10864616 DOI: 10.3389/fmed.2024.1336588] [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: 11/11/2023] [Accepted: 01/19/2024] [Indexed: 02/16/2024] Open
Abstract
Big data and artificial intelligence are key elements in the medical field as they are expected to improve accuracy and efficiency in diagnosis and treatment, particularly in identifying biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. These applications belong to current research practice that is data-intensive. While the combination of imaging, pathological, genomic, and clinical data is needed to train algorithms to realize the full potential of these technologies, biobanks often serve as crucial infrastructures for data-sharing and data flows. In this paper, we argue that the 'data turn' in the life sciences has increasingly re-structured major infrastructures, which often were created for biological samples and associated data, as predominantly data infrastructures. These have evolved and diversified over time in terms of tackling relevant issues such as harmonization and standardization, but also consent practices and risk assessment. In line with the datafication, an increased use of AI-based technologies marks the current developments at the forefront of the big data research in life science and medicine that engender new issues and concerns along with opportunities. At a time when secure health data environments, such as European Health Data Space, are in the making, we argue that such meta-infrastructures can benefit both from the experience and evolution of biobanking, but also the current state of affairs in AI in medicine, regarding good governance, the social aspects and practices, as well as critical thinking about data practices, which can contribute to trustworthiness of such meta-infrastructures.
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Affiliation(s)
- Kaya Akyüz
- Department of ELSI Services and Research, BBMRI-ERIC, Graz, Austria
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14
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Huang YCT, Henriquez L, Chen H, Henriquez C. Development and evaluation of a computerized algorithm for the interpretation of pulmonary function tests. PLoS One 2024; 19:e0297519. [PMID: 38285673 PMCID: PMC10824436 DOI: 10.1371/journal.pone.0297519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 01/07/2024] [Indexed: 01/31/2024] Open
Abstract
Pulmonary function tests (PFTs) are usually interpreted by clinicians using rule-based strategies and pattern recognition. The interpretation, however, has variabilities due to patient and interpreter errors. Most PFTs have recognizable patterns that can be categorized into specific physiological defects. In this study, we developed a computerized algorithm using the python package (pdfplumber) and validated against clinicians' interpretation. We downloaded PFT reports in the electronic medical record system that were in PDF format. We digitized the flow volume loop (FVL) and extracted numeric values from the reports. The algorithm used FEV1/FVC<0.7 for obstruction, TLC<80%pred for restriction and <80% or >120%pred for abnormal DLCO. The algorithm also used a small airway disease index (SADI) to quantify late expiratory flattening of the FVL to assess small airway dysfunction. We devised keywords for the python Natural Language Processing (NLP) package (spaCy) to identify obstruction, restriction, abnormal DLCO and small airway dysfunction in the reports. The algorithm was compared to clinicians' interpretation in 6,889 PFTs done between March 1st, 2018, and September 30th, 2020. The agreement rates (Cohen's kappa) for obstruction, restriction and abnormal DLCO were 94.4% (0.868), 99.0% (0.979) and 87.9% (0.750) respectively. In 4,711 PFTs with FEV1/FVC≥0.7, the algorithm identified 190 tests with SADI < lower limit of normal (LLN), suggesting small airway dysfunction. Of these, the clinicians (67.9%) also flagged 129 tests. When SADI was ≥ LLN, no clinician's reports indicated small airway dysfunction. Our results showed the computerized algorithm agreed with clinicians' interpretation in approximately 90% of the tests and provided a sensitive objective measure for assessing small airway dysfunction. The algorithm can improve efficiency and consistency and decrease human errors in PFT interpretation. The computerized algorithm works directly on PFT reports in PDF format and can be adapted to incorporate a different interpretation strategy and platform.
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Affiliation(s)
- Yuh-Chin T. Huang
- Department of Medicine, Duke University Medical Center, Durham, NC, United States of America
| | - Luke Henriquez
- Department of Cognitive Science, Case Western University, Cleveland, OH, United States of America
| | - Hengji Chen
- Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC, United States of America
| | - Craig Henriquez
- Department of Biomedical Engineering, Pratt School of Engineering, Duke University, Durham, NC, United States of America
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15
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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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16
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Amin A, Cardoso SA, Suyambu J, Abdus Saboor H, Cardoso RP, Husnain A, Isaac NV, Backing H, Mehmood D, Mehmood M, Maslamani ANJ. Future of Artificial Intelligence in Surgery: A Narrative Review. Cureus 2024; 16:e51631. [PMID: 38318552 PMCID: PMC10839429 DOI: 10.7759/cureus.51631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/03/2024] [Indexed: 02/07/2024] Open
Abstract
Artificial intelligence (AI) is the capability of a machine to execute cognitive processes that are typically considered to be functions of the human brain. It is the study of algorithms that enable machines to reason and perform mental tasks, including problem-solving, object and word recognition, and decision-making. Once considered science fiction, AI today is a fact and an increasingly prevalent subject in both academic and popular literature. It is expected to reshape medicine, benefiting both healthcare professionals and patients. Machine learning (ML) is a subset of AI that allows machines to learn and make predictions by recognizing patterns, thus empowering the medical team to deliver better care to patients through accurate diagnosis and treatment. ML is expanding its footprint in a variety of surgical specialties, including general surgery, ophthalmology, cardiothoracic surgery, and vascular surgery, to name a few. In recent years, we have seen AI make its way into the operating theatres. Though it has not yet been able to replace the surgeon, it has the potential to become a highly valuable surgical tool. Rest assured that the day is not far off when AI shall play a significant intraoperative role, a projection that is currently marred by safety concerns. This review aims to explore the present application of AI in various surgical disciplines and how it benefits both patients and physicians, as well as the current obstacles and limitations facing its seemingly unstoppable rise.
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Affiliation(s)
- Aamir Amin
- Cardiothoracic Surgery, Harefield Hospital, Guy's and St Thomas' NHS Foundation Trust, London, GBR
| | - Swizel Ann Cardoso
- Major Trauma Services, University Hospital Birmingham NHS Foundation Trust DC, Birmingham, GBR
| | - Jenisha Suyambu
- Medicine, University of Perpetual Help System Data - Jonelta Foundation School of Medicine, Las Piñas, PHL
| | | | - Rayner P Cardoso
- Medicine and Surgery, All India Institute of Medical Sciences, Jodhpur, Jodhpur, IND
| | - Ali Husnain
- Radiology, Northwestern University, Lahore, PAK
| | - Natasha Varghese Isaac
- Medicine and Surgery, St John's Medical College Hospital, Rajiv Gandhi University of Health Sciences, Bengaluru, IND
| | - Haydee Backing
- Medicine, Universidad de San Martin de Porres, Lima, PER
| | - Dalia Mehmood
- Community Medicine, Fatima Jinnah Medical University, Lahore, PAK
| | - Maria Mehmood
- Internal Medicine, Shalamar Medical and Dental College, Lahore, PAK
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17
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Savic-Pesic D, Chamorro N, Lopez-Rodriguez V, Daniel-Diez J, Torres Creixenti A, El Mesnaoui MI, Benavides Navas VK, Castellanos Cotte JD, Abellan Cano I, Da Costa Azevedo FA, Trenza Peñas M, Voelcker-Sala I, Villalobos F, Satue-Gracia EM, Martin-Lujan F. Validity of the Espiro Mobile Application in the Interpretation of Spirometric Patterns: An App Accuracy Study. Diagnostics (Basel) 2023; 14:29. [PMID: 38201338 PMCID: PMC10795716 DOI: 10.3390/diagnostics14010029] [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: 11/14/2023] [Revised: 12/13/2023] [Accepted: 12/14/2023] [Indexed: 01/12/2024] Open
Abstract
Spirometry is a pulmonary function test where correct interpretation of the results is crucial for accurate diagnosis of disease. There are online tools to assist in the interpretation of spirometry results; however, as yet none are validated. We evaluated the interpretation accuracy of the Espiro app using pulmonologist interpretations as the gold standard. This is an observational descriptive study in which 118 spirometry results were interpreted by the Espiro app, two pulmonologists, two primary care physicians, and two residents of a primary care training program. We determined the interpretation accuracy of the Espiro app and the concordance of the pattern and severity interpretation between the Espiro app and each of the observers using Cohen's kappa coefficient (k). We obtained a sensitivity and specificity for the Espiro app of 97.5% (95% confidence interval (CI): 86.8-99.9%) and 94.9% (95%CI: 87.4-98.6%) with pulmonologist 1 and 100% (95%CI: 91.6-100%) and 98.7% (95%CI: 92.9-99.9%) with pulmonologist 2. The concordance for the pattern interpretation was greater than k 0.907, representing almost perfect agreement. The concordance of the severity interpretation was greater than k 0.807, representing substantial to almost perfect agreement. We concluded that the Espiro app is a valid tool for spirometry interpretation.
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Affiliation(s)
- Darinka Savic-Pesic
- Camp de Tarragona Primary Care Unit, Institut Català de la Salut, Doctor Mallafrè Guasch, 4, 43005 Tarragona, Spain; (D.S.-P.); (E.-M.S.-G.)
- ISAC Research Group, Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut IDIAP Jordi Gol, Gran Vía de Les Corts Catalanes, 591 Ático, 08007 Barcelona, Spain;
- School of Medicine and Health Sciences, Universitat Rovira i Virgili, Carrer de Sant Llorenç, 21, 43201 Reus, Spain
| | - Nuria Chamorro
- Pneumology Service, Hospital Universitari de Tarragona Joan XXII, Institut Català de la Salut, Doctor Mallafrè Guasch, 4, 43005 Tarragona, Spain
| | - Vanesa Lopez-Rodriguez
- Pneumology Service, Hospital Universitari de Tarragona Joan XXII, Institut Català de la Salut, Doctor Mallafrè Guasch, 4, 43005 Tarragona, Spain
| | - Jordi Daniel-Diez
- Camp de Tarragona Primary Care Unit, Institut Català de la Salut, Doctor Mallafrè Guasch, 4, 43005 Tarragona, Spain; (D.S.-P.); (E.-M.S.-G.)
| | - Anna Torres Creixenti
- Camp de Tarragona Primary Care Unit, Institut Català de la Salut, Doctor Mallafrè Guasch, 4, 43005 Tarragona, Spain; (D.S.-P.); (E.-M.S.-G.)
| | - Mohamed Issam El Mesnaoui
- Camp de Tarragona Primary Care Unit, Institut Català de la Salut, Doctor Mallafrè Guasch, 4, 43005 Tarragona, Spain; (D.S.-P.); (E.-M.S.-G.)
| | - Viviana Katherine Benavides Navas
- Camp de Tarragona Primary Care Unit, Institut Català de la Salut, Doctor Mallafrè Guasch, 4, 43005 Tarragona, Spain; (D.S.-P.); (E.-M.S.-G.)
| | - Jose David Castellanos Cotte
- Camp de Tarragona Primary Care Unit, Institut Català de la Salut, Doctor Mallafrè Guasch, 4, 43005 Tarragona, Spain; (D.S.-P.); (E.-M.S.-G.)
| | - Iván Abellan Cano
- Primary Care Unit, Sanitat Conselleria, Generalitat Valenciana, Dpto 18, Carretera de Sax s/n, 03600 Elda, Spain
| | | | - María Trenza Peñas
- Centro de Salud Aguilas Sur, Primary Care Unit, Servicio Murciano de Salud, Calle Rey Carlos III, s/n, 30880 Aguilas, Spain
| | - Iñaki Voelcker-Sala
- College of Medicine and Public Health, Flinders University, Flinders Drive, Bedford Park, SA 5042, Australia
| | - Felipe Villalobos
- ISAC Research Group, Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut IDIAP Jordi Gol, Gran Vía de Les Corts Catalanes, 591 Ático, 08007 Barcelona, Spain;
| | - Eva-María Satue-Gracia
- Camp de Tarragona Primary Care Unit, Institut Català de la Salut, Doctor Mallafrè Guasch, 4, 43005 Tarragona, Spain; (D.S.-P.); (E.-M.S.-G.)
- Primary Care Research Support Unit Reus-Tarragona, Institut Català de la Salut, Camí de Riudoms, 53–55, 43202 Reus, Spain
| | - Francisco Martin-Lujan
- Camp de Tarragona Primary Care Unit, Institut Català de la Salut, Doctor Mallafrè Guasch, 4, 43005 Tarragona, Spain; (D.S.-P.); (E.-M.S.-G.)
- ISAC Research Group, Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut IDIAP Jordi Gol, Gran Vía de Les Corts Catalanes, 591 Ático, 08007 Barcelona, Spain;
- School of Medicine and Health Sciences, Universitat Rovira i Virgili, Carrer de Sant Llorenç, 21, 43201 Reus, Spain
- Primary Care Research Support Unit Reus-Tarragona, Institut Català de la Salut, Camí de Riudoms, 53–55, 43202 Reus, Spain
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18
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Doe G, Taylor SJ, Topalovic M, Russell R, Evans RA, Maes J, Van Orshovon K, Sunjaya A, Scott D, Prevost AT, El-Emir E, Harvey J, Hopkinson NS, Kon SS, Patel S, Jarrold I, Spain N, Man WDC, Hutchinson A. Spirometry services in England post-pandemic and the potential role of AI support software: a qualitative study of challenges and opportunities. Br J Gen Pract 2023; 73:e915-e923. [PMID: 37903639 PMCID: PMC10633654 DOI: 10.3399/bjgp.2022.0608] [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: 12/06/2022] [Accepted: 06/26/2023] [Indexed: 11/01/2023] Open
Abstract
BACKGROUND Spirometry services to diagnose and monitor lung disease in primary care were identified as a priority in the NHS Long Term Plan, and are restarting post-COVID-19 pandemic in England; however, evidence regarding best practice is limited. AIM To explore perspectives on spirometry provision in primary care, and the potential for artificial intelligence (AI) decision support software to aid quality and interpretation. DESIGN AND SETTING Semi-structured interviews with stakeholders in spirometry services across England. METHOD Participants were recruited by snowball sampling. Interviews explored the pre- pandemic delivery of spirometry, restarting of services, and perceptions of the role of AI. Transcripts were analysed thematically. RESULTS In total, 28 participants (mean years' clinical experience = 21.6 [standard deviation 9.4, range 3-40]) were interviewed between April and June 2022. Participants included clinicians (n = 25) and commissioners (n = 3); eight held regional and/or national respiratory network advisory roles. Four themes were identified: 1) historical challenges in provision of spirometry services; 2) inequity in post- pandemic spirometry provision and challenges to restarting spirometry in primary care; 3) future delivery closer to patients' homes by appropriately trained staff; and 4) the potential for AI to have supportive roles in spirometry. CONCLUSION Stakeholders highlighted historic challenges and the damaging effects of the pandemic contributing to inequity in provision of spirometry, which must be addressed. Overall, stakeholders were positive about the potential of AI to support clinicians in quality assessment and interpretation of spirometry. However, it was evident that validation of the software must be sufficiently robust for clinicians and healthcare commissioners to have trust in the process.
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Affiliation(s)
- Gillian Doe
- Department of Respiratory Science, University of Leicester, Leicester, UK
| | - Stephanie Jc Taylor
- Wolfson Institute of Population Health, Queen Mary University of London, London, UK
| | | | - Richard Russell
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Rachael A Evans
- Department of Respiratory Science, University of Leicester, Leicester, UK
| | | | | | - Anthony Sunjaya
- George Institute for Global Health, UNSW Sydney, Australia; George Institute for Global Health, Imperial College London, London; Harefield Respiratory Research Group, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - David Scott
- Southampton Health Technology Assessments Centre, University of Southampton, Southampton, UK; Diabetes Research Centre, University of Leicester, Leicester, UK
| | - A Toby Prevost
- Nightingale-Saunders Clinical Trials and Epidemiology Unit, Cicely Saunders Institute of Palliative Care, Policy and Rehabilitation, King's College London, London, UK
| | - Ethaar El-Emir
- Harefield Respiratory Research Group, Guy's and St Thomas' NHS Foundation Trust, London; Department of Respiratory Medicine, Hillingdon Hospitals NHS Foundation Trust, London, UK
| | - Jennifer Harvey
- Harefield Respiratory Research Group, Guy's and St Thomas' NHS Foundation Trust, London; Department of Respiratory Medicine, Hillingdon Hospitals NHS Foundation Trust, London, UK
| | | | - Samantha S Kon
- Harefield Respiratory Research Group, Guy's and St Thomas' NHS Foundation Trust, London; Department of Respiratory Medicine, Hillingdon Hospitals NHS Foundation Trust, London, UK
| | - Suhani Patel
- Harefield Respiratory Research Group, Guy's and St Thomas' NHS Foundation Trust, London; National Heart & Lung Institute, Imperial College London, London, UK
| | | | - Nanette Spain
- Harefield Respiratory Research Group, Guy's and St Thomas' NHS Foundation Trust, London; National Heart & Lung Institute, Imperial College London, London; Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - William D-C Man
- Harefield Respiratory Research Group, Guy's and St Thomas' NHS Foundation Trust, London; National Heart & Lung Institute, Imperial College London, London; Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Ann Hutchinson
- Wolfson Palliative Care Research Centre, Hull York Medical School, University of Hull, Hull, UK
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19
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Stoichita A, Ghita M, Mahler B, Vlasceanu S, Ghinet A, Mosteanu M, Cioacata A, Udrea A, Marcu A, Mitra GD, Ionescu CM, Iliesiu A. Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital. J Clin Med 2023; 12:7115. [PMID: 38002725 PMCID: PMC10672398 DOI: 10.3390/jcm12227115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 10/27/2023] [Accepted: 11/04/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND In December 2019 the World Health Organization announced that the widespread severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection had become a global pandemic. The most affected organ by the novel virus is the lung, and imaging exploration of the thorax using computer tomography (CT) scanning and X-ray has had an important impact. MATERIALS AND METHODS We assessed the prevalence of lung lesions in vaccinated versus unvaccinated SARS-CoV-2 patients using an artificial intelligence (AI) platform provided by Medicai. The software analyzes the CT scans, performing the lung and lesion segmentation using a variant of the U-net convolutional network. RESULTS We conducted a cohort study at a tertiary lung hospital in which we included 186 patients: 107 (57.52%) male and 59 (42.47%) females, of which 157 (84.40%) were not vaccinated for SARS-CoV-2. Over five times more unvaccinated patients than vaccinated ones are admitted to the hospital and require imaging investigations. More than twice as many unvaccinated patients have more than 75% of the lungs affected. Patients in the age group 30-39 have had the most lung lesions at almost 69% of both lungs affected. Compared to vaccinated patients with comorbidities, unvaccinated patients with comorbidities had developed increased lung lesions by 5%. CONCLUSION The study revealed a higher percentage of lung lesions among unvaccinated SARS-CoV-2-positive patients admitted to The National Institute of Pulmonology "Marius Nasta" in Bucharest, Romania, underlining the importance of vaccination and also the usefulness of artificial intelligence in CT interpretation.
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Affiliation(s)
- Alexandru Stoichita
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (B.M.); (S.V.); (A.I.)
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Maria Ghita
- Research Group of Dynamical Systems and Control, Ghent University, 9052 Ghent, Belgium; (M.G.); (C.M.I.)
- Faculty of Medicine and Health Sciences, Antwerp University, 2610 Wilrijk, Belgium
| | - Beatrice Mahler
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (B.M.); (S.V.); (A.I.)
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Silviu Vlasceanu
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (B.M.); (S.V.); (A.I.)
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Andreea Ghinet
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Madalina Mosteanu
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
- Faculty of Medicine, University of Medicine and Pharmacy of Craiova, 200349 Craiova, Romania
| | - Andreea Cioacata
- “Marius Nasta” Institute of Pneumology, 050159 Bucharest, Romania; (A.G.); (M.M.); (A.C.)
| | - Andreea Udrea
- Medicai, 020961 Bucharest, Romania; (A.U.); (A.M.); (G.D.M.)
| | - Alina Marcu
- Medicai, 020961 Bucharest, Romania; (A.U.); (A.M.); (G.D.M.)
| | | | - Clara Mihaela Ionescu
- Research Group of Dynamical Systems and Control, Ghent University, 9052 Ghent, Belgium; (M.G.); (C.M.I.)
- Automation Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
| | - Adriana Iliesiu
- Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, Romania; (B.M.); (S.V.); (A.I.)
- Clinical Hospital “Prof. Dr. Th. Burghele”, 061344 Bucharest, Romania
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20
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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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21
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Waląg D, Soliński M, Kołtowski Ł, Górska K, Korczyński P, Kuźnar-Kamińska B, Grabicki M, Basza M, Łepek M. Deep learning algorithm for visual quality assessment of the spirograms. Physiol Meas 2023; 44:085004. [PMID: 37552997 DOI: 10.1088/1361-6579/acee41] [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: 12/27/2022] [Accepted: 08/08/2023] [Indexed: 08/10/2023]
Abstract
Objective. The quality of spirometry manoeuvres is crucial for correctly interpreting the values of spirometry parameters. A fundamental guideline for proper quality assessment is the American Thoracic Society and European Respiratory Society (ATS/ERS) Standards for spirometry, updated in 2019, which describe several start-of-test and end-of-test criteria which can be assessed automatically. However, the spirometry standards also require a visual evaluation of the spirometry curve to determine the spirograms' acceptability or usability. In this study, we present an automatic algorithm based on a convolutional neural network (CNN) for quality assessment of the spirometry curves as an alternative to manual verification performed by specialists.Approach. The algorithm for automatic assessment of spirometry measurements was created using a set of randomly selected 1998 spirograms which met all quantitative criteria defined by ATS/ERS Standards. Each spirogram was annotated as 'confirm' (remaining acceptable or usable status) or 'reject' (change the status to unacceptable) by four pulmonologists, separately for FEV1 and FVC parameters. The database was split into a training (80%) and test set (20%) for developing the CNN classification algorithm. The algorithm was optimised using a cross-validation method.Main results. The accuracy, sensitivity and specificity obtained for the algorithm were 92.6%, 93.1% and 90.0% for FEV1 and 94.1%, 95.6% and 88.3% for FVC, respectively.Significance.The algorithm provides an opportunity to significantly improve the quality of spirometry tests, especially during unsupervised spirometry. It can also serve as an additional tool in clinical trials to quickly assess the quality of a large group of tests.
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Affiliation(s)
- Damian Waląg
- Faculty of Physics, Warsaw University of Technology, Koszykowa St. 75, 00-662, Warsaw, Poland
| | - Mateusz Soliński
- School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences & Medicine, King's College London, Strand, London WC2R 2LS, United Kingdom
| | - Łukasz Kołtowski
- 1st Department of Cardiology, Medical University of Warsaw, Stefana Banacha St. 1a, 02-097, Warsaw, Poland
| | - Katarzyna Górska
- Department of Internal Medicine, Pulmonary Diseases and Allergy, Medical University of Warsaw, Stefana Banacha St. 1A, 02-097, Warsaw, Poland
| | - Piotr Korczyński
- Department of Internal Medicine, Pulmonary Diseases and Allergy, Medical University of Warsaw, Stefana Banacha St. 1A, 02-097, Warsaw, Poland
| | - Barbara Kuźnar-Kamińska
- Department of Pulmonology, Allergology and Respiratory Oncology, Poznan University of Medical Sciences, Szamarzewskiego St. 82, 61-001, Poznan, Poland
| | - Marcin Grabicki
- Department of Pulmonology, Allergology and Respiratory Oncology, Poznan University of Medical Sciences, Szamarzewskiego St. 82, 61-001, Poznan, Poland
| | - Mikołaj Basza
- Medical University of Silesia, Poniatowskiego St. 15, 40-055, Katowice, Poland
| | - Michał Łepek
- Faculty of Physics, Warsaw University of Technology, Koszykowa St. 75, 00-662, Warsaw, Poland
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22
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van der Sar IG, van Jaarsveld N, Spiekerman IA, Toxopeus FJ, Langens QL, Wijsenbeek MS, Dauwels J, Moor CC. Evaluation of different classification methods using electronic nose data to diagnose sarcoidosis. J Breath Res 2023; 17:047104. [PMID: 37595574 DOI: 10.1088/1752-7163/acf1bf] [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: 12/02/2022] [Accepted: 08/18/2023] [Indexed: 08/20/2023]
Abstract
Electronic nose (eNose) technology is an emerging diagnostic application, using artificial intelligence to classify human breath patterns. These patterns can be used to diagnose medical conditions. Sarcoidosis is an often difficult to diagnose disease, as no standard procedure or conclusive test exists. An accurate diagnostic model based on eNose data could therefore be helpful in clinical decision-making. The aim of this paper is to evaluate the performance of various dimensionality reduction methods and classifiers in order to design an accurate diagnostic model for sarcoidosis. Various methods of dimensionality reduction and multiple hyperparameter optimised classifiers were tested and cross-validated on a dataset of patients with pulmonary sarcoidosis (n= 224) and other interstitial lung disease (n= 317). Best performing methods were selected to create a model to diagnose patients with sarcoidosis. Nested cross-validation was applied to calculate the overall diagnostic performance. A classification model with feature selection and random forest (RF) classifier showed the highest accuracy. The overall diagnostic performance resulted in an accuracy of 87.1% and area-under-the-curve of 91.2%. After comparing different dimensionality reduction methods and classifiers, a highly accurate model to diagnose a patient with sarcoidosis using eNose data was created. The RF classifier and feature selection showed the best performance. The presented systematic approach could also be applied to other eNose datasets to compare methods and select the optimal diagnostic model.
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Affiliation(s)
- Iris G van der Sar
- Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Nynke van Jaarsveld
- Educational Program Technical Medicine, Leiden University Medical Center, Delft University of Technology & Erasmus University Medical Center, Leiden, Delft & Rotterdam, The Netherlands
| | - Imme A Spiekerman
- Educational Program Technical Medicine, Leiden University Medical Center, Delft University of Technology & Erasmus University Medical Center, Leiden, Delft & Rotterdam, The Netherlands
| | - Floor J Toxopeus
- Educational Program Technical Medicine, Leiden University Medical Center, Delft University of Technology & Erasmus University Medical Center, Leiden, Delft & Rotterdam, The Netherlands
| | - Quint L Langens
- Educational Program Technical Medicine, Leiden University Medical Center, Delft University of Technology & Erasmus University Medical Center, Leiden, Delft & Rotterdam, The Netherlands
| | - Marlies S Wijsenbeek
- Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Justin Dauwels
- Department of Microelectronics, Delft University of Technology, Delft, The Netherlands
| | - Catharina C Moor
- Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, The Netherlands
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23
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Ma FQ, He C, Yang HR, Hu ZW, Mao HR, Fan CY, Qi Y, Zhang JX, Xu B. Interpretable machine-learning model for Predicting the Convalescent COVID-19 patients with pulmonary diffusing capacity impairment. BMC Med Inform Decis Mak 2023; 23:169. [PMID: 37644543 PMCID: PMC10466769 DOI: 10.1186/s12911-023-02192-6] [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: 12/15/2022] [Accepted: 05/04/2023] [Indexed: 08/31/2023] Open
Abstract
INTRODUCTION The COVID-19 patients in the convalescent stage noticeably have pulmonary diffusing capacity impairment (PDCI). The pulmonary diffusing capacity is a frequently-used indicator of the COVID-19 survivors' prognosis of pulmonary function, but the current studies focusing on prediction of the pulmonary diffusing capacity of these people are limited. The aim of this study was to develop and validate a machine learning (ML) model for predicting PDCI in the COVID-19 patients using routinely available clinical data, thus assisting the clinical diagnosis. METHODS Collected from a follow-up study from August to September 2021 of 221 hospitalized survivors of COVID-19 18 months after discharge from Wuhan, including the demographic characteristics and clinical examination, the data in this study were randomly separated into a training (80%) data set and a validation (20%) data set. Six popular machine learning models were developed to predict the pulmonary diffusing capacity of patients infected with COVID-19 in the recovery stage. The performance indicators of the model included area under the curve (AUC), Accuracy, Recall, Precision, Positive Predictive Value(PPV), Negative Predictive Value (NPV) and F1. The model with the optimum performance was defined as the optimal model, which was further employed in the interpretability analysis. The MAHAKIL method was utilized to balance the data and optimize the balance of sample distribution, while the RFECV method for feature selection was utilized to select combined features more favorable to machine learning. RESULTS A total of 221 COVID-19 survivors were recruited in this study after discharge from hospitals in Wuhan. Of these participants, 117 (52.94%) were female, with a median age of 58.2 years (standard deviation (SD) = 12). After feature selection, 31 of the 37 clinical factors were finally selected for use in constructing the model. Among the six tested ML models, the best performance was accomplished in the XGBoost model, with an AUC of 0.755 and an accuracy of 78.01% after experimental verification. The SHAPELY Additive explanations (SHAP) summary analysis exhibited that hemoglobin (Hb), maximal voluntary ventilation (MVV), severity of illness, platelet (PLT), Uric Acid (UA) and blood urea nitrogen (BUN) were the top six most important factors affecting the XGBoost model decision-making. CONCLUSION The XGBoost model reported here showed a good prognostic prediction ability for PDCI of COVID-19 survivors during the recovery period. Among the interpretation methods based on the importance of SHAP values, Hb and MVV contributed the most to the prediction of PDCI outcomes of COVID-19 survivors in the recovery period.
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Affiliation(s)
- Fu-Qiang Ma
- Hubei University of Chinese Medicine, Wuhan, 430065, China
| | - Cong He
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, 430061, China
- Affiliated Hospital of Hubei University of Traditional Chinese Medicine, Wuhan, 430061, China
- Hubei Province Academy of Traditional Chinese Medicine, Wuhan, 430074, China
| | - Hao-Ran Yang
- School of Software, HuaZhong University of Science and Technology, Wuhan, 430074, China
| | - Zuo-Wei Hu
- Wuhan No.1 Hospital, Wuhan, 430022, China
| | - He-Rong Mao
- Hubei University of Chinese Medicine, Wuhan, 430065, China
| | - Cun-Yu Fan
- Hubei Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Wuhan, 430015, China
| | - Yu Qi
- Hubei University of Chinese Medicine, Wuhan, 430065, China
| | - Ji-Xian Zhang
- Hubei Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Wuhan, 430015, China.
| | - Bo Xu
- Hubei University of Chinese Medicine, Wuhan, 430065, China.
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24
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Hurvitz N, Ilan Y. The Constrained-Disorder Principle Assists in Overcoming Significant Challenges in Digital Health: Moving from "Nice to Have" to Mandatory Systems. Clin Pract 2023; 13:994-1014. [PMID: 37623270 PMCID: PMC10453547 DOI: 10.3390/clinpract13040089] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 08/16/2023] [Accepted: 08/18/2023] [Indexed: 08/26/2023] Open
Abstract
The success of artificial intelligence depends on whether it can penetrate the boundaries of evidence-based medicine, the lack of policies, and the resistance of medical professionals to its use. The failure of digital health to meet expectations requires rethinking some of the challenges faced. We discuss some of the most significant challenges faced by patients, physicians, payers, pharmaceutical companies, and health systems in the digital world. The goal of healthcare systems is to improve outcomes. Assisting in diagnosing, collecting data, and simplifying processes is a "nice to have" tool, but it is not essential. Many of these systems have yet to be shown to improve outcomes. Current outcome-based expectations and economic constraints make "nice to have," "assists," and "ease processes" insufficient. Complex biological systems are defined by their inherent disorder, bounded by dynamic boundaries, as described by the constrained disorder principle (CDP). It provides a platform for correcting systems' malfunctions by regulating their degree of variability. A CDP-based second-generation artificial intelligence system provides solutions to some challenges digital health faces. Therapeutic interventions are held to improve outcomes with these systems. In addition to improving clinically meaningful endpoints, CDP-based second-generation algorithms ensure patient and physician engagement and reduce the health system's costs.
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Affiliation(s)
| | - Yaron Ilan
- Hadassah Medical Center, Department of Medicine, Faculty of Medicine, Hebrew University, POB 1200, Jerusalem IL91120, Israel;
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25
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Nakshbandi G, Moor CC, Wijsenbeek MS. Role of the internet of medical things in care for patients with interstitial lung disease. Curr Opin Pulm Med 2023; 29:285-292. [PMID: 37212372 PMCID: PMC10241441 DOI: 10.1097/mcp.0000000000000971] [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: 05/23/2023]
Abstract
PURPOSE OF REVIEW Online technologies play an increasing role in facilitating care for patients with interstitial lung disease (ILD). In this review, we will give an overview of different applications of the internet of medical things (IoMT) for patients with ILD. RECENT FINDINGS Various applications of the IoMT, including teleconsultations, virtual MDTs, digital information, and online peer support, are now used in daily care of patients with ILD. Several studies showed that other IoMT applications, such as online home monitoring and telerehabilitation, seem feasible and reliable, but widespread implementation in clinical practice is lacking. The use of artificial intelligence algorithms and online data clouds in ILD is still in its infancy, but has the potential to improve remote, outpatient clinic, and in-hospital care processes. Further studies in large real-world cohorts to confirm and clinically validate results from previous studies are needed. SUMMARY We believe that in the near future innovative technologies, facilitated by the IoMT, will further enhance individually targeted treatment for patients with ILD by interlinking and combining data from various sources.
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Affiliation(s)
- Gizal Nakshbandi
- Department of Respiratory Medicine, Erasmus University Medical Centre, Rotterdam, The Netherlands
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26
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Siebert JN, Hartley MA, Courvoisier DS, Salamin M, Robotham L, Doenz J, Barazzone-Argiroffo C, Gervaix A, Bridevaux PO. Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study. BMC Pulm Med 2023; 23:191. [PMID: 37264374 DOI: 10.1186/s12890-022-02255-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 11/20/2022] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Interstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach. METHODS A total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls. DISCUSSION This approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity. Trial registration Registration: August 8, 2022. CLINICALTRIALS gov Identifier: NCT05318599.
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Affiliation(s)
- Johan N Siebert
- Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, 47 Avenue de la Roseraie, 1211, Geneva 14, Switzerland.
- Faculty of Medicine, University of Geneva, Geneva, Switzerland.
| | - Mary-Anne Hartley
- Machine Learning and Optimization (MLO) Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Delphine S Courvoisier
- Quality of Care Unit, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Marlène Salamin
- Division of Pulmonology, Hospital of Valais, Sion, Switzerland
| | - Laura Robotham
- Division of Pulmonology, Hospital of Valais, Sion, Switzerland
| | - Jonathan Doenz
- Machine Learning and Optimization (MLO) Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland
| | - Constance Barazzone-Argiroffo
- Division of Paediatric Pulmonology, Department of Women, Child and Adolescent, Geneva University Hospitals, Geneva, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Alain Gervaix
- Division of Paediatric Emergency Medicine, Department of Women, Child and Adolescent, Geneva University Hospitals, 47 Avenue de la Roseraie, 1211, Geneva 14, Switzerland
- Faculty of Medicine, University of Geneva, Geneva, Switzerland
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27
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Long H, Li S, Chen Y. Digital health in chronic obstructive pulmonary disease. Chronic Dis Transl Med 2023; 9:90-103. [PMID: 37305103 PMCID: PMC10249197 DOI: 10.1002/cdt3.68] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 02/11/2023] [Accepted: 04/03/2023] [Indexed: 06/13/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) can be prevented and treated through effective care, reducing exacerbations and hospitalizations. Early identification of individuals at high risk of COPD exacerbation is an opportunity for preventive measures. However, many patients struggle to follow their treatment plans because of a lack of knowledge about the disease, limited access to resources, and insufficient clinical support. The growth of digital health-which encompasses advancements in health information technology, artificial intelligence, telehealth, the Internet of Things, mobile health, wearable technology, and digital therapeutics-offers opportunities for improving the early diagnosis and management of COPD. This study reviewed the field of digital health in terms of COPD. The findings showed that despite significant advances in digital health, there are still obstacles impeding its effectiveness. Finally, we highlighted some of the major challenges and possibilities for developing and integrating digital health in COPD management.
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Affiliation(s)
- Huanyu Long
- Department of Pulmonary and Critical Care MedicinePeking University Third HospitalBeijingChina
| | - Shurun Li
- Peking University Health Science CenterBeijingChina
| | - Yahong Chen
- Department of Pulmonary and Critical Care MedicinePeking University Third HospitalBeijingChina
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28
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Steenbruggen I, McCormack MC. Artificial intelligence: do we really need it in pulmonary function interpretation? Eur Respir J 2023; 61:61/5/2300625. [PMID: 37208036 DOI: 10.1183/13993003.00625-2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 04/25/2023] [Indexed: 05/21/2023]
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29
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Mehrpour O, Saeedi F, Nakhaee S, Tavakkoli Khomeini F, Hadianfar A, Amirabadizadeh A, Hoyte C. Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System. BMC Med Inform Decis Mak 2023; 23:60. [PMID: 37024869 PMCID: PMC10080923 DOI: 10.1186/s12911-022-02095-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 12/26/2022] [Indexed: 04/08/2023] Open
Abstract
BACKGROUND Biguanides and sulfonylurea are two classes of anti-diabetic medications that have commonly been prescribed all around the world. Diagnosis of biguanide and sulfonylurea exposures is based on history taking and physical examination; thus, physicians might misdiagnose these two different clinical settings. We aimed to conduct a study to develop a model based on decision tree analysis to help physicians better diagnose these poisoning cases. METHODS The National Poison Data System was used for this six-year retrospective cohort study.The decision tree model, common machine learning models multi layers perceptron, stochastic gradient descent (SGD), Adaboosting classiefier, linear support vector machine and ensembling methods including bagging, voting and stacking methods were used. The confusion matrix, precision, recall, specificity, f1-score, and accuracy were reported to evaluate the model's performance. RESULTS Of 6183 participants, 3336 patients (54.0%) were identified as biguanides exposures, and the remaining were those with sulfonylureas exposures. The decision tree model showed that the most important clinical findings defining biguanide and sulfonylurea exposures were hypoglycemia, abdominal pain, acidosis, diaphoresis, tremor, vomiting, diarrhea, age, and reasons for exposure. The specificity, precision, recall, f1-score, and accuracy of all models were greater than 86%, 89%, 88%, and 88%, respectively. The lowest values belong to SGD model. The decision tree model has a sensitivity (recall) of 93.3%, specificity of 92.8%, precision of 93.4%, f1_score of 93.3%, and accuracy of 93.3%. CONCLUSION Our results indicated that machine learning methods including decision tree and ensembling methods provide a precise prediction model to diagnose biguanides and sulfonylureas exposure.
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Affiliation(s)
- Omid Mehrpour
- Data Science Institute, Southern Methodist University, Dallas, TX, USA.
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences, Birjand, Iran.
| | - Farhad Saeedi
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences, Birjand, Iran
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - Samaneh Nakhaee
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences, Birjand, Iran
| | | | - Ali Hadianfar
- Department of Epidemiology and Biostatistics, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alireza Amirabadizadeh
- Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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30
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Mehrpour O, Nakhaee S, Saeedi F, Valizade B, Lotfi E, Nawaz MH. Utility of artificial intelligence to identify antihyperglycemic agents poisoning in the USA: introducing a practical web application using National Poison Data System (NPDS). ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:57801-57810. [PMID: 36973614 DOI: 10.1007/s11356-023-26605-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 03/18/2023] [Indexed: 05/10/2023]
Abstract
Clinical effects of antihyperglycemic agents poisoning may overlap each other. So, distinguishing exposure to these pharmaceutical drugs may take work. This study examined the application of machine learning techniques in identifying antihyperglycemic agent exposure using the national poisoning database in the USA. In this study, the data of single exposure due to Biguanides and Sulfonylureas (n=6183) was requested from the National Poison Data System (NPDS) for 2014-2018. We have tried five machine learning models (random forest classifier, k-nearest neighbors, Xgboost classifier, logistic regression, neural network Keras). For the multiclass classification modeling, we have divided the dataset into two parts: train (75%) and test (25%). The performance metrics used were accuracy, specificity, precision, recall, and F1-score. The algorithms used to get the classification results of different models to diagnose antihyperglycemic agents were very accurate. The accuracy of our model in determining these two antihyperglycemic agents was 91-93%. The precision-recall curve showed average precision of 0.91, 0.97, 0.97, and 0.98 for k-nearest neighbors, logistic regression, random forest, and XGB, respectively. The logistic regression, random forest, and XGB had the highest AUC (AUC=0.97) among both biguanides and sulfonylureas groups. The negative predictive values (NPV) for all the models were between 89 and 93%. We introduced a practical web application to help physicians distinguish between these agents. Despite variations in accuracy among the different types of algorithms used, all of them could accurately determine the specific exposure to biguanides and sulfonylureas retrospectively. Machine learning can distinguish antihyperglycemic agents, which may be useful for physicians without any background in medical toxicology. Besides, Our suggested ML-based Web application might help physicians in their diagnosis.
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Affiliation(s)
- Omid Mehrpour
- AI and Health LLC, Tucson, AZ, USA.
- Rocky Mountain Poison & Drug Safety, Denver Health, and Hospital Authority, Denver, CO, USA.
| | - Samaneh Nakhaee
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
| | - Farhad Saeedi
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
- Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran
| | - Bahare Valizade
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
| | - Erfan Lotfi
- Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran
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Ward T, Jha A, Daynes E, Ackland J, Chalmers JD. Review of the British Thoracic Society Winter Meeting 23 November 2022 23-25 November 2022. Thorax 2023; 78:e1. [PMID: 36717241 DOI: 10.1136/thorax-2022-219941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 01/06/2023] [Indexed: 02/01/2023]
Abstract
The British Thoracic Society Winter Meeting at the QEII Centre in London provided the first opportunity for the respiratory community to meet and disseminate research findings face to face since the start of the COVID-19 pandemic. World-leading researchers from the UK and abroad presented their latest findings across a range of respiratory diseases. This article aims to represent the range of the conference and as such is written from the perspective of a basic scientist, a physiotherapist and two doctors. The authors reviewed showcase sessions plus a selection of symposia based on their personal highlights. Content ranged from exciting new developments in basic science to new and unpublished results from clinical trials, delivered by leading scientists from their fields including former deputy chief medical officer Professor Sir Jonathan Van-Tam and former WHO chief scientist Dr Soumya Swaminathan.
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Affiliation(s)
- Tom Ward
- Department Respiratory Sciences, College of Life Sciences, University of Leicester, Leicester, UK
| | - Akhilesh Jha
- Department of Medicine, University of Cambridge, Cambridge, UK
| | - Enya Daynes
- Department of Respiratory Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK
| | - Jodie Ackland
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UK
| | - James D Chalmers
- Division of Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
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Briganti G. [Artificial intelligence: An introduction for clinicians]. Rev Mal Respir 2023; 40:308-313. [PMID: 36894376 DOI: 10.1016/j.rmr.2023.02.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/15/2023] [Indexed: 03/09/2023]
Abstract
Artificial intelligence (AI) is a growing field that has the potential to transform many areas of society, including healthcare. For a physician, it is important to understand the basics of AI and its potential applications in medicine. AI refers to the development of computer systems capable of performing tasks that typically require human intelligence, such as pattern recognition, learning from data, and decision-making. This technology can be used to analyze large amounts of patient data and to identify trends and patterns that can be difficult for human physicians to detect. This can help doctors to manage their workload more efficiently and provide better care for their patients. All in all, AI has the potential to dramatically improve the practice of medicine and improve patient outcomes. In this work, the definition and the key principles of AI are outlined, with particular focus on the field of machine learning, which has been undergoing considerable development in medicine, providing clinicians with in-depth understanding of the principles underlying the new technologies ensuring improved health care.
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Affiliation(s)
- G Briganti
- Chaire d'intelligence artificielle et médecine digitale, service de neurosciences, faculté de médecine, université de Mons, avenue du Champs de Mars, 6, 7000 Mons, Belgique.
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Ladbury C, Amini A, Govindarajan A, Mambetsariev I, Raz DJ, Massarelli E, Williams T, Rodin A, Salgia R. Integration of artificial intelligence in lung cancer: Rise of the machine. Cell Rep Med 2023; 4:100933. [PMID: 36738739 PMCID: PMC9975283 DOI: 10.1016/j.xcrm.2023.100933] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Revised: 11/14/2022] [Accepted: 01/17/2023] [Indexed: 02/05/2023]
Abstract
The goal of oncology is to provide the longest possible survival outcomes with the therapeutics that are currently available without sacrificing patients' quality of life. In lung cancer, several data points over a patient's diagnostic and treatment course are relevant to optimizing outcomes in the form of precision medicine, and artificial intelligence (AI) provides the opportunity to use available data from molecular information to radiomics, in combination with patient and tumor characteristics, to help clinicians provide individualized care. In doing so, AI can help create models to identify cancer early in diagnosis and deliver tailored therapy on the basis of available information, both at the time of diagnosis and in real time as they are undergoing treatment. The purpose of this review is to summarize the current literature in AI specific to lung cancer and how it applies to the multidisciplinary team taking care of these complex patients.
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Affiliation(s)
- Colton Ladbury
- Department of Radiation Oncology, City of Hope National Medical Center, 1500 E Duarte Road, Duarte, CA 91010, USA
| | - Arya Amini
- Department of Radiation Oncology, City of Hope National Medical Center, 1500 E Duarte Road, Duarte, CA 91010, USA.
| | - Ameish Govindarajan
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Isa Mambetsariev
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Dan J Raz
- Department of Surgery, City of Hope National Medical Center, Duarte, CA, USA
| | - Erminia Massarelli
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
| | - Terence Williams
- Department of Radiation Oncology, City of Hope National Medical Center, 1500 E Duarte Road, Duarte, CA 91010, USA
| | - Andrei Rodin
- Department of Computational and Quantitative Medicine, City of Hope National Medical Center, Duarte, CA, USA
| | - Ravi Salgia
- Department of Medical Oncology, City of Hope National Medical Center, Duarte, CA, USA
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Alsomali H, Palmer E, Aujayeb A, Funston W. Early Diagnosis and Treatment of Idiopathic Pulmonary Fibrosis: A Narrative Review. Pulm Ther 2023; 9:177-193. [PMID: 36773130 PMCID: PMC10203082 DOI: 10.1007/s41030-023-00216-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 01/19/2023] [Indexed: 02/12/2023] Open
Abstract
Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrosing interstitial lung disease of unknown aetiology. Patients typically present with symptoms of chronic dyspnoea and cough over a period of months to years. IPF has a poor prognosis, with an average life expectancy of 3-5 years from diagnosis if left untreated. Two anti-fibrotic medications (nintedanib and pirfenidone) have been approved for the treatment of IPF. These drugs slow disease progression by reducing decline in lung function. Early diagnosis is crucial to ensure timely treatment selection and improve outcomes. High-resolution computed tomography (HRCT) plays a major role in the diagnosis of IPF. In this narrative review, we discuss the importance of early diagnosis, awareness among primary care physicians, lung cancer screening programmes and early IPF detection, and barriers to accessing anti-fibrotic medications.
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Affiliation(s)
- Hana Alsomali
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, NE1 7RU, UK
| | - Evelyn Palmer
- Department of Respiratory Medicine, The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, NE1 4LP, UK.
| | - Avinash Aujayeb
- Department of Respiratory Medicine, Northumbria Healthcare NHS Trust, Northumbria Way, Cramlington, NE23 6NZ, UK
| | - Wendy Funston
- Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, NE1 7RU, UK.,Department of Respiratory Medicine, The Newcastle Upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne, NE1 4LP, UK
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Tele-Medicine: The Search of the Holy Grail. Arch Bronconeumol 2023:S0300-2896(23)00026-1. [PMID: 36803936 DOI: 10.1016/j.arbres.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/10/2023]
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36
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Zhang G, Luo L, Zhang L, Liu Z. Research Progress of Respiratory Disease and Idiopathic Pulmonary Fibrosis Based on Artificial Intelligence. Diagnostics (Basel) 2023; 13:diagnostics13030357. [PMID: 36766460 PMCID: PMC9914063 DOI: 10.3390/diagnostics13030357] [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: 12/22/2022] [Revised: 01/06/2023] [Accepted: 01/16/2023] [Indexed: 01/21/2023] Open
Abstract
Machine Learning (ML) is an algorithm based on big data, which learns patterns from the previously observed data through classifying, predicting, and optimizing to accomplish specific tasks. In recent years, there has been rapid development in the field of ML in medicine, including lung imaging analysis, intensive medical monitoring, mechanical ventilation, and there is need for intubation etiology prediction evaluation, pulmonary function evaluation and prediction, obstructive sleep apnea, such as biological information monitoring and so on. ML can have good performance and is a great potential tool, especially in the imaging diagnosis of interstitial lung disease. Idiopathic pulmonary fibrosis (IPF) is a major problem in the treatment of respiratory diseases, due to the abnormal proliferation of fibroblasts, leading to lung tissue destruction. The diagnosis mainly depends on the early detection of imaging and early treatment, which can effectively prolong the life of patients. If the computer can be used to assist the examination results related to the effects of fibrosis, a timely diagnosis of such diseases will be of great value to both doctors and patients. We also previously proposed a machine learning algorithm model that can play a good clinical guiding role in early imaging prediction of idiopathic pulmonary fibrosis. At present, AI and machine learning have great potential and ability to transform many aspects of respiratory medicine and are the focus and hotspot of research. AI needs to become an invisible, seamless, and impartial auxiliary tool to help patients and doctors make better decisions in an efficient, effective, and acceptable way. The purpose of this paper is to review the current application of machine learning in various aspects of respiratory diseases, with the hope to provide some help and guidance for clinicians when applying algorithm models.
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Affiliation(s)
- Gerui Zhang
- Department of Critical Care Unit, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
| | - Lin Luo
- Department of Critical Care Unit, The Second Hospital of Dalian Medical University, 467 Zhongshan Road, Shahekou District, Dalian 116023, China
| | - Limin Zhang
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
| | - Zhuo Liu
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, 222, Zhongshan Road, Dalian 116011, China
- Correspondence:
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Li HY, Gao TY, Fang W, Xian-Yu CY, Deng NJ, Zhang C, Niu YM. Global, regional and national burden of chronic obstructive pulmonary disease over a 30-year period: Estimates from the 1990 to 2019 Global Burden of Disease Study. Respirology 2023; 28:29-36. [PMID: 36054068 PMCID: PMC10087739 DOI: 10.1111/resp.14349] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 08/08/2022] [Indexed: 02/03/2023]
Abstract
BACKGROUND AND OBJECTIVE Chronic obstructive pulmonary disease (COPD) is the most prevalent chronic respiratory disease. This study investigated the global, regional and country burden of COPD based on gender, age and socio-demographic indices (SDIs) in the last 30-year period from 1990 to 2019. METHODS The COPD data, including incidence, mortality and disability-adjusted life years (DALYs), were obtained from the 2019 Global Burden of Disease Study. If age-standardized incidence rate (ASIR) or death rate (ASDR) remains almost constant or decreases, the number of cases will still increase as the global population increases substantially. Estimated annual percentage change (EAPC) was calculated to assess incidence, mortality and DALY trends. RESULTS The incidence of COPD increased by 85.89% from 8,722,966 cases in 1990 to 16,214,828 cases in 2019, and the ASIR decreased from 216.48/100,000 persons in 1990 (95%UI, 204.56-227.33) to 200.49 per 100,000 persons (95%UI, 188.63-212.57) in 2019. The ASIR increased (EAPC = 0.05, 95%CI, 0.01-0.10) in the low SDI region, was stable in the high SDI region, and fell in the other three SDI regions. Men had a higher ASIR than women over the past 30 years, and there were differences in the incidence rates for different age groups. Male mortality and DALYs were higher than female mortality. ASDR decreased by 2.13% (95%CI, -2.23% to -2.02%) per year and the annual age-standardized DALY rate decreased by 1.97% (95%CI, -2.05% to -1.89%). CONCLUSIONS The ASIR, ASDR and age-standardized DALY rate of COPD declined overall in the last 30 years, and were highest in the low-middle SDI region.
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Affiliation(s)
- Hao-Yang Li
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Teng-Yu Gao
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Wei Fang
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Chen-Yang Xian-Yu
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Nian-Jia Deng
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Chao Zhang
- Center for Evidence-Based Medicine and Clinical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China
| | - Yu-Ming Niu
- Department of Stomatology & Center for Evidence-Based Medicine and Clinical Research, Gongli Hospital of Shanghai Pudong New Area, Shanghai, China
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38
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Wijsenbeek MS, Moor CC, Johannson KA, Jackson PD, Khor YH, Kondoh Y, Rajan SK, Tabaj GC, Varela BE, van der Wal P, van Zyl-Smit RN, Kreuter M, Maher TM. Home monitoring in interstitial lung diseases. THE LANCET. RESPIRATORY MEDICINE 2023; 11:97-110. [PMID: 36206780 DOI: 10.1016/s2213-2600(22)00228-4] [Citation(s) in RCA: 26] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/08/2022] [Accepted: 06/08/2022] [Indexed: 11/05/2022]
Abstract
The widespread use of smartphones and the internet has enabled self-monitoring and more hybrid-care models. The COVID-19 pandemic has further accelerated remote monitoring, including in the heterogenous and often vulnerable group of patients with interstitial lung diseases (ILDs). Home monitoring in ILD has the potential to improve access to specialist care, reduce the burden on health-care systems, improve quality of life for patients, identify acute and chronic disease worsening, guide treatment decisions, and simplify clinical trials. Home spirometry has been used in ILD for several years and studies with other devices (such as pulse oximeters, activity trackers, and cough monitors) have emerged. At the same time, challenges have surfaced, including technical, analytical, and implementational issues. In this Series paper, we provide an overview of experiences with home monitoring in ILD, address the challenges and limitations for both care and research, and provide future perspectives. VIDEO ABSTRACT.
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Affiliation(s)
- Marlies S Wijsenbeek
- Centre of Excellence for Interstitial Lung Diseases and Sarcoidosis, Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, Netherlands.
| | - Catharina C Moor
- Centre of Excellence for Interstitial Lung Diseases and Sarcoidosis, Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, Netherlands
| | - Kerri A Johannson
- Department of Medicine and Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada
| | - Peter D Jackson
- Department of Pulmonary and Critical Care Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Yet H Khor
- Central Clinical School, Monash University, Melbourne, VIC, Australia; Department of Respiratory and Sleep Medicine, Austin Health, Heidelberg, VIC, Australia
| | - Yasuhiro Kondoh
- Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan
| | - Sujeet K Rajan
- Department of Chest Medicine, Bombay Hospital Institute of Medical Sciences, Bhatia Hospital, Mumbai, India
| | - Gabriela C Tabaj
- Department of Respiratory Medicine, Cetrángolo Hospital, Buenos Aires, Argentina
| | - Brenda E Varela
- Department of Respiratory Medicine, Hospital Alemán, Buenos Aires, Argentina
| | - Pieter van der Wal
- Patient expert, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Richard N van Zyl-Smit
- Division of Pulmonology and University of Cape Town Lung Institute, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Michael Kreuter
- Center for Interstitial and Rare Lung Diseases and Interdisciplinary Center for Sarcoidosis, Thoraxklinik, University Hospital Heidelberg, Germany; German Center for Lung Research, Heidelberg, Germany; Department of Pneumology, RKH Clinics Ludwigsburg, Ludwigsburg, Germany
| | - Toby M Maher
- Division of Pulmonary, Critical Care and Sleep Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; National Heart and Lung Institute, Imperial College London, London, UK
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Mac A, Xu T, Wu JKY, Belousova N, Kitazawa H, Vozoris N, Rozenberg D, Ryan CM, Valaee S, Chow CW. Deep learning using multilayer perception improves the diagnostic acumen of spirometry: a single-centre Canadian study. BMJ Open Respir Res 2022; 9:9/1/e001396. [PMID: 36572484 PMCID: PMC9806081 DOI: 10.1136/bmjresp-2022-001396] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 12/14/2022] [Indexed: 12/27/2022] Open
Abstract
RATIONALE Spirometry and plethysmography are the gold standard pulmonary function tests (PFT) for diagnosis and management of lung disease. Due to the inaccessibility of plethysmography, spirometry is often used alone but this leads to missed or misdiagnoses as spirometry cannot identify restrictive disease without plethysmography. We aimed to develop a deep learning model to improve interpretation of spirometry alone. METHODS We built a multilayer perceptron model using full PFTs from 748 patients, interpreted according to international guidelines. Inputs included spirometry (forced vital capacity, forced expiratory volume in 1 s, forced mid-expiratory flow25-75), plethysmography (total lung capacity, residual volume) and biometrics (sex, age, height). The model was developed with 2582 PFTs from 477 patients, randomly divided into training (80%), validation (10%) and test (10%) sets, and refined using 1245 previously unseen PFTs from 271 patients, split 50/50 as validation (136 patients) and test (135 patients) sets. Only one test per patient was used for each of 10 experiments conducted for each input combination. The final model was compared with interpretation of 82 spirometry tests by 6 trained pulmonologists and a decision tree. RESULTS Accuracies from the first 477 patients were similar when inputs included biometrics+spirometry+plethysmography (95%±3%) vs biometrics+spirometry (90%±2%). Model refinement with the next 271 patients improved accuracies with biometrics+pirometry (95%±2%) but no change for biometrics+spirometry+plethysmography (95%±2%). The final model significantly outperformed (94.67%±2.63%, p<0.01 for both) interpretation of 82 spirometry tests by the decision tree (75.61%±0.00%) and pulmonologists (66.67%±14.63%). CONCLUSIONS Deep learning improves the diagnostic acumen of spirometry and classifies lung physiology better than pulmonologists with accuracies comparable to full PFTs.
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Affiliation(s)
- Amanda Mac
- Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada
| | - Tong Xu
- Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada
| | - Joyce K Y Wu
- Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada,Department of Medicine, University Health Network, Toronto, Ontario, Canada
| | - Natalia Belousova
- Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada,Department of Medicine, University Health Network, Toronto, Ontario, Canada
| | - Haruna Kitazawa
- Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada,Department of Medicine, University Health Network, Toronto, Ontario, Canada
| | - Nick Vozoris
- Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada
| | - Dmitry Rozenberg
- Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada,Department of Medicine, University Health Network, Toronto, Ontario, Canada
| | - Clodagh M Ryan
- Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada,Department of Medicine, University Health Network, Toronto, Ontario, Canada
| | - Shahrokh Valaee
- Electrical and Computer Engineeing, University of Toronto, Toronto, Ontario, Canada
| | - Chung-Wai Chow
- Medicine, Division of Respirology, University of Toronto, Toronto, Ontario, Canada,Department of Medicine, University Health Network, Toronto, Ontario, Canada
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de Lima AD, Lopes AJ, do Amaral JLM, de Melo PL. Explainable machine learning methods and respiratory oscillometry for the diagnosis of respiratory abnormalities in sarcoidosis. BMC Med Inform Decis Mak 2022; 22:274. [PMID: 36266674 PMCID: PMC9583465 DOI: 10.1186/s12911-022-02021-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Accepted: 10/11/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND In this work, we developed many machine learning classifiers to assist in diagnosing respiratory changes associated with sarcoidosis, based on results from the Forced Oscillation Technique (FOT), a non-invasive method used to assess pulmonary mechanics. In addition to accurate results, there is a particular interest in their interpretability and explainability, so we used Genetic Programming since the classification is made with intelligible expressions and we also evaluate the feature importance in different experiments to find the more discriminative features. METHODOLOGY/PRINCIPAL FINDINGS We used genetic programming in its traditional tree form and a grammar-based form. To check if interpretable results are competitive, we compared their performance to K-Nearest Neighbors, Support Vector Machine, AdaBoost, Random Forest, LightGBM, XGBoost, Decision Trees and Logistic Regressor. We also performed experiments with fuzzy features and tested a feature selection technique to bring even more interpretability. The data used to feed the classifiers come from the FOT exams in 72 individuals, of which 25 were healthy, and 47 were diagnosed with sarcoidosis. Among the latter, 24 showed normal conditions by spirometry, and 23 showed respiratory changes. The results achieved high accuracy (AUC > 0.90) in two analyses performed (controls vs. individuals with sarcoidosis and normal spirometry and controls vs. individuals with sarcoidosis and altered spirometry). Genetic Programming and Grammatical Evolution were particularly beneficial because they provide intelligible expressions to make the classification. The observation of which features were selected most frequently also brought explainability to the study of sarcoidosis. CONCLUSIONS The proposed system may provide decision support for clinicians when they are struggling to give a confirmed clinical diagnosis. Clinicians may reference the prediction results and make better decisions, improving the productivity of pulmonary function services by AI-assisted workflow.
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Affiliation(s)
- Allan Danilo de Lima
- Electronic Engineering Post-Graduation Program, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Agnaldo J Lopes
- Pulmonary Function Laboratory, Faculty of Medical Sciences, State University of Rio de Janeiro, Rio de Janeiro, Brazil
| | - Jorge Luis Machado do Amaral
- Department of Electronics and Telecommunications Engineering, Rio de Janeiro State University, Rio de Janeiro, Brazil
| | - Pedro Lopes de Melo
- Biomedical Instrumentation Laboratory, Institute of Biology Roberto Alcantara Gomes and Laboratory of Clinical and Experimental Research in Vascular Biology (BioVasc), Rio de Janeiro State University, Rio de Janeiro, Brazil.
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41
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Assessment of Multi-Layer Perceptron Neural Network for Pulmonary Function Test’s Diagnosis Using ATS and ERS Respiratory Standard Parameters. COMPUTERS 2022. [DOI: 10.3390/computers11090130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The aim of the research work is to investigate the operability of the entire 23 pulmonary function parameters, which are stipulated by the American Thoracic Society (ATS) and the European Respiratory Society (ERS), to design a medical decision support system capable of classifying the pulmonary function tests into normal, obstructive, restrictive, or mixed cases. The 23 respiratory parameters specified by the ATS and the ERS guidelines, obtained from the Pulmonary Function Test (PFT) device, were employed as input features to a Multi-Layer Perceptron (MLP) neural network. Thirteen possible MLP Back Propagation (BP) algorithms were assessed. Three different categories of respiratory diseases were evaluated, namely obstructive, restrictive, and mixed conditions. The framework was applied on 201 PFT examinations: 103 normal and 98 abnormal cases. The PFT decision support system’s outcomes were compared with both the clinical truth (physician decision) and the PFT built-in diagnostic software. It yielded 92–99% and 87–92% accuracies on the training and the test sets, respectively. An 88–94% area under the receiver operating characteristic curve (ROC) was recorded on the test set. The system exceeded the performance of the PFT machine by 9%. All 23 ATS\ERS standard PFT parameters can be used as inputs to design a PFT decision support system, yielding a favorable performance compared with the literature and the PFT machine’s diagnosis program.
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42
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Zhou J, Wang P, Guo L, Cao J, Zhou M, Dai R. Automated interpretation of the pulmonary function test by a portable spirometer in Chinese adults. THE CLINICAL RESPIRATORY JOURNAL 2022; 16:555-561. [PMID: 35869604 PMCID: PMC9376142 DOI: 10.1111/crj.13525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 05/24/2022] [Accepted: 06/27/2022] [Indexed: 11/28/2022]
Abstract
Introduction A portable spirometer is a promising alternative to a traditional pulmonary function test (PFT) spirometer for respiratory function evaluation. Objectives This study aimed to investigate the accuracy of automated interpretation of the PFT measured by a portable Yue Cloud spirometer in Chinese adults. Methods The PFT was performed to evaluate subjects prospectively enrolled at Ruijin Hospital (n = 220). A Yue Cloud spirometer and a conventional Jaeger MasterScreen device were applied to each patient with a 20‐min quiescent period between each measurement. Pulmonary function parameters, including forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), peak expiratory flow (PEF), maximal expiratory flow at 25%, 50%, and 75% of the FVC (MEF25, MEF50, and MEF75, respectively), and maximal mid‐expiratory flow (MMEF), were compared by correlation analyses and Bland–Altman methods. The Yue Cloud spirometer automatically interpreted the PFT results, and a conventional strategy was performed to interpret the PFT results obtained by the Jaeger machine. Concordance of the categorization of pulmonary dysfunction, small airway dysfunction, and severity was analyzed by the kappa (κ) statistic. Results Significantly similar correlations of all variables measured with the two spirometers were observed (all p < 0.001). No significant bias was observed in any of the measured spirometer variables. A satisfactory concordance of pulmonary function and severity classification was observed between the automated interpretation results obtained with the Yue Cloud spirometer vs. a conventional spirometer interpretation strategy (all κ > 0.80). Conclusion The portable Yue Cloud spirometer not only yields reliable measurements of pulmonary function but also can automatically interpret the PFT results.
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Affiliation(s)
- Jun Zhou
- Department of Respiratory and Critical Care Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ping Wang
- Department of Respiratory and Critical Care Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Leixin Guo
- Department of Respiratory and Critical Care Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jin Cao
- Department of Respiratory and Critical Care Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Min Zhou
- Department of Respiratory and Critical Care Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Ranran Dai
- Department of Respiratory and Critical Care Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
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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: 33] [Impact Index Per Article: 16.5] [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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Stanojevic S, Kaminsky DA, Miller MR, Thompson B, Aliverti A, Barjaktarevic I, Cooper BG, Culver B, Derom E, Hall GL, Hallstrand TS, Leuppi JD, MacIntyre N, McCormack M, Rosenfeld M, Swenson ER. ERS/ATS technical standard on interpretive strategies for routine lung function tests. Eur Respir J 2022; 60:2101499. [PMID: 34949706 DOI: 10.1183/13993003.01499-2021] [Citation(s) in RCA: 386] [Impact Index Per Article: 193.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 11/18/2021] [Indexed: 01/21/2023]
Abstract
BACKGROUND Appropriate interpretation of pulmonary function tests (PFTs) involves the classification of observed values as within/outside the normal range based on a reference population of healthy individuals, integrating knowledge of physiological determinants of test results into functional classifications and integrating patterns with other clinical data to estimate prognosis. In 2005, the American Thoracic Society (ATS) and European Respiratory Society (ERS) jointly adopted technical standards for the interpretation of PFTs. We aimed to update the 2005 recommendations and incorporate evidence from recent literature to establish new standards for PFT interpretation. METHODS This technical standards document was developed by an international joint Task Force, appointed by the ERS/ATS with multidisciplinary expertise in conducting and interpreting PFTs and developing international standards. A comprehensive literature review was conducted and published evidence was reviewed. RESULTS Recommendations for the choice of reference equations and limits of normal of the healthy population to identify individuals with unusually low or high results are discussed. Interpretation strategies for bronchodilator responsiveness testing, limits of natural changes over time and severity are also updated. Interpretation of measurements made by spirometry, lung volumes and gas transfer are described as they relate to underlying pathophysiology with updated classification protocols of common impairments. CONCLUSIONS Interpretation of PFTs must be complemented with clinical expertise and consideration of the inherent biological variability of the test and the uncertainty of the test result to ensure appropriate interpretation of an individual's lung function measurements.
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Affiliation(s)
- Sanja Stanojevic
- Dept of Community Health and Epidemiology, Dalhousie University, Halifax, NS, Canada
| | - David A Kaminsky
- Pulmonary Disease and Critical Care Medicine, University of Vermont Larner College of Medicine, Burlington, VT, USA
| | - Martin R Miller
- Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Bruce Thompson
- Physiology Service, Dept of Respiratory Medicine, The Alfred Hospital and School of Health Sciences, Swinburne University of Technology, Melbourne, Australia
| | - Andrea Aliverti
- Dept of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Milan, Italy
| | - Igor Barjaktarevic
- Division of Pulmonary and Critical Care Medicine, University of California, Los Angeles, CA, USA
| | - Brendan G Cooper
- Lung Function and Sleep, Queen Elizabeth Hospital, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Bruce Culver
- Dept of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA, USA
| | - Eric Derom
- Dept of Respiratory Medicine, Ghent University, Ghent, Belgium
| | - Graham L Hall
- Children's Lung Health, Wal-yan Respiratory Research Centre, Telethon Kids Institute and School of Allied Health, Faculty of Health Science, Curtin University, Bentley, Australia
| | - Teal S Hallstrand
- Dept of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA, USA
| | - Joerg D Leuppi
- University Clinic of Medicine, Cantonal Hospital Basel, Liestal, Switzerland
- University Clinic of Medicine, University of Basel, Basel, Switzerland
| | - Neil MacIntyre
- Division of Pulmonary, Allergy, and Critical Care Medicine, Dept of Medicine, Duke University Medical Center, Durham, NC, USA
| | - Meredith McCormack
- Pulmonary Function Laboratory, Pulmonary and Critical Care Medicine, Johns Hopkins University, Baltimore, MD, USA
| | | | - Erik R Swenson
- Dept of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA, USA
- VA Puget Sound Health Care System, Seattle, WA, USA
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Avdeev SN, Emelyanov AV, Aisanov ZR, Sinopalnikov AI, Fomina DS, Nenasheva NM, Leshchenko IV, Zaikova-Khelimskaia IV, Vizel AA, Demko IV, Shaporova NL, Shulzhenko LV, Shabanov EA. Problems and opportunities to improve diagnosis of asthma and chronic obstructive pulmonary disease in Russia: resolution of advisory board. TERAPEVT ARKH 2022; 94:524-529. [DOI: 10.26442/00403660.2022.04.201487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 05/25/2022] [Indexed: 11/22/2022]
Abstract
Asthma and chronic obstructive pulmonary disease remain major problems of medicine, and still there is need to improve the level and quality of diagnosis of these diseases. Primary care physicians (general practitioners, therapists) should be involved widely and actively in this process. To simplify the diagnosis, special questionnaires have been developed, they can be used in a real clinical practice. Only this approach will bring statistical data closer to the true prevalence of these diseases and improve quality of their treatment.
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Lo YC, Varghese S, Blackley S, Seger DL, Blumenthal KG, Goss FR, Zhou L. Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing. FRONTIERS IN ALLERGY 2022; 3:904923. [PMID: 35769562 PMCID: PMC9234873 DOI: 10.3389/falgy.2022.904923] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 04/19/2022] [Indexed: 12/23/2022] Open
Abstract
Background Drug challenge tests serve to evaluate whether a patient is allergic to a medication. However, the allergy list in the electronic health record (EHR) is not consistently updated to reflect the results of the challenge, affecting clinicians' prescription decisions and contributing to inaccurate allergy labels, inappropriate drug-allergy alerts, and potentially ineffective, more toxic, and/or costly care. In this study, we used natural language processing (NLP) to automatically detect discrepancies between the EHR allergy list and drug challenge test results and to inform the clinical recommendations provided in a real-time allergy reconciliation module. Methods This study included patients who received drug challenge tests at the Mass General Brigham (MGB) Healthcare System between June 9, 2015 and January 5, 2022. At MGB, drug challenge tests are performed in allergy/immunology encounters with routine clinical documentation in notes and flowsheets. We developed a rule-based NLP tool to analyze and interpret the challenge test results. We compared these results against EHR allergy lists to detect potential discrepancies in allergy documentation and form a recommendation for reconciliation if a discrepancy was identified. To evaluate the capability of our tool in identifying discrepancies, we calculated the percentage of challenge test results that were not updated and the precision of the NLP algorithm for 200 randomly sampled encounters. Results Among 200 samples from 5,312 drug challenge tests, 59% challenged penicillin reactivity and 99% were negative. 42.0%, 61.5%, and 76.0% of the results were confirmed by flowsheets, NLP, or both, respectively. The precision of the NLP algorithm was 96.1%. Seven percent of patient allergy lists were not updated based on drug challenge test results. Flowsheets alone were used to identify 2.0% of these discrepancies, and NLP alone detected 5.0% of these discrepancies. Because challenge test results can be recorded in both flowsheets and clinical notes, the combined use of NLP and flowsheets can reliably detect 5.5% of discrepancies. Conclusion This NLP-based tool may be able to advance global delabeling efforts and the effectiveness of drug allergy assessments. In the real-time EHR environment, it can be used to examine patient allergy lists and identify drug allergy label discrepancies, mitigating patient risks.
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Affiliation(s)
- Ying-Chih Lo
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- *Correspondence: Ying-Chih Lo
| | - Sheril Varghese
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States
| | | | - Diane L. Seger
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States
- Mass General Brigham, Boston, MA, United States
| | - Kimberly G. Blumenthal
- Harvard Medical School, Boston, MA, United States
- Division of Rheumatology, Allergy, and Immunology, Department of Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Foster R. Goss
- Department of Emergency Medicine, University of Colorado School of Medicine, Aurora, CO, United States
| | - Li Zhou
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
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Wang Y, Li Y, Chen W, Zhang C, Liang L, Huang R, Liang J, Tu D, Gao Y, Zheng J, Zhong N. Deep learning for spirometry quality assurance with spirometric indices and curves. Respir Res 2022; 23:98. [PMID: 35448995 PMCID: PMC9028127 DOI: 10.1186/s12931-022-02014-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 04/02/2022] [Indexed: 11/18/2022] Open
Abstract
Background Spirometry quality assurance is a challenging task across levels of healthcare tiers, especially in primary care. Deep learning may serve as a support tool for enhancing spirometry quality. We aimed to develop a high accuracy and sensitive deep learning-based model aiming at assisting high-quality spirometry assurance. Methods Spirometry PDF files retrieved from one hospital between October 2017 and October 2020 were labeled according to ATS/ERS 2019 criteria and divided into training and internal test sets. Additional files from three hospitals were used for external testing. A deep learning-based model was constructed and assessed to determine acceptability, usability, and quality rating for FEV1 and FVC. System warning messages and patient instructions were also generated for general practitioners (GPs). Results A total of 16,502 files were labeled. Of these, 4592 curves were assigned to the internal test set, the remaining constituted the training set. In the internal test set, the model generated 95.1%, 92.4%, and 94.3% accuracy for FEV1 acceptability, usability, and rating. The accuracy for FVC acceptability, usability, and rating were 93.6%, 94.3%, and 92.2%. With the assistance of the model, the performance of GPs in terms of monthly percentages of good quality (A, B, or C grades) tests for FEV1 and FVC was higher by ~ 21% and ~ 36%, respectively. Conclusion The proposed model assisted GPs in spirometry quality assurance, resulting in enhancing the performance of GPs in quality control of spirometry. Supplementary Information The online version contains supplementary material available at 10.1186/s12931-022-02014-9.
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Affiliation(s)
- Yimin Wang
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Yanjiang Road 151, Guangzhou, 510120, Guangdong, People's Republic of China
| | - Yicong Li
- Tsinghua-Berkeley Shenzhen Institute, Tsinghua University, Shenzhen, 518055, China.,Huawei Cloud BU EI Innovation Laboratory, Huawei Technologies, Shenzhen, 518129, China
| | - Wenya Chen
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Yanjiang Road 151, Guangzhou, 510120, Guangdong, People's Republic of China
| | - Changzheng Zhang
- Huawei Cloud BU EI Innovation Laboratory, Huawei Technologies, Shenzhen, 518129, China
| | - Lijuan Liang
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Yanjiang Road 151, Guangzhou, 510120, Guangdong, People's Republic of China
| | - Ruibo Huang
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Yanjiang Road 151, Guangzhou, 510120, Guangdong, People's Republic of China
| | - Jianling Liang
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Yanjiang Road 151, Guangzhou, 510120, Guangdong, People's Republic of China
| | - Dandan Tu
- Huawei Cloud BU EI Innovation Laboratory, Huawei Technologies, Shenzhen, 518129, China
| | - Yi Gao
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Yanjiang Road 151, Guangzhou, 510120, Guangdong, People's Republic of China.
| | - Jinping Zheng
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Yanjiang Road 151, Guangzhou, 510120, Guangdong, People's Republic of China.
| | - Nanshan Zhong
- National Center for Respiratory Medicine, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, First Affiliated Hospital of Guangzhou Medical University, Yanjiang Road 151, Guangzhou, 510120, Guangdong, People's Republic of China.
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Ramakrishnan S, Beaufils F, De Brandt J, Viney K, Bradley C, Cottin V, Hassan M, Cruz J. European Respiratory Society International Congress 2021: highlights from best-abstract awardees. Breathe (Sheff) 2022; 18:210176. [PMID: 36338250 PMCID: PMC9584552 DOI: 10.1183/20734735.0176-2021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 02/11/2022] [Indexed: 12/26/2022] Open
Abstract
This article provides an overview of some of the highlights of the @EuroRespSoc Congress 2021 from the perspective of the best-abstract awardees of the ERS Assemblies @EarlyCareerERS @OrphaLung https://bit.ly/3JCjHYS.
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Affiliation(s)
- Sanjay Ramakrishnan
- Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK,School of Medical and Health Sciences, Edith Cowan University, Perth, Australia,These authors contributed equally
| | - Fabien Beaufils
- Univ. Bordeaux, Centre de Recherche Cardio-thoracique de Bordeaux, INSERM U1045, Bordeaux Imaging Center, Bordeaux, France,CHU Bordeaux, Service d'Exploration Fonctionnelle Respiratoire, Bordeaux, France,These authors contributed equally
| | - Jana De Brandt
- Faculty of Rehabilitation Sciences, Rehabilitation Research Center REVAL, Biomedical Research Institute BIOMED, Hasselt University, Hasselt, Belgium,Faculty of Medicine, Dept of Community Medicine and Rehabilitation, Section of Physiotherapy, Umeå University, Umeå, Sweden,These authors contributed equally
| | - Kerri Viney
- Global Tuberculosis Programme, World Health Organization, Geneva, Switzerland,These authors contributed equally
| | - Claire Bradley
- Leeds Teaching Hospitals, Leeds, UK,These authors contributed equally
| | - Vincent Cottin
- National French Reference Coordinating Center for Rare Pulmonary Diseases, Louis Pradel Hospital and Hospices Civils de Lyon, Université de Lyon, Université Claude Bernard Lyon 1, INRAE, member of ERN-LUNG, Lyon, France,These authors contributed equally
| | - Maged Hassan
- Chest Diseases Dept, Alexandria University Faculty of Medicine, Alexandria, Egypt,These authors contributed equally
| | - Joana Cruz
- Center for Innovative Care and Health Technology (ciTechCare), School of Health Sciences (ESSLei), Polytechnic of Leiria, Leiria, Portugal,Corresponding author: Joana Cruz ()
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Choudhury S, Chohan A, Dadhwal R, Vakil AP, Franco R, Taweesedt PT. Applications of artificial intelligence in common pulmonary diseases. Artif Intell Med Imaging 2022; 3:1-7. [DOI: 10.35711/aimi.v3.i1.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/14/2022] [Accepted: 02/23/2022] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a branch of computer science where machines are trained to imitate human-level intelligence and perform well-defined tasks. AI can provide accurate results as well as analyze vast amounts of data that cannot be analyzed via conventional statistical methods. AI has been utilized in pulmonary medicine for almost two decades and its utilization continues to expand. AI can help in making diagnoses and predicting outcomes in pulmonary diseases based on clinical data, chest imaging, lung pathology, and pulmonary function testing. AI-based applications enable physicians to use enormous amounts of data and improve their precision in the treatment of pulmonary diseases. Given the growing role of AI in pulmonary medicine, it is important for practitioners caring for patients with pulmonary diseases to understand how AI can work in order to implement it into clinical practices and improve patient care. The goal of this mini-review is to discuss the use of AI in pulmonary medicine and imaging in cases of obstructive lung disease, interstitial lung disease, infections, nodules, and lung cancer.
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Affiliation(s)
- Saiara Choudhury
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Asad Chohan
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Rahul Dadhwal
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Abhay P Vakil
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Rene Franco
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
| | - Pahnwat Tonya Taweesedt
- Department of Pulmonary Medicine, Corpus Christi Medical Center, Corpus Christi, TX 78411, United States
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Abstract
Purpose of Review To review the data supporting the use of telemedicine (TM) and to provide practical guidance for practitioners to optimize the care of their asthmatic patients. Recent Findings Previous to the pandemic, TM was little used in various aspects of asthma care. Since the pandemic, TM has been increasingly used in new ways to care for asthma patients at various locations. In addition to direct-to-consumer visits for asthma care, other forms of telehealth visits have been increasing such as facilitated visits, asynchronous, remote patient monitoring, e-consults, and mHealth. Moreover, patient and provider satisfaction with the use of TM has been increasing and is comparable at times with face-to-face visits. In this review, best practices for starting a telemedicine asthma service with patients at home, distant clinic sites, and various other locations, including school-based asthma programs, are reviewed. Summary TM is a valuable adjunct to face-to-face visits for asthma care. Following the recommended best practices can strengthen the implementation of a telemedicine asthma program (TMAP) into clinical practice. Providers must be vigilant in keeping current with the various nuances required for asthma telemedicine care in preparation for the post-pandemic environment.
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Affiliation(s)
- Yudy K Persaud
- Division of Allergy, BronxCare Hospital Systems, Bronx, NY, USA.
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