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Labaki WW, Agusti A, Bhatt SP, Bodduluri S, Criner GJ, Fabbri LM, Halpin DMG, Lynch DA, Mannino DM, Miravitlles M, Papi A, Sin DD, Washko GR, Kazerooni EA, Han MK. Leveraging Computed Tomography Imaging to Detect Chronic Obstructive Pulmonary Disease and Concomitant Chronic Diseases. Am J Respir Crit Care Med 2024; 210:281-287. [PMID: 38843079 DOI: 10.1164/rccm.202402-0407pp] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Accepted: 06/04/2024] [Indexed: 08/02/2024] Open
Affiliation(s)
| | - Alvar Agusti
- Cathedra Salut Respiratoria, University of Barcelona, Barcelona, Spain
- Pulmonary Service, Respiratory Institute, Clinic Barcelona, Barcelona, Spain
- Fundació Clinic, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Barcelona, Spain
| | - Surya P Bhatt
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Sandeep Bodduluri
- Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Gerard J Criner
- Department of Thoracic Medicine and Surgery, Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania
| | - Leonardo M Fabbri
- Section of Respiratory Medicine, University of Ferrara, Ferrara, Italy
| | - David M G Halpin
- Respiratory Medicine, University of Exeter Medical School, Exeter, United Kingdom
| | - David A Lynch
- Department of Radiology, National Jewish Health, Denver, Colorado
| | - David M Mannino
- Department of Medicine, University of Kentucky, Lexington, Kentucky
| | - Marc Miravitlles
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias, Barcelona, Spain
- Neumología, Hospital Universitari Vall d'Hebron/Vall d'Hebron Institut de Recerca, Barcelona, Spain
| | - Alberto Papi
- Section of Respiratory Medicine, University of Ferrara, Ferrara, Italy
| | - Don D Sin
- Centre for Heart Lung Innovation, St. Paul's Hospital and University of British Columbia, Vancouver, British Columbia, Canada
- Division of Respiratory Medicine, University of British Columbia, Vancouver, British Columbia, Canada
| | - George R Washko
- Division of Pulmonary and Critical Care Medicine and
- Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Ella A Kazerooni
- Division of Pulmonary and Critical Care Medicine and
- Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - MeiLan K Han
- Division of Pulmonary and Critical Care Medicine and
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2
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Almeida SD, Norajitra T, Lüth CT, Wald T, Weru V, Nolden M, Jäger PF, von Stackelberg O, Heußel CP, Weinheimer O, Biederer J, Kauczor HU, Maier-Hein K. Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT. Eur Radiol 2024; 34:4379-4392. [PMID: 38150075 PMCID: PMC11213737 DOI: 10.1007/s00330-023-10540-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 11/13/2023] [Accepted: 12/11/2023] [Indexed: 12/28/2023]
Abstract
OBJECTIVES To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity. MATERIALS AND METHODS Paired inspiratory/expiratory CT and clinical data from COPDGene and COSYCONET cohort studies were included. COPDGene data served as training/validation/test data sets (N = 3144/786/1310) and COSYCONET as external test set (N = 446). To differentiate low-risk (healthy/minimal disease, [GOLD 0]) from COPD patients (GOLD 1-4), the self-supervised DL model learned semantic information from 50 × 50 × 50 voxel samples from segmented intact lungs. An anomaly detection approach was trained to quantify lung abnormalities related to COPD, as regional deviations. Four supervised DL models were run for comparison. The clinical and radiological predictive power of the proposed anomaly score was assessed using linear mixed effects models (LMM). RESULTS The proposed approach achieved an area under the curve of 84.3 ± 0.3 (p < 0.001) for COPDGene and 76.3 ± 0.6 (p < 0.001) for COSYCONET, outperforming supervised models even when including only inspiratory CT. Anomaly scores significantly improved fitting of LMM for predicting lung function, health status, and quantitative CT features (emphysema/air trapping; p < 0.001). Higher anomaly scores were significantly associated with exacerbations for both cohorts (p < 0.001) and greater dyspnea scores for COPDGene (p < 0.001). CONCLUSION Quantifying heterogeneous COPD manifestations as anomaly offers advantages over supervised methods and was found to be predictive for lung function impairment and morphology deterioration. CLINICAL RELEVANCE STATEMENT Using deep learning, lung manifestations of COPD can be identified as deviations from normal-appearing chest CT and attributed an anomaly score which is consistent with decreased pulmonary function, emphysema, and air trapping. KEY POINTS • A self-supervised DL anomaly detection method discriminated low-risk individuals and COPD subjects, outperforming classic DL methods on two datasets (COPDGene AUC = 84.3%, COSYCONET AUC = 76.3%). • Our contrastive task exhibits robust performance even without the inclusion of expiratory images, while voxel-based methods demonstrate significant performance enhancement when incorporating expiratory images, in the COPDGene dataset. • Anomaly scores improved the fitting of linear mixed effects models in predicting clinical parameters and imaging alterations (p < 0.001) and were directly associated with clinical outcomes (p < 0.001).
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Affiliation(s)
- Silvia D Almeida
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany.
- Medical Faculty, Heidelberg University, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Medical Center, Heidelberg, Germany.
| | - Tobias Norajitra
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany
| | - Carsten T Lüth
- Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tassilo Wald
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Vivienn Weru
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marco Nolden
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany
- Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Paul F Jäger
- Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oyunbileg von Stackelberg
- Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Claus Peter Heußel
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University Hospital, Heidelberg, Germany
| | - Oliver Weinheimer
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jürgen Biederer
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Faculty of Medicine, University of Latvia, Raina Bulvaris 19, Riga, LV-1586, Latvia
- Faculty of Medicine, Christian-Albrechts-Universität zu Kiel, D-24098, Kiel, Germany
| | - Hans-Ulrich Kauczor
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.
- Translational Lung Research Center Heidelberg (TLRC), Member of the German Lung Research Center (DZL), Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and Heidelberg University Medical Center, Heidelberg, Germany.
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany.
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Grenier PA. COPD: artificial intelligence detects and quantifies anomalies on chest CT enabling prediction of disease severity. Eur Radiol 2024; 34:4376-4378. [PMID: 38253906 DOI: 10.1007/s00330-024-10601-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 01/08/2024] [Accepted: 01/11/2024] [Indexed: 01/24/2024]
Affiliation(s)
- Philippe A Grenier
- Department of Clinical Research and Innovation, Hôpital Foch, Suresnes, France.
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Maldonado-Franco A, Giraldo-Cadavid LF, Tuta-Quintero E, Cagy M, Bastidas Goyes AR, Botero-Rosas DA. Curve-Modelling and Machine Learning for a Better COPD Diagnosis. Int J Chron Obstruct Pulmon Dis 2024; 19:1333-1343. [PMID: 38895045 PMCID: PMC11182754 DOI: 10.2147/copd.s456390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 05/20/2024] [Indexed: 06/21/2024] Open
Abstract
Background Development of new tools in artificial intelligence has an outstanding performance in the recognition of multidimensional patterns, which is why they have proven to be useful in the diagnosis of Chronic Obstructive Pulmonary Disease (COPD). Methods This was an observational analytical single-centre study in patients with spirometry performed in outpatient medical care. The segment that goes from the peak expiratory flow to the forced vital capacity was modelled with quadratic polynomials, the coefficients obtained were used to train and test neural networks in the task of classifying patients with COPD. Results A total of 695 patient records were included in the analysis. The COPD group was significantly older than the No COPD group. The pre-bronchodilator (Pre BD) and post-bronchodilator (Post BD) spirometric curves were modelled with a quadratic polynomial, and the coefficients obtained were used to feed three neural networks (Pre BD, Post BD and all coefficients). The best neural network was the one that used the post-bronchodilator coefficients, which has an input layer of 3 neurons and three hidden layers with sigmoid activation function and two neurons in the output layer with softmax activation function. This system had an accuracy of 92.9% accuracy, a sensitivity of 88.2% and a specificity of 94.3% when assessed using expert judgment as the reference test. It also showed better performance than the current gold standard, especially in specificity and negative predictive value. Conclusion Artificial Neural Networks fed with coefficients obtained from quadratic and cubic polynomials have interesting potential of emulating the clinical diagnostic process and can become an important aid in primary care to help diagnose COPD in an early stage.
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Affiliation(s)
| | - Luis F Giraldo-Cadavid
- School of Medicine, Universidad de La Sabana, Chía, Colombia
- Interventional Pulmonology Service, Fundación Neumológica Colombiana, Bogotá, DC, Colombia
| | | | - Mauricio Cagy
- Biomedical Engineering Program, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brasil
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Xu L, Si H, Zhuang F, Li C, Zhang L, Zhao Y, Chen T, Dong Y, Wang T, Hou L, Hu T, Sun T, She Y, Hu X, Xie D, Wu J, Wu C, Zhao D, Chen C. Predicting therapeutic response to neoadjuvant immunotherapy based on an integration model in resectable stage IIIA (N2) non-small cell lung cancer. J Thorac Cardiovasc Surg 2024:S0022-5223(24)00437-9. [PMID: 38763304 DOI: 10.1016/j.jtcvs.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/28/2024] [Accepted: 05/08/2024] [Indexed: 05/21/2024]
Abstract
OBJECTIVE Accurately predicting response during neoadjuvant chemoimmunotherapy for resectable non-small cell lung cancer remains clinically challenging. In this study, we investigated the effectiveness of blood-based tumor mutational burden (bTMB) and a deep learning (DL) model in predicting major pathologic response (MPR) and survival from a phase 2 trial. METHODS Blood samples were prospectively collected from 45 patients with stage IIIA (N2) non-small cell lung cancer undergoing neoadjuvant chemoimmunotherapy. An integrated model, combining the computed tomography-based DL score, bTMB, and clinical factors, was developed to predict tumor response to neoadjuvant chemoimmunotherapy. RESULTS At baseline, bTMB were detected in 77.8% (35 of 45) of patients. Baseline bTMB ≥11 mutations/megabase was associated with significantly greater MPR rates (77.8% vs 38.5%, P = .042), and longer disease-free survival (P = .043), but not overall survival (P = .131), compared with bTMB <11 mutations/megabase in 35 patients with bTMB available. The developed DL model achieved an area under the curve of 0.703 in all patients. Importantly, the predictive performance of the integrated model improved to an area under the curve of 0.820 when combining the DL score with bTMB and clinical factors. Baseline circulating tumor DNA (ctDNA) status was not associated with pathologic response and survival. Compared with ctDNA residual, ctDNA clearance before surgery was associated with significantly greater MPR rates (88.2% vs 11.1%, P < .001) and improved disease-free survival (P = .010). CONCLUSIONS The integrated model shows promise as a predictor of tumor response to neoadjuvant chemoimmunotherapy. Serial ctDNA dynamics provide a reliable tool for monitoring tumor response.
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Affiliation(s)
- Long Xu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Haojie Si
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Fenghui Zhuang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Chongwu Li
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Lei Zhang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yue Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Tao Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yichen Dong
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Tingting Wang
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Likun Hou
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Tao Hu
- Department of Medicine, Amoy Diagnostics Co, Ltd, Xiamen, China
| | - Tianlin Sun
- Department of Medicine, Amoy Diagnostics Co, Ltd, Xiamen, China
| | - Yunlang She
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xuefei Hu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Dong Xie
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Junqi Wu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Chunyan Wu
- Department of Pathology, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Deping Zhao
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
| | - Chang Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
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Aaron SD, Montes de Oca M, Celli B, Bhatt SP, Bourbeau J, Criner GJ, DeMeo DL, Halpin DMG, Han MK, Hurst JR, Krishnan JK, Mannino D, van Boven JFM, Vogelmeier CF, Wedzicha JA, Yawn BP, Martinez FJ. Early Diagnosis and Treatment of Chronic Obstructive Pulmonary Disease: The Costs and Benefits of Case Finding. Am J Respir Crit Care Med 2024; 209:928-937. [PMID: 38358788 DOI: 10.1164/rccm.202311-2120pp] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Accepted: 02/14/2024] [Indexed: 02/16/2024] Open
Affiliation(s)
- Shawn D Aaron
- The Ottawa Hospital Research Institute, Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada
| | - Maria Montes de Oca
- Universidad Central de Venezuela, Caracas, Venezuela
- Hospital Centro Médico de Caracas, Caracas, Venezuela
| | | | - Surya P Bhatt
- Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Jean Bourbeau
- Department of Medicine, McGill University, Montreal, Quebec, Canada
| | - Gerard J Criner
- Lewis Katz School of Medicine at Temple University, Philadelphia, Pennsylvania
| | - Dawn L DeMeo
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - David M G Halpin
- University of Exeter Medical School, University of Exeter, Exeter, United Kingdom
| | - MeiLan K Han
- Division of Pulmonary & Critical Care, University of Michigan, Ann Arbor, Michigan
| | - John R Hurst
- UCL Respiratory, University College London, London, United Kingdom
| | - Jamuna K Krishnan
- Division of Pulmonary and Critical Care, Weill Cornell Medicine, New York, New York
| | - David Mannino
- College of Medicine, University of Kentucky, Lexington, Kentucky
| | - Job F M van Boven
- Department of Clinical Pharmacy & Pharmacology, University Medical Center Groningen, Groningen Research Institute for Asthma and COPD, University of Groningen, Groningen, The Netherlands
| | - Claus F Vogelmeier
- Philipps-Universität Marburg, German Center for Lung Research, Marburg, Germany
| | - Jadwiga A Wedzicha
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Barbara P Yawn
- Department of Family Medicine and Community Health, University of Minnesota, Minneapolis, Minnesota; and
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Zou X, Ren Y, Yang H, Zou M, Meng P, Zhang L, Gong M, Ding W, Han L, Zhang T. Screening and staging of chronic obstructive pulmonary disease with deep learning based on chest X-ray images and clinical parameters. BMC Pulm Med 2024; 24:153. [PMID: 38532368 DOI: 10.1186/s12890-024-02945-7] [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: 09/13/2023] [Accepted: 03/01/2024] [Indexed: 03/28/2024] Open
Abstract
BACKGROUND Chronic obstructive pulmonary disease (COPD) is underdiagnosed with the current gold standard measure pulmonary function test (PFT). A more sensitive and simple option for early detection and severity evaluation of COPD could benefit practitioners and patients. METHODS In this multicenter retrospective study, frontal chest X-ray (CXR) images and related clinical information of 1055 participants were collected and processed. Different deep learning algorithms and transfer learning models were trained to classify COPD based on clinical data and CXR images from 666 subjects, and validated in internal test set based on 284 participants. External test including 105 participants was also performed to verify the generalization ability of the learning algorithms in diagnosing COPD. Meanwhile, the model was further used to evaluate disease severity of COPD by predicting different grads. RESULTS The Ensemble model showed an AUC of 0.969 in distinguishing COPD by simultaneously extracting fusion features of clinical parameters and CXR images in internal test, better than models that used clinical parameters (AUC = 0.963) or images (AUC = 0.946) only. For the external test set, the AUC slightly declined to 0.934 in predicting COPD based on clinical parameters and CXR images. When applying the Ensemble model to determine disease severity of COPD, the AUC reached 0.894 for three-classification and 0.852 for five-classification respectively. CONCLUSION The present study used DL algorithms to screen COPD and predict disease severity based on CXR imaging and clinical parameters. The models showed good performance and the approach might be an effective case-finding tool with low radiation dose for COPD diagnosis and staging.
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Affiliation(s)
- XiaoLing Zou
- Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, China
| | - Yong Ren
- Scientific research project department, Guangdong Artificial Intelligence and Digital Economy Laboratory (Guangzhou), Pazhou Lab, Guangzhou, China
- Shensi lab, Shenzhen Institute for Advanced Study, UESTC, Shenzhen, China
| | - HaiLing Yang
- Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, China
| | - ManMan Zou
- Department of Pulmonary and Critical Care Medicine, Dongguan People's Hospital, Dongguan, China
| | - Ping Meng
- Department of Pulmonary and Critical Care Medicine, the Six Affiliated Hospital of Guangzhou Medical University, Qingyuan People's Hospital, Qingyuan, China
| | - LiYi Zhang
- Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, China
| | - MingJuan Gong
- Department of Internal Medicine, Huazhou Hospital of Traditional Chinese Medicine, Huazhou, China
| | - WenWen Ding
- Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, China
| | - LanQing Han
- Center for artificial intelligence in medicine, Research Institute of Tsinghua, Pearl River Delta, Guangzhou, China.
| | - TianTuo Zhang
- Department of Pulmonary and Critical Care Medicine, The Third Affiliated Hospital of Sun Yat-sen University, Institute of Respiratory Diseases of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, China.
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8
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Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [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: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
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Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
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9
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Almeida SD, Norajitra T, Lüth CT, Wald T, Weru V, Nolden M, Jäger PF, von Stackelberg O, Heußel CP, Weinheimer O, Biederer J, Kauczor HU, Maier-Hein K. Capturing COPD heterogeneity: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography. Front Med (Lausanne) 2024; 11:1360706. [PMID: 38495118 PMCID: PMC10941845 DOI: 10.3389/fmed.2024.1360706] [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: 12/23/2023] [Accepted: 02/20/2024] [Indexed: 03/19/2024] Open
Abstract
Background Chronic obstructive pulmonary disease (COPD) poses a substantial global health burden, demanding advanced diagnostic tools for early detection and accurate phenotyping. In this line, this study seeks to enhance COPD characterization on chest computed tomography (CT) by comparing the spatial and quantitative relationships between traditional parametric response mapping (PRM) and a novel self-supervised anomaly detection approach, and to unveil potential additional insights into the dynamic transitional stages of COPD. Methods Non-contrast inspiratory and expiratory CT of 1,310 never-smoker and GOLD 0 individuals and COPD patients (GOLD 1-4) from the COPDGene dataset were retrospectively evaluated. A novel self-supervised anomaly detection approach was applied to quantify lung abnormalities associated with COPD, as regional deviations. These regional anomaly scores were qualitatively and quantitatively compared, per GOLD class, to PRM volumes (emphysema: PRMEmph, functional small-airway disease: PRMfSAD) and to a Principal Component Analysis (PCA) and Clustering, applied on the self-supervised latent space. Its relationships to pulmonary function tests (PFTs) were also evaluated. Results Initial t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the self-supervised latent space highlighted distinct spatial patterns, revealing clear separations between regions with and without emphysema and air trapping. Four stable clusters were identified among this latent space by the PCA and Cluster Analysis. As the GOLD stage increased, PRMEmph, PRMfSAD, anomaly score, and Cluster 3 volumes exhibited escalating trends, contrasting with a decline in Cluster 2. The patient-wise anomaly scores significantly differed across GOLD stages (p < 0.01), except for never-smokers and GOLD 0 patients. In contrast, PRMEmph, PRMfSAD, and cluster classes showed fewer significant differences. Pearson correlation coefficients revealed moderate anomaly score correlations to PFTs (0.41-0.68), except for the functional residual capacity and smoking duration. The anomaly score was correlated with PRMEmph (r = 0.66, p < 0.01) and PRMfSAD (r = 0.61, p < 0.01). Anomaly scores significantly improved fitting of PRM-adjusted multivariate models for predicting clinical parameters (p < 0.001). Bland-Altman plots revealed that volume agreement between PRM-derived volumes and clusters was not constant across the range of measurements. Conclusion Our study highlights the synergistic utility of the anomaly detection approach and traditional PRM in capturing the nuanced heterogeneity of COPD. The observed disparities in spatial patterns, cluster dynamics, and correlations with PFTs underscore the distinct - yet complementary - strengths of these methods. Integrating anomaly detection and PRM offers a promising avenue for understanding of COPD pathophysiology, potentially informing more tailored diagnostic and intervention approaches to improve patient outcomes.
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Affiliation(s)
- Silvia D. Almeida
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Medical Faculty, Heidelberg University, Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, A Partnership Between DKFZ and Heidelberg University Medical Center, Heidelberg, Germany
| | - Tobias Norajitra
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
| | - Carsten T. Lüth
- Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tassilo Wald
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Vivienn Weru
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Marco Nolden
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Paul F. Jäger
- Interactive Machine Learning Group (IML), German Cancer Research Center (DKFZ), Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oyunbileg von Stackelberg
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Claus Peter Heußel
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Diagnostic and Interventional Radiology with Nuclear Medicine, Thoraxklinik at University Hospital, Heidelberg, Germany
| | - Oliver Weinheimer
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jürgen Biederer
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
- Faculty of Medicine, University of Latvia, Riga, Latvia
- Faculty of Medicine, Christian-Albrechts-Universität zu Kiel, Kiel, Germany
| | - Hans-Ulrich Kauczor
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Translational Lung Research Center Heidelberg (TLRC), German Center for Lung Research (DZL), Heidelberg, Germany
- National Center for Tumor Diseases (NCT), NCT Heidelberg, A Partnership Between DKFZ and Heidelberg University Medical Center, Heidelberg, Germany
- Helmholtz Imaging, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Pattern Analysis and Learning Group, Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
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10
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Antão J, de Mast J, Marques A, Franssen FME, Spruit MA, Deng Q. Demystification of artificial intelligence for respiratory clinicians managing patients with obstructive lung diseases. Expert Rev Respir Med 2023; 17:1207-1219. [PMID: 38270524 DOI: 10.1080/17476348.2024.2302940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 01/04/2024] [Indexed: 01/26/2024]
Abstract
INTRODUCTION Asthma and chronic obstructive pulmonary disease (COPD) are leading causes of morbidity and mortality worldwide. Despite all available diagnostics and treatments, these conditions pose a significant individual, economic and social burden. Artificial intelligence (AI) promises to support clinical decision-making processes by optimizing diagnosis and treatment strategies of these heterogeneous and complex chronic respiratory diseases. Its capabilities extend to predicting exacerbation risk, disease progression and mortality, providing healthcare professionals with valuable insights for more effective care. Nevertheless, the knowledge gap between respiratory clinicians and data scientists remains a major constraint for wide application of AI and may hinder future progress. This narrative review aims to bridge this gap and encourage AI deployment by explaining its methodology and added value in asthma and COPD diagnosis and treatment. AREAS COVERED This review offers an overview of the fundamental concepts of AI and machine learning, outlines the key steps in building a model, provides examples of their applicability in asthma and COPD care, and discusses barriers to their implementation. EXPERT OPINION Machine learning can advance our understanding of asthma and COPD, enabling personalized therapy and better outcomes. Further research and validation are needed to ensure the development of clinically meaningful and generalizable models.
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Affiliation(s)
- Joana Antão
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Jeroen de Mast
- Economics and Business, University of Amsterdam, Amsterdam, The Netherlands
| | - Alda Marques
- Lab3R - Respiratory Research and Rehabilitation Laboratory, School of Health Sciences, University of Aveiro (ESSUA), Aveiro, Portugal
- iBiMED - Institute of Biomedicine, Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
| | - Frits M E Franssen
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Martijn A Spruit
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | - Qichen Deng
- Department of Research and Development, Ciro, Horn, The Netherlands
- Department of Respiratory Medicine, Maastricht University Medical Centre, NUTRIM School of Nutrition and Translational Research in Metabolism, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
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11
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Xu S, Deo RC, Soar J, Barua PD, Faust O, Homaira N, Jaffe A, Kabir AL, Acharya UR. Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 241:107746. [PMID: 37660550 DOI: 10.1016/j.cmpb.2023.107746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 07/07/2023] [Accepted: 08/02/2023] [Indexed: 09/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality. METHODS We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance. RESULTS We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent. CONCLUSIONS Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.
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Affiliation(s)
- Shuting Xu
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia; Cogninet Australia, Sydney, NSW 2010, Australia
| | - Ravinesh C Deo
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia
| | - Jeffrey Soar
- School of Business, University of Southern Queensland, Australia
| | - Prabal Datta Barua
- Cogninet Australia, Sydney, NSW 2010, Australia; School of Business, University of Southern Queensland, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia; Australian International Institute of Higher Education, Sydney, NSW 2000, Australia; School of Science Technology, University of New England, Australia; School of Biosciences, Taylor's University, Malaysia; School of Computing, SRM Institute of Science and Technology, India; School of Science and Technology, Kumamoto University, Japan; Sydney School of Education and Social Work, University of Sydney, Australia.
| | - Oliver Faust
- School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, UK
| | - Nusrat Homaira
- School of Clinical Medicine, University of New South Wales, Australia; Sydney Children's Hospital, Sydney, Australia; James P. Grant School of Public Health, Dhaka, Bangladesh
| | - Adam Jaffe
- School of Clinical Medicine, University of New South Wales, Australia; Sydney Children's Hospital, Sydney, Australia
| | | | - U Rajendra Acharya
- School of Mathematics Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia; School of Science and Technology, Kumamoto University, Japan
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12
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O'Dowd EL, Tietzova I, Bartlett E, Devaraj A, Biederer J, Brambilla M, Brunelli A, Chorostowska J, Decaluwe H, Deruysscher D, De Wever W, Donoghue M, Fabre A, Gaga M, van Geffen W, Hardavella G, Kauczor HU, Kerpel-Fronius A, van Meerbeeck J, Nagavci B, Nestle U, Novoa N, Prosch H, Prokop M, Putora PM, Rawlinson J, Revel MP, Snoeckx A, Veronesi G, Vliegenthart R, Weckbach S, Blum TG, Baldwin DR. ERS/ESTS/ESTRO/ESR/ESTI/EFOMP statement on management of incidental findings from low dose CT screening for lung cancer. Eur J Cardiothorac Surg 2023; 64:ezad302. [PMID: 37804174 PMCID: PMC10876118 DOI: 10.1093/ejcts/ezad302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 06/06/2023] [Indexed: 10/09/2023] Open
Abstract
BACKGROUND Screening for lung cancer with low radiation dose computed tomography has a strong evidence base, is being introduced in several European countries and is recommended as a new targeted cancer screening programme. The imperative now is to ensure that implementation follows an evidence-based process that will ensure clinical and cost effectiveness. This European Respiratory Society (ERS) task force was formed to provide an expert consensus for the management of incidental findings which can be adapted and followed during implementation. METHODS A multi-European society collaborative group was convened. 23 topics were identified, primarily from an ERS statement on lung cancer screening, and a systematic review of the literature was conducted according to ERS standards. Initial review of abstracts was completed and full text was provided to members of the group for each topic. Sections were edited and the final document approved by all members and the ERS Science Council. RESULTS Nine topics considered most important and frequent were reviewed as standalone topics (interstitial lung abnormalities, emphysema, bronchiectasis, consolidation, coronary calcification, aortic valve disease, mediastinal mass, mediastinal lymph nodes and thyroid abnormalities). Other topics considered of lower importance or infrequent were grouped into generic categories, suitable for general statements. CONCLUSIONS This European collaborative group has produced an incidental findings statement that can be followed during lung cancer screening. It will ensure that an evidence-based approach is used for reporting and managing incidental findings, which will mean that harms are minimised and any programme is as cost-effective as possible.
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Affiliation(s)
- Emma L O'Dowd
- Nottingham University Hospitals NHS Trust, Nottingham, UK
- University of Nottingham, Faculty of Medicine and Health Sciences, Nottingham, UK
| | - Ilona Tietzova
- Charles University, First Faculty of Medicine, Department of Tuberculosis and Respiratory Diseases, Prague, Czech Republic
| | - Emily Bartlett
- Royal Brompton and Harefield NHS Foundation Trust, Radiology, London, UK
| | - Anand Devaraj
- Royal Brompton and Harefield NHS Foundation Trust, Radiology, London, UK
| | - Jürgen Biederer
- University of Heidelberg, Diagnostic and Interventional Radiology, Heidelberg, Germany
- German Center for Lung Research DZL, Translational Lung Research Center TLRC, Heidelberg, Germany
- University of Latvia, Faculty of Medicine, Riga, Latvia
- Christian-Albrechts-Universität zu Kiel, Faculty of Medicine, Kiel, Germany
| | - Marco Brambilla
- Azienda Ospedaliero-Universitaria Maggiore della Carità di Novara, Novara, Italy
| | | | - Joanna Chorostowska
- Institute of Tuberculosis and Lung Diseases, Warsaw, Genetics and Clinical Immunology, Warsaw, Poland
| | | | - Dirk Deruysscher
- Maastricht University Medical Centre, Department of Radiation Oncology (MAASTRO Clinic), GROW-School for Oncology and Developmental Biology, Limburg, The Netherlands
| | - Walter De Wever
- Universitaire Ziekenhuizen Leuven, Radiology, Leuven, Belgium
| | | | - Aurelie Fabre
- University College Dublin School of Medicine, Histopathology, Dublin, Ireland
| | - Mina Gaga
- Sotiria General Hospital of Chest Diseases of Athens, 7th Respiratory Medicine Department, Athens, Greece
| | - Wouter van Geffen
- Medical Centre Leeuwarden, Department of Respiratory Medicine, Leeuwarden, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, Groningen, The Netherlands
| | - Georgia Hardavella
- Sotiria General Hospital of Chest Diseases of Athens, Respiratory Medicine, Athens, Greece
| | - Hans-Ulrich Kauczor
- University of Heidelberg, Diagnostic and Interventional Radiology, Heidelberg, Germany
- German Center for Lung Research DZL, Translational Lung Research Center TLRC, Heidelberg, Germany
| | - Anna Kerpel-Fronius
- National Koranyi Institute of Pulmonology, Department of Radiology, Budapest, Hungary
| | | | - Blin Nagavci
- Institute for Evidence in Medicine, Medical Center – University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Ursula Nestle
- Kliniken Maria Hilf GmbH Monchengladbach, Nordrhein-Westfalen, Germany
| | - Nuria Novoa
- University Hospital of Salamanca, Thoracic Surgery, Salamanca, Spain
| | - Helmut Prosch
- Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Vienna, Austria
| | - Mathias Prokop
- Radboud University Nijmegen Medical Center, Department of Radiology, Nijmegen, The Netherlands
| | - Paul Martin Putora
- Kantonsspital Sankt Gallen, Radiation Oncology, Sankt Gallen, Switzerland
- Inselspital Universitatsspital Bern, Radiation Oncology, Bern, Switzerland
| | | | - Marie-Pierre Revel
- Cochin Hospital, APHP, Radiology Department, Paris, France
- Université de Paris, Paris, France
| | | | - Giulia Veronesi
- Humanitas Research Hospital, Division of Thoracic and General Surgery, Rozzano, Italy
| | | | - Sabine Weckbach
- UniversitatsKlinikum Heidelberg, Heidelberg, Germany
- Bayer AG, Research and Development, Pharmaceuticals, Radiology, Berlin, Germany
| | - Torsten G Blum
- HELIOS Klinikum Emil von Behring GmbH, Lungenklinik Heckeshorn, Berlin, Germany
| | - David R Baldwin
- University of Nottingham, Faculty of Medicine and Health Sciences, Nottingham, UK
- Nottingham University Hospitals NHS Trust, Department of Respiratory Medicine, Nottingham, UK
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13
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O'Dowd EL, Tietzova I, Bartlett E, Devaraj A, Biederer J, Brambilla M, Brunelli A, Chorostowska-Wynimko J, Decaluwe H, Deruysscher D, De Wever W, Donoghue M, Fabre A, Gaga M, van Geffen W, Hardavella G, Kauczor HU, Kerpel-Fronius A, van Meerbeeck J, Nagavci B, Nestle U, Novoa N, Prosch H, Prokop M, Putora PM, Rawlinson J, Revel MP, Snoeckx A, Veronesi G, Vliegenthart R, Weckbach S, Blum TG, Baldwin DR. ERS/ESTS/ESTRO/ESR/ESTI/EFOMP statement on management of incidental findings from low dose CT screening for lung cancer. Eur Respir J 2023; 62:2300533. [PMID: 37802631 DOI: 10.1183/13993003.00533-2023] [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: 03/28/2023] [Accepted: 06/06/2023] [Indexed: 10/10/2023]
Abstract
BACKGROUND Screening for lung cancer with low radiation dose computed tomography has a strong evidence base, is being introduced in several European countries and is recommended as a new targeted cancer screening programme. The imperative now is to ensure that implementation follows an evidence-based process that will ensure clinical and cost effectiveness. This European Respiratory Society (ERS) task force was formed to provide an expert consensus for the management of incidental findings which can be adapted and followed during implementation. METHODS A multi-European society collaborative group was convened. 23 topics were identified, primarily from an ERS statement on lung cancer screening, and a systematic review of the literature was conducted according to ERS standards. Initial review of abstracts was completed and full text was provided to members of the group for each topic. Sections were edited and the final document approved by all members and the ERS Science Council. RESULTS Nine topics considered most important and frequent were reviewed as standalone topics (interstitial lung abnormalities, emphysema, bronchiectasis, consolidation, coronary calcification, aortic valve disease, mediastinal mass, mediastinal lymph nodes and thyroid abnormalities). Other topics considered of lower importance or infrequent were grouped into generic categories, suitable for general statements. CONCLUSIONS This European collaborative group has produced an incidental findings statement that can be followed during lung cancer screening. It will ensure that an evidence-based approach is used for reporting and managing incidental findings, which will mean that harms are minimised and any programme is as cost-effective as possible.
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Affiliation(s)
- Emma L O'Dowd
- Nottingham University Hospitals NHS Trust, Nottingham, UK
- University of Nottingham, Faculty of Medicine and Health Sciences, Nottingham, UK
| | - Ilona Tietzova
- Charles University, First Faculty of Medicine, Department of Tuberculosis and Respiratory Diseases, Prague, Czech Republic
| | - Emily Bartlett
- Royal Brompton and Harefield NHS Foundation Trust, Radiology, London, UK
| | - Anand Devaraj
- Royal Brompton and Harefield NHS Foundation Trust, Radiology, London, UK
| | - Jürgen Biederer
- University of Heidelberg, Diagnostic and Interventional Radiology, Heidelberg, Germany
- German Center for Lung Research DZL, Translational Lung Research Center TLRC, Heidelberg, Germany
- University of Latvia, Faculty of Medicine, Riga, Latvia
- Christian-Albrechts-Universität zu Kiel, Faculty of Medicine, Kiel, Germany
| | - Marco Brambilla
- Azienda Ospedaliero-Universitaria Maggiore della Carità di Novara, Novara, Italy
| | | | | | | | - Dirk Deruysscher
- Maastricht University Medical Centre, Department of Radiation Oncology (MAASTRO Clinic), GROW-School for Oncology and Developmental Biology, Limburg, The Netherlands
| | - Walter De Wever
- Universitaire Ziekenhuizen Leuven, Radiology, Leuven, Belgium
| | | | - Aurelie Fabre
- University College Dublin School of Medicine, Histopathology, Dublin, Ireland
| | - Mina Gaga
- Sotiria General Hospital of Chest Diseases of Athens, 7th Respiratory Medicine Department, Athens, Greece
| | - Wouter van Geffen
- Medical Centre Leeuwarden, Department of Respiratory Medicine, Leeuwarden, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Pulmonary Diseases, Groningen, The Netherlands
| | - Georgia Hardavella
- Sotiria General Hospital of Chest Diseases of Athens, Respiratory Medicine, Athens, Greece
| | - Hans-Ulrich Kauczor
- University of Heidelberg, Diagnostic and Interventional Radiology, Heidelberg, Germany
- German Center for Lung Research DZL, Translational Lung Research Center TLRC, Heidelberg, Germany
| | - Anna Kerpel-Fronius
- National Koranyi Institute of Pulmonology, Department of Radiology, Budapest, Hungary
| | | | - Blin Nagavci
- Institute for Evidence in Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg im Breisgau, Germany
| | - Ursula Nestle
- Kliniken Maria Hilf GmbH Monchengladbach, Nordrhein-Westfalen, Germany
| | - Nuria Novoa
- University Hospital of Salamanca, Thoracic Surgery, Salamanca, Spain
| | - Helmut Prosch
- Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Vienna, Austria
| | - Mathias Prokop
- Radboud University Nijmegen Medical Center, Department of Radiology, Nijmegen, The Netherlands
| | - Paul Martin Putora
- Kantonsspital Sankt Gallen, Radiation Oncology, Sankt Gallen, Switzerland
- Inselspital Universitatsspital Bern, Radiation Oncology, Bern, Switzerland
| | | | - Marie-Pierre Revel
- Cochin Hospital, APHP, Radiology Department, Paris, France
- Université de Paris, Paris, France
| | | | - Giulia Veronesi
- Humanitas Research Hospital, Division of Thoracic and General Surgery, Rozzano, Italy
| | | | - Sabine Weckbach
- UniversitatsKlinikum Heidelberg, Heidelberg, Germany
- Bayer AG, Research and Development, Pharmaceuticals, Radiology, Berlin, Germany
| | - Torsten G Blum
- HELIOS Klinikum Emil von Behring GmbH, Lungenklinik Heckeshorn, Berlin, Germany
| | - David R Baldwin
- Nottingham University Hospitals NHS Trust, Nottingham, UK
- University of Nottingham, Faculty of Medicine and Health Sciences, Nottingham, UK
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Maldonado-Franco A, Giraldo-Cadavid LF, Tuta-Quintero E, Bastidas Goyes AR, Botero-Rosas DA. The Challenges of Spirometric Diagnosis of COPD. Can Respir J 2023; 2023:6991493. [PMID: 37808623 PMCID: PMC10558269 DOI: 10.1155/2023/6991493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 03/09/2023] [Accepted: 03/28/2023] [Indexed: 10/10/2023] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is one of the top causes of morbidity and mortality worldwide. Although for many years its accurate diagnosis has been a focus of intense research, it is still challenging. Due to its simplicity, portability, and low cost, spirometry has been established as the main tool to detect this condition, but its flawed performance makes it an imperfect COPD diagnosis gold standard. This review aims to provide an up-to-date literature overview of recent studies regarding COPD diagnosis; we seek to identify their limitations and establish perspectives for spirometric diagnosis of COPD in the XXI century by combining deep clinical knowledge of the disease with advanced computer analysis techniques.
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Affiliation(s)
| | - Luis F. Giraldo-Cadavid
- Departments of Epidemiology and Internal Medicine, School of Medicine, Universidad de La Sabana, Chía, Colombia
- Director of Interventional Pulmonology Service, Fundación Neumológica Colombiana, Bogotá, Colombia
| | - Eduardo Tuta-Quintero
- Candidate for Master's Degree in Epidemiology, Universidad de La Sabana, Chía, Colombia
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15
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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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Amudala Puchakayala PR, Sthanam VL, Nakhmani A, Chaudhary MFA, Kizhakke Puliyakote A, Reinhardt JM, Zhang C, Bhatt SP, Bodduluri S. Radiomics for Improved Detection of Chronic Obstructive Pulmonary Disease in Low-Dose and Standard-Dose Chest CT Scans. Radiology 2023; 307:e222998. [PMID: 37338355 PMCID: PMC10315520 DOI: 10.1148/radiol.222998] [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: 11/28/2022] [Revised: 04/07/2023] [Accepted: 04/24/2023] [Indexed: 06/21/2023]
Abstract
Background Approximately half of adults with chronic obstructive pulmonary disease (COPD) remain undiagnosed. Chest CT scans are frequently acquired in clinical practice and present an opportunity to detect COPD. Purpose To assess the performance of radiomics features in COPD diagnosis using standard-dose and low-dose CT models. Materials and Methods This secondary analysis included participants enrolled in the Genetic Epidemiology of COPD, or COPDGene, study at baseline (visit 1) and 10 years after baseline (visit 3). COPD was defined by a forced expiratory volume in the 1st second of expiration to forced vital capacity ratio less than 0.70 at spirometry. The performance of demographics, CT emphysema percentage, radiomics features, and a combined feature set derived from inspiratory CT alone was evaluated. CatBoost (Yandex), a gradient boosting algorithm, was used to perform two classification experiments to detect COPD; the two models were trained and tested on standard-dose CT data from visit 1 (model I) and low-dose CT data from visit 3 (model II). Classification performance of the models was evaluated using area under the receiver operating characteristic curve (AUC) and precision-recall curve analysis. Results A total of 8878 participants (mean age, 57 years ± 9 [SD]; 4180 female, 4698 male) were evaluated. Radiomics features in model I achieved an AUC of 0.90 (95% CI: 0.88, 0.91) in the standard-dose CT test cohort versus demographics (AUC, 0.73; 95% CI: 0.71, 0.76; P < .001), emphysema percentage (AUC, 0.82; 95% CI 0.80, 0.84; P < .001), and combined features (AUC, 0.90; 95% CI: 0.89, 0.92; P = .16). Model II, trained on low-dose CT scans, achieved an AUC of 0.87 (95% CI: 0.83, 0.91) on the 20% held-out test set for radiomics features compared with demographics (AUC, 0.70; 95% CI: 0.64, 0.75; P = .001), emphysema percentage (AUC, 0.74; 95% CI: 0.69, 0.79; P = .002), and combined features (AUC, 0.88; 95% CI: 0.85, 0.92; P = .32). Density and texture features were the majority of the top 10 features in the standard-dose model, whereas shape features of lungs and airways were significant contributors in the low-dose CT model. Conclusion A combination of features representing parenchymal texture and lung and airway shape on inspiratory CT scans can be used to accurately detect COPD. ClinicalTrials.gov registration no. NCT00608764 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Vliegenthart in this issue.
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Affiliation(s)
- Praneeth Reddy Amudala Puchakayala
- From the UAB Lung Imaging Lab (P.R.A.P., V.L.S., A.N., A.K.P.,
S.P.B., S.B.), Department of Computer Science (P.R.A.P., C.Z.), Department of
Electrical and Computer Engineering (V.L.S., A.N.), and Division of Pulmonary,
Allergy and Critical Care Medicine (A.K.P., S.P.B., S.B.), University of Alabama
at Birmingham, 1720 2nd Ave S, THT 422, Birmingham, AL 35294; and The Roy
J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City,
Iowa (M.F.A.C., J.M.R.)
| | - Venkata L. Sthanam
- From the UAB Lung Imaging Lab (P.R.A.P., V.L.S., A.N., A.K.P.,
S.P.B., S.B.), Department of Computer Science (P.R.A.P., C.Z.), Department of
Electrical and Computer Engineering (V.L.S., A.N.), and Division of Pulmonary,
Allergy and Critical Care Medicine (A.K.P., S.P.B., S.B.), University of Alabama
at Birmingham, 1720 2nd Ave S, THT 422, Birmingham, AL 35294; and The Roy
J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City,
Iowa (M.F.A.C., J.M.R.)
| | - Arie Nakhmani
- From the UAB Lung Imaging Lab (P.R.A.P., V.L.S., A.N., A.K.P.,
S.P.B., S.B.), Department of Computer Science (P.R.A.P., C.Z.), Department of
Electrical and Computer Engineering (V.L.S., A.N.), and Division of Pulmonary,
Allergy and Critical Care Medicine (A.K.P., S.P.B., S.B.), University of Alabama
at Birmingham, 1720 2nd Ave S, THT 422, Birmingham, AL 35294; and The Roy
J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City,
Iowa (M.F.A.C., J.M.R.)
| | - Muhammad F. A. Chaudhary
- From the UAB Lung Imaging Lab (P.R.A.P., V.L.S., A.N., A.K.P.,
S.P.B., S.B.), Department of Computer Science (P.R.A.P., C.Z.), Department of
Electrical and Computer Engineering (V.L.S., A.N.), and Division of Pulmonary,
Allergy and Critical Care Medicine (A.K.P., S.P.B., S.B.), University of Alabama
at Birmingham, 1720 2nd Ave S, THT 422, Birmingham, AL 35294; and The Roy
J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City,
Iowa (M.F.A.C., J.M.R.)
| | - Abhilash Kizhakke Puliyakote
- From the UAB Lung Imaging Lab (P.R.A.P., V.L.S., A.N., A.K.P.,
S.P.B., S.B.), Department of Computer Science (P.R.A.P., C.Z.), Department of
Electrical and Computer Engineering (V.L.S., A.N.), and Division of Pulmonary,
Allergy and Critical Care Medicine (A.K.P., S.P.B., S.B.), University of Alabama
at Birmingham, 1720 2nd Ave S, THT 422, Birmingham, AL 35294; and The Roy
J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City,
Iowa (M.F.A.C., J.M.R.)
| | - Joseph M. Reinhardt
- From the UAB Lung Imaging Lab (P.R.A.P., V.L.S., A.N., A.K.P.,
S.P.B., S.B.), Department of Computer Science (P.R.A.P., C.Z.), Department of
Electrical and Computer Engineering (V.L.S., A.N.), and Division of Pulmonary,
Allergy and Critical Care Medicine (A.K.P., S.P.B., S.B.), University of Alabama
at Birmingham, 1720 2nd Ave S, THT 422, Birmingham, AL 35294; and The Roy
J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City,
Iowa (M.F.A.C., J.M.R.)
| | - Chengcui Zhang
- From the UAB Lung Imaging Lab (P.R.A.P., V.L.S., A.N., A.K.P.,
S.P.B., S.B.), Department of Computer Science (P.R.A.P., C.Z.), Department of
Electrical and Computer Engineering (V.L.S., A.N.), and Division of Pulmonary,
Allergy and Critical Care Medicine (A.K.P., S.P.B., S.B.), University of Alabama
at Birmingham, 1720 2nd Ave S, THT 422, Birmingham, AL 35294; and The Roy
J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City,
Iowa (M.F.A.C., J.M.R.)
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Wu Y, Du R, Feng J, Qi S, Pang H, Xia S, Qian W. Deep CNN for COPD identification by Multi-View snapshot integration of 3D airway tree and lung field. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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18
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Karpiel I, Starcevic A, Urzeniczok M. Database and AI Diagnostic Tools Improve Understanding of Lung Damage, Correlation of Pulmonary Disease and Brain Damage in COVID-19. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22166312. [PMID: 36016071 PMCID: PMC9414394 DOI: 10.3390/s22166312] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/17/2022] [Accepted: 08/18/2022] [Indexed: 05/02/2023]
Abstract
The COVID-19 pandemic caused a sharp increase in the interest in artificial intelligence (AI) as a tool supporting the work of doctors in difficult conditions and providing early detection of the implications of the disease. Recent studies have shown that AI has been successfully applied in the healthcare sector. The objective of this paper is to perform a systematic review to summarize the electroencephalogram (EEG) findings in patients with coronavirus disease (COVID-19) and databases and tools used in artificial intelligence algorithms, supporting the diagnosis and correlation between lung disease and brain damage, and lung damage. Available search tools containing scientific publications, such as PubMed and Google Scholar, were comprehensively evaluated and searched with open databases and tools used in AI algorithms. This work aimed to collect papers from the period of January 2019-May 2022 including in their resources the database from which data necessary for further development of algorithms supporting the diagnosis of the respiratory system can be downloaded and the correlation between lung disease and brain damage can be evaluated. The 10 articles which show the most interesting AI algorithms, trained by using open databases and associated with lung diseases, were included for review with 12 articles related to EEGs, which have/or may be related with lung diseases.
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Affiliation(s)
- Ilona Karpiel
- Łukasiewicz Research Network—Institute of Medical Technology and Equipment, 41-800 Zabrze, Poland
- Correspondence:
| | - Ana Starcevic
- Laboratory for Multimodal Neuroimaging, Institute of Anatomy, Medical Faculty, University of Belgrade, 11000 Belgrade, Serbia
| | - Mirella Urzeniczok
- Łukasiewicz Research Network—Institute of Medical Technology and Equipment, 41-800 Zabrze, Poland
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19
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Li Z, Huang K, Liu L, Zhang Z. Early detection of COPD based on graph convolutional network and small and weakly labeled data. Med Biol Eng Comput 2022; 60:2321-2333. [PMID: 35750976 PMCID: PMC9244127 DOI: 10.1007/s11517-022-02589-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 05/08/2022] [Indexed: 11/25/2022]
Affiliation(s)
- Zongli Li
- Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, People's Republic of China
- Beijing Institute of Respiratory Medicine, Beijing, 100020, People's Republic of China
- Department of Respiratory, Shijingshan Teaching Hospital of Capital Medical University, Beijing Shijingshan Hospital, Beijing, 100043, People's Republic of China
| | - Kewu Huang
- Department of Pulmonary and Critical Care Medicine, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, 100020, People's Republic of China.
- Beijing Institute of Respiratory Medicine, Beijing, 100020, People's Republic of China.
| | - Ligong Liu
- Department of Enterprise Management, China Energy Engineering Corporation Limited, Beijing, 100022, People's Republic of China
| | - Zuoqing Zhang
- Department of Respiratory, Shijingshan Teaching Hospital of Capital Medical University, Beijing Shijingshan Hospital, Beijing, 100043, People's Republic of China
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20
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Jeong MH, Han H, Lagares D, Im H. Recent Advances in Molecular Diagnosis of Pulmonary Fibrosis for Precision Medicine. ACS Pharmacol Transl Sci 2022; 5:520-538. [DOI: 10.1021/acsptsci.2c00028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Indexed: 12/12/2022]
Affiliation(s)
- Mi Ho Jeong
- Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
| | - Hongwei Han
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, United States
| | - David Lagares
- Department of Medicine, Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, United States
| | - Hyungsoon Im
- Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts 02114, United States
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21
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Effect Analysis of Lung Rehabilitation Training in 5A Nursing Mode for Elderly Patients with COPD Based on X-Ray. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1963426. [PMID: 35734776 PMCID: PMC9208961 DOI: 10.1155/2022/1963426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Revised: 05/20/2022] [Accepted: 05/23/2022] [Indexed: 11/30/2022]
Abstract
This study was aimed at evaluating the application effect of pulmonary rehabilitation training under 5A nursing mode based on X-ray in elderly patients with chronic obstructive pulmonary disease (COPD). Then, 84 elderly patients with chronic obstructive emphysema were selected as the research subjects. COPD knowledge level questionnaire, caregiver self-efficacy scale (CSES), COPD assessment test (CAT), and 6-minute walking experiment (6MWD) were adopted, and the clinical application effect of pulmonary rehabilitation training and conventional nursing under 5A nursing mode was comprehensively compared. The results show that after two and four months of intervention, the average score of COPD knowledge level questionnaire in the test group was 27.43 points and 30.08 points, respectively, higher than that in the control group (P < 0.05). After two and four months of intervention, the number of patients with good compliance in the test group was remarkably improved, and the severity of airflow restriction in the test group was slower than that in the control group. In short, pulmonary rehabilitation training under 5A nursing mode based on X-ray can effectively improve the disease knowledge level, self-efficacy, and pulmonary rehabilitation training compliance of elderly COPD patients, which played an important role in improving the quality of life of patients and alleviating the degree of dyspnea of patients.
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22
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Nagaraj Y, Wisselink HJ, Rook M, Cai J, Nagaraj SB, Sidorenkov G, Veldhuis R, Oudkerk M, Vliegenthart R, van Ooijen P. AI-Driven Model for Automatic Emphysema Detection in Low-Dose Computed Tomography Using Disease-Specific Augmentation. J Digit Imaging 2022; 35:538-550. [PMID: 35182291 PMCID: PMC9156637 DOI: 10.1007/s10278-022-00599-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 01/03/2022] [Accepted: 01/29/2022] [Indexed: 12/15/2022] Open
Abstract
The objective of this study is to evaluate the feasibility of a disease-specific deep learning (DL) model based on minimum intensity projection (minIP) for automated emphysema detection in low-dose computed tomography (LDCT) scans. LDCT scans of 240 individuals from a population-based cohort in the Netherlands (ImaLife study, mean age ± SD = 57 ± 6 years) were retrospectively chosen for training and internal validation of the DL model. For independent testing, LDCT scans of 125 individuals from a lung cancer screening cohort in the USA (NLST study, mean age ± SD = 64 ± 5 years) were used. Dichotomous emphysema diagnosis based on radiologists' annotation was used to develop the model. The automated model included minIP processing (slab thickness range: 1 mm to 11 mm), classification, and detection maps generation. The data-split for the pipeline evaluation involved class-balanced and imbalanced settings. The proposed DL pipeline showed the highest performance (area under receiver operating characteristics curve) for 11 mm slab thickness in both the balanced (ImaLife = 0.90 ± 0.05) and the imbalanced dataset (NLST = 0.77 ± 0.06). For ImaLife subcohort, the variation in minIP slab thickness from 1 to 11 mm increased the DL model's sensitivity from 75 to 88% and decreased the number of false-negative predictions from 10 to 5. The minIP-based DL model can automatically detect emphysema in LDCTs. The performance of thicker minIP slabs was better than that of thinner slabs. LDCT can be leveraged for emphysema detection by applying disease specific augmentation.
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Affiliation(s)
- Yeshaswini Nagaraj
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands ,DASH, Machine Learning Lab, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Hendrik Joost Wisselink
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Mieneke Rook
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands ,Department of Radiology, Martini Hospital Groningen, Groningen, The Netherlands
| | - Jiali Cai
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Sunil Belur Nagaraj
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Grigory Sidorenkov
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Raymond Veldhuis
- Faculty of Electrical Engineering, Mathematics Computer Science (EWI), Data Management Biometrics (DMB), University of Twente, Enschede, The Netherlands
| | - Matthijs Oudkerk
- Faculty of Medical Sciences, University of Groningen, Groningen, The Netherlands ,Institute for DiagNostic Accuracy Research B.V., Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Peter van Ooijen
- Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands ,DASH, Machine Learning Lab, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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23
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Yu AC, Mohajer B, Eng J. External Validation of Deep Learning Algorithms for Radiologic Diagnosis: A Systematic Review. Radiol Artif Intell 2022; 4:e210064. [PMID: 35652114 DOI: 10.1148/ryai.210064] [Citation(s) in RCA: 83] [Impact Index Per Article: 41.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 03/09/2022] [Accepted: 04/12/2022] [Indexed: 01/17/2023]
Abstract
Purpose To assess generalizability of published deep learning (DL) algorithms for radiologic diagnosis. Materials and Methods In this systematic review, the PubMed database was searched for peer-reviewed studies of DL algorithms for image-based radiologic diagnosis that included external validation, published from January 1, 2015, through April 1, 2021. Studies using nonimaging features or incorporating non-DL methods for feature extraction or classification were excluded. Two reviewers independently evaluated studies for inclusion, and any discrepancies were resolved by consensus. Internal and external performance measures and pertinent study characteristics were extracted, and relationships among these data were examined using nonparametric statistics. Results Eighty-three studies reporting 86 algorithms were included. The vast majority (70 of 86, 81%) reported at least some decrease in external performance compared with internal performance, with nearly half (42 of 86, 49%) reporting at least a modest decrease (≥0.05 on the unit scale) and nearly a quarter (21 of 86, 24%) reporting a substantial decrease (≥0.10 on the unit scale). No study characteristics were found to be associated with the difference between internal and external performance. Conclusion Among published external validation studies of DL algorithms for image-based radiologic diagnosis, the vast majority demonstrated diminished algorithm performance on the external dataset, with some reporting a substantial performance decrease.Keywords: Meta-Analysis, Computer Applications-Detection/Diagnosis, Neural Networks, Computer Applications-General (Informatics), Epidemiology, Technology Assessment, Diagnosis, Informatics Supplemental material is available for this article. © RSNA, 2022.
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Affiliation(s)
- Alice C Yu
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
| | - Bahram Mohajer
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
| | - John Eng
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 1800 Orleans St, Baltimore, MD 21287
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24
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Abstract
Artificial intelligence (AI) is transforming the way we perform advanced imaging. From high-resolution image reconstruction to predicting functional response from clinically acquired data, AI is promising to revolutionize clinical evaluation of lung performance, pushing the boundary in pulmonary functional imaging for patients suffering from respiratory conditions. In this review, we overview the current developments and expound on some of the encouraging new frontiers. We focus on the recent advances in machine learning and deep learning that enable reconstructing images, quantitating, and predicting functional responses of the lung. Finally, we shed light on the potential opportunities and challenges ahead in adopting AI for functional lung imaging in clinical settings.
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Affiliation(s)
- Raúl San José Estépar
- Applied Chest Imaging Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, United States
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25
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Chetan MR, Dowson N, Price NW, Ather S, Nicolson A, Gleeson FV. Developing an understanding of artificial intelligence lung nodule risk prediction using insights from the Brock model. Eur Radiol 2022; 32:5330-5338. [PMID: 35238972 PMCID: PMC9279235 DOI: 10.1007/s00330-022-08635-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Revised: 01/17/2022] [Accepted: 02/02/2022] [Indexed: 12/17/2022]
Abstract
Objectives To determine if predictions of the Lung Cancer Prediction convolutional neural network (LCP-CNN) artificial intelligence (AI) model are analogous to the Brock model. Methods In total, 10,485 lung nodules in 4660 participants from the National Lung Screening Trial (NLST) were analysed. Both manual and automated nodule measurements were inputted into the Brock model, and this was compared to LCP-CNN. The performance of an experimental AI model was tested after ablating imaging features in a manner analogous to removing predictors from the Brock model. First, the nodule was ablated leaving lung parenchyma only. Second, a sphere of the same size as the nodule was implanted in the parenchyma. Third, internal texture of both nodule and parenchyma was ablated. Results Automated axial diameter (AUC 0.883) and automated equivalent spherical diameter (AUC 0.896) significantly improved the accuracy of Brock when compared to manual measurement (AUC 0.873), although not to the level of the LCP-CNN (AUC 0.936). Ablating nodule and parenchyma texture (AUC 0.915) led to a small drop in AI predictive accuracy, as did implanting a sphere of the same size as the nodule (AUC 0.889). Ablating the nodule leaving parenchyma only led to a large drop in AI performance (AUC 0.717). Conclusions Feature ablation is a feasible technique for understanding AI model predictions. Nodule size and morphology play the largest role in AI prediction, with nodule internal texture and background parenchyma playing a limited role. This is broadly analogous to the relative importance of morphological factors over clinical factors within the Brock model. Key Points • Brock lung cancer risk prediction accuracy was significantly improved using automated axial or equivalent spherical measurements of lung nodule diameter, when compared to manual measurements. • Predictive accuracy was further improved by using the Lung Cancer Prediction convolutional neural network, an artificial intelligence-based model which obviates the requirement for nodule measurement. • Nodule size and morphology are important factors in artificial intelligence lung cancer risk prediction, with nodule texture and background parenchyma contributing a small, but measurable, role. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-022-08635-4.
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Affiliation(s)
- Madhurima R Chetan
- Department of Radiology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, X-ray Level 2, Headley Way, Headington, Oxford, OX3 9DU, UK. .,Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, UK.
| | - Nicholas Dowson
- Optellum Ltd, Oxford Centre for Innovation, Oxford, OX1 1BY, UK
| | | | - Sarim Ather
- Department of Radiology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, X-ray Level 2, Headley Way, Headington, Oxford, OX3 9DU, UK
| | - Angus Nicolson
- Optellum Ltd, Oxford Centre for Innovation, Oxford, OX1 1BY, UK
| | - Fergus V Gleeson
- Department of Radiology, John Radcliffe Hospital, Oxford University Hospitals NHS Foundation Trust, X-ray Level 2, Headley Way, Headington, Oxford, OX3 9DU, UK.,Department of Oncology, University of Oxford, Old Road Campus Research Building, Roosevelt Drive, Oxford, OX3 7DQ, UK
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26
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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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27
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Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach. Eur Radiol 2022; 32:5319-5329. [PMID: 35201409 DOI: 10.1007/s00330-022-08632-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 01/25/2022] [Accepted: 02/07/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES Chronic obstructive pulmonary disease (COPD) is underdiagnosed globally. The present study aimed to develop weakly supervised deep learning (DL) models that utilize computed tomography (CT) image data for the automated detection and staging of spirometry-defined COPD. METHODS A large, highly heterogeneous dataset was established, consisting of 1393 participants retrospectively recruited from outpatient, inpatient, and physical examination center settings of four large public hospitals in China. All participants underwent both inspiratory chest CT scans and pulmonary function tests. CT images, spirometry data, demographic information, and clinical information of each participant were collected. An attention-based multi-instance learning (MIL) model for COPD detection was trained using CT scans from 837 participants. External validation of the COPD detection was performed with 620 low-dose CT (LDCT) scans acquired from the National Lung Screening Trial (NLST) cohort. A multi-channel 3D residual network was further developed to categorize GOLD stages among confirmed COPD patients. RESULTS The attention-based MIL model used for COPD detection achieved an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI: 0.903, 0.961) on the internal test set and 0.866 (95% CI: 0.805, 0.928) on the LDCT subset acquired from the NLST. The multi-channel 3D residual network was able to correctly grade 76.4% of COPD patients in the test set (423/553) using the GOLD scale. CONCLUSIONS The proposed chest CT-DL approach can automatically identify spirometry-defined COPD and categorize patients according to the GOLD scale. As such, this approach may be an effective case-finding tool for COPD diagnosis and staging. KEY POINTS • Chronic obstructive pulmonary disease is underdiagnosed globally, particularly in developing countries. • The proposed chest computed tomography (CT)-based deep learning (DL) approaches could accurately identify spirometry-defined COPD and categorize patients according to the GOLD scale. • The chest CT-DL approach may be an alternative case-finding tool for COPD identification and evaluation.
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Kumar Y, Koul A, Singla R, Ijaz MF. Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:8459-8486. [PMID: 35039756 PMCID: PMC8754556 DOI: 10.1007/s12652-021-03612-z] [Citation(s) in RCA: 117] [Impact Index Per Article: 58.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 11/18/2021] [Indexed: 05/03/2023]
Abstract
Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.
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Affiliation(s)
- Yogesh Kumar
- Department of Computer Engineering, Indus Institute of Technology and Engineering, Indus University, Ahmedabad, 382115 India
| | | | - Ruchi Singla
- Department of Research, Innovations, Sponsored Projects and Entrepreneurship, CGC Landran, Mohali, India
| | - Muhammad Fazal Ijaz
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006 South Korea
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Vannier MW. Isophotes, Scale Space, and Invariants in Lung CT for COPD Diagnosis. Radiol Artif Intell 2022; 4:e210301. [PMID: 35146438 PMCID: PMC8823455 DOI: 10.1148/ryai.210301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 12/15/2021] [Accepted: 12/20/2021] [Indexed: 06/14/2023]
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Savadjiev P, Gallix B, Rezanejad M, Bhatnagar S, Semionov A, Siddiqi K, Forghani R, Reinhold C, Eidelman DH, Dandurand RJ. Improved Detection of Chronic Obstructive Pulmonary Disease at Chest CT Using the Mean Curvature of Isophotes. Radiol Artif Intell 2022; 4:e210105. [PMID: 35146436 DOI: 10.1148/ryai.210105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 11/17/2021] [Accepted: 11/24/2021] [Indexed: 01/01/2023]
Abstract
PURPOSE To determine if the mean curvature of isophotes (MCI), a standard computer vision technique, can be used to improve detection of chronic obstructive pulmonary disease (COPD) at chest CT. MATERIALS AND METHODS In this retrospective study, chest CT scans were obtained in 243 patients with COPD and 31 controls (among all 274: 151 women [mean age, 70 years; range, 44-90 years] and 123 men [mean age, 71 years; range, 29-90 years]) from two community practices between 2006 and 2019. A convolutional neural network (CNN) architecture was trained on either CT images or CT images transformed through the MCI algorithm. Separately, a linear classification based on a single feature derived from the MCI computation (called hMCI1) was also evaluated. All three models were evaluated with cross-validation, using precision-macro and recall-macro metrics, that is, the mean of per-class precision and recall values, respectively (the latter being equivalent to balanced accuracy). RESULTS Linear classification based on hMCI1 resulted in a higher recall-macro relative to the CNN trained and applied on CT images (0.85 [95% CI: 0.84, 0.86] vs 0.77 [95% CI: 0.75, 0.79]) but with a similar reduction in precision-macro (0.66 [95% CI: 0.65, 0.67] vs 0.77 [95% CI: 0.75, 0.79]). The CNN model trained and applied on MCI-transformed images had a higher recall-macro (0.85 [95% CI: 0.83, 0.87] vs 0.77 [95% CI: 0.75, 0.79]) and precision-macro (0.85 [95% CI: 0.83, 0.87] vs 0.77 [95% CI: 0.75, 0.79]) relative to the CNN trained and applied on CT images. CONCLUSION The MCI algorithm may be valuable toward the automated detection and diagnosis of COPD on chest CT scans as part of a CNN-based pipeline or with stand-alone features.Keywords: Chronic Obstructive Pulmonary Disease, Quantification, Lung, CT Supplemental material is available for this article. See also the invited commentary by Vannier in this issue.© RSNA, 2021.
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Affiliation(s)
- Peter Savadjiev
- Department of Diagnostic Radiology (P.S., S.B., A.S., R.F., C.R.), Research Institute (R.F., C.R., D.H.E., R.J.D.), Meakins-Christie Laboratories, Research Institute (D.H.E., R.J.D.), Centre for Innovative Medicine, Research Institute (R.J.D.), and Montreal Chest Institute (R.J.D.), McGill University Health Centre, 1001 Décarie Blvd, Montréal, QC, Canada H4A 3J1; Department of Pathology (P.S.), Medical Physics Unit, Department of Oncology (P.S.), School of Computer Science (P.S., K.S.), Department of Epidemiology, Biostatistics and Occupational Health (S.B.), Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital (R.F.), and Department of Medicine (D.H.E., R.J.D.), McGill University, Montreal, Quebec, Canada; Institut de Chirurgie Guidée par l'Image, IHU Strasbourg, Strasbourg, France (B.G.); Bernhardt-Walther Laboratory, Department of Psychology, University of Toronto, Toronto, Ontario, Canada (M.R.); and Lakeshore General Hospital, Pointe-Claire, Quebec, Canada (R.J.D.)
| | - Benoit Gallix
- Department of Diagnostic Radiology (P.S., S.B., A.S., R.F., C.R.), Research Institute (R.F., C.R., D.H.E., R.J.D.), Meakins-Christie Laboratories, Research Institute (D.H.E., R.J.D.), Centre for Innovative Medicine, Research Institute (R.J.D.), and Montreal Chest Institute (R.J.D.), McGill University Health Centre, 1001 Décarie Blvd, Montréal, QC, Canada H4A 3J1; Department of Pathology (P.S.), Medical Physics Unit, Department of Oncology (P.S.), School of Computer Science (P.S., K.S.), Department of Epidemiology, Biostatistics and Occupational Health (S.B.), Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital (R.F.), and Department of Medicine (D.H.E., R.J.D.), McGill University, Montreal, Quebec, Canada; Institut de Chirurgie Guidée par l'Image, IHU Strasbourg, Strasbourg, France (B.G.); Bernhardt-Walther Laboratory, Department of Psychology, University of Toronto, Toronto, Ontario, Canada (M.R.); and Lakeshore General Hospital, Pointe-Claire, Quebec, Canada (R.J.D.)
| | - Morteza Rezanejad
- Department of Diagnostic Radiology (P.S., S.B., A.S., R.F., C.R.), Research Institute (R.F., C.R., D.H.E., R.J.D.), Meakins-Christie Laboratories, Research Institute (D.H.E., R.J.D.), Centre for Innovative Medicine, Research Institute (R.J.D.), and Montreal Chest Institute (R.J.D.), McGill University Health Centre, 1001 Décarie Blvd, Montréal, QC, Canada H4A 3J1; Department of Pathology (P.S.), Medical Physics Unit, Department of Oncology (P.S.), School of Computer Science (P.S., K.S.), Department of Epidemiology, Biostatistics and Occupational Health (S.B.), Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital (R.F.), and Department of Medicine (D.H.E., R.J.D.), McGill University, Montreal, Quebec, Canada; Institut de Chirurgie Guidée par l'Image, IHU Strasbourg, Strasbourg, France (B.G.); Bernhardt-Walther Laboratory, Department of Psychology, University of Toronto, Toronto, Ontario, Canada (M.R.); and Lakeshore General Hospital, Pointe-Claire, Quebec, Canada (R.J.D.)
| | - Sahir Bhatnagar
- Department of Diagnostic Radiology (P.S., S.B., A.S., R.F., C.R.), Research Institute (R.F., C.R., D.H.E., R.J.D.), Meakins-Christie Laboratories, Research Institute (D.H.E., R.J.D.), Centre for Innovative Medicine, Research Institute (R.J.D.), and Montreal Chest Institute (R.J.D.), McGill University Health Centre, 1001 Décarie Blvd, Montréal, QC, Canada H4A 3J1; Department of Pathology (P.S.), Medical Physics Unit, Department of Oncology (P.S.), School of Computer Science (P.S., K.S.), Department of Epidemiology, Biostatistics and Occupational Health (S.B.), Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital (R.F.), and Department of Medicine (D.H.E., R.J.D.), McGill University, Montreal, Quebec, Canada; Institut de Chirurgie Guidée par l'Image, IHU Strasbourg, Strasbourg, France (B.G.); Bernhardt-Walther Laboratory, Department of Psychology, University of Toronto, Toronto, Ontario, Canada (M.R.); and Lakeshore General Hospital, Pointe-Claire, Quebec, Canada (R.J.D.)
| | - Alexandre Semionov
- Department of Diagnostic Radiology (P.S., S.B., A.S., R.F., C.R.), Research Institute (R.F., C.R., D.H.E., R.J.D.), Meakins-Christie Laboratories, Research Institute (D.H.E., R.J.D.), Centre for Innovative Medicine, Research Institute (R.J.D.), and Montreal Chest Institute (R.J.D.), McGill University Health Centre, 1001 Décarie Blvd, Montréal, QC, Canada H4A 3J1; Department of Pathology (P.S.), Medical Physics Unit, Department of Oncology (P.S.), School of Computer Science (P.S., K.S.), Department of Epidemiology, Biostatistics and Occupational Health (S.B.), Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital (R.F.), and Department of Medicine (D.H.E., R.J.D.), McGill University, Montreal, Quebec, Canada; Institut de Chirurgie Guidée par l'Image, IHU Strasbourg, Strasbourg, France (B.G.); Bernhardt-Walther Laboratory, Department of Psychology, University of Toronto, Toronto, Ontario, Canada (M.R.); and Lakeshore General Hospital, Pointe-Claire, Quebec, Canada (R.J.D.)
| | - Kaleem Siddiqi
- Department of Diagnostic Radiology (P.S., S.B., A.S., R.F., C.R.), Research Institute (R.F., C.R., D.H.E., R.J.D.), Meakins-Christie Laboratories, Research Institute (D.H.E., R.J.D.), Centre for Innovative Medicine, Research Institute (R.J.D.), and Montreal Chest Institute (R.J.D.), McGill University Health Centre, 1001 Décarie Blvd, Montréal, QC, Canada H4A 3J1; Department of Pathology (P.S.), Medical Physics Unit, Department of Oncology (P.S.), School of Computer Science (P.S., K.S.), Department of Epidemiology, Biostatistics and Occupational Health (S.B.), Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital (R.F.), and Department of Medicine (D.H.E., R.J.D.), McGill University, Montreal, Quebec, Canada; Institut de Chirurgie Guidée par l'Image, IHU Strasbourg, Strasbourg, France (B.G.); Bernhardt-Walther Laboratory, Department of Psychology, University of Toronto, Toronto, Ontario, Canada (M.R.); and Lakeshore General Hospital, Pointe-Claire, Quebec, Canada (R.J.D.)
| | - Reza Forghani
- Department of Diagnostic Radiology (P.S., S.B., A.S., R.F., C.R.), Research Institute (R.F., C.R., D.H.E., R.J.D.), Meakins-Christie Laboratories, Research Institute (D.H.E., R.J.D.), Centre for Innovative Medicine, Research Institute (R.J.D.), and Montreal Chest Institute (R.J.D.), McGill University Health Centre, 1001 Décarie Blvd, Montréal, QC, Canada H4A 3J1; Department of Pathology (P.S.), Medical Physics Unit, Department of Oncology (P.S.), School of Computer Science (P.S., K.S.), Department of Epidemiology, Biostatistics and Occupational Health (S.B.), Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital (R.F.), and Department of Medicine (D.H.E., R.J.D.), McGill University, Montreal, Quebec, Canada; Institut de Chirurgie Guidée par l'Image, IHU Strasbourg, Strasbourg, France (B.G.); Bernhardt-Walther Laboratory, Department of Psychology, University of Toronto, Toronto, Ontario, Canada (M.R.); and Lakeshore General Hospital, Pointe-Claire, Quebec, Canada (R.J.D.)
| | - Caroline Reinhold
- Department of Diagnostic Radiology (P.S., S.B., A.S., R.F., C.R.), Research Institute (R.F., C.R., D.H.E., R.J.D.), Meakins-Christie Laboratories, Research Institute (D.H.E., R.J.D.), Centre for Innovative Medicine, Research Institute (R.J.D.), and Montreal Chest Institute (R.J.D.), McGill University Health Centre, 1001 Décarie Blvd, Montréal, QC, Canada H4A 3J1; Department of Pathology (P.S.), Medical Physics Unit, Department of Oncology (P.S.), School of Computer Science (P.S., K.S.), Department of Epidemiology, Biostatistics and Occupational Health (S.B.), Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital (R.F.), and Department of Medicine (D.H.E., R.J.D.), McGill University, Montreal, Quebec, Canada; Institut de Chirurgie Guidée par l'Image, IHU Strasbourg, Strasbourg, France (B.G.); Bernhardt-Walther Laboratory, Department of Psychology, University of Toronto, Toronto, Ontario, Canada (M.R.); and Lakeshore General Hospital, Pointe-Claire, Quebec, Canada (R.J.D.)
| | - David H Eidelman
- Department of Diagnostic Radiology (P.S., S.B., A.S., R.F., C.R.), Research Institute (R.F., C.R., D.H.E., R.J.D.), Meakins-Christie Laboratories, Research Institute (D.H.E., R.J.D.), Centre for Innovative Medicine, Research Institute (R.J.D.), and Montreal Chest Institute (R.J.D.), McGill University Health Centre, 1001 Décarie Blvd, Montréal, QC, Canada H4A 3J1; Department of Pathology (P.S.), Medical Physics Unit, Department of Oncology (P.S.), School of Computer Science (P.S., K.S.), Department of Epidemiology, Biostatistics and Occupational Health (S.B.), Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital (R.F.), and Department of Medicine (D.H.E., R.J.D.), McGill University, Montreal, Quebec, Canada; Institut de Chirurgie Guidée par l'Image, IHU Strasbourg, Strasbourg, France (B.G.); Bernhardt-Walther Laboratory, Department of Psychology, University of Toronto, Toronto, Ontario, Canada (M.R.); and Lakeshore General Hospital, Pointe-Claire, Quebec, Canada (R.J.D.)
| | - Ronald J Dandurand
- Department of Diagnostic Radiology (P.S., S.B., A.S., R.F., C.R.), Research Institute (R.F., C.R., D.H.E., R.J.D.), Meakins-Christie Laboratories, Research Institute (D.H.E., R.J.D.), Centre for Innovative Medicine, Research Institute (R.J.D.), and Montreal Chest Institute (R.J.D.), McGill University Health Centre, 1001 Décarie Blvd, Montréal, QC, Canada H4A 3J1; Department of Pathology (P.S.), Medical Physics Unit, Department of Oncology (P.S.), School of Computer Science (P.S., K.S.), Department of Epidemiology, Biostatistics and Occupational Health (S.B.), Segal Cancer Centre and Lady Davis Institute for Medical Research, Jewish General Hospital (R.F.), and Department of Medicine (D.H.E., R.J.D.), McGill University, Montreal, Quebec, Canada; Institut de Chirurgie Guidée par l'Image, IHU Strasbourg, Strasbourg, France (B.G.); Bernhardt-Walther Laboratory, Department of Psychology, University of Toronto, Toronto, Ontario, Canada (M.R.); and Lakeshore General Hospital, Pointe-Claire, Quebec, Canada (R.J.D.)
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Watson A, Wilkinson TMA. Digital healthcare in COPD management: a narrative review on the advantages, pitfalls, and need for further research. Ther Adv Respir Dis 2022; 16:17534666221075493. [PMID: 35234090 PMCID: PMC8894614 DOI: 10.1177/17534666221075493] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) remains a leading cause of morbidity and mortality despite current treatment strategies which focus on smoking cessation, pulmonary rehabilitation, and symptomatic relief. A focus of COPD care is to encourage self-management, particularly during COVID-19, where much face-to-face care has been reduced or ceased. Digital health solutions may offer affordable and scalable solutions to support COPD patient education and self-management, such solutions could improve clinical outcomes and expand service reach for limited additional cost. However, optimal ways to deliver digital medicine are still in development, and there are a number of important considerations for clinicians, commissioners, and patients to ensure successful implementation of digitally augmented care. In this narrative review, we discuss advantages, pitfalls, and future prospects of digital healthcare, which offer a variety of tools including self-management plans, education videos, inhaler training videos, feedback to patients and healthcare professionals (HCPs), exacerbation monitoring, and pulmonary rehabilitation. We discuss the key issues with sustaining patient and HCP engagement and limiting attrition of use, interoperability with devices, integration into healthcare systems, and ensuring inclusivity and accessibility. We explore the essential areas of research beyond determining safety and efficacy to understand the acceptability of digital healthcare solutions to patients, clinicians, and healthcare systems, and hence ways to improve this and sustain engagement. Finally, we explore the regulatory challenges to ensure quality and engagement and effective integration into current healthcare systems and care pathways, while maintaining patients’ autonomy and privacy. Understanding and addressing these issues and successful incorporation of an acceptable, simple, scalable, affordable, and future-proof digital solution into healthcare systems could help remodel global chronic disease management and fractured healthcare systems to provide best patient care and optimisation of healthcare resources to meet the global burden and unmet clinical need of COPD.
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Affiliation(s)
- Alastair Watson
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, UKNIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UKCollege of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - Tom M A Wilkinson
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton SO16 6YD, UK. NIHR Southampton Biomedical Research Centre, University Hospital Southampton, Southampton, UK
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Exarchos K, Aggelopoulou A, Oikonomou A, Biniskou T, Beli V, Antoniadou E, Kostikas K. Review of Artificial Intelligence techniques in Chronic Obstructive Lung Disease. IEEE J Biomed Health Inform 2021; 26:2331-2338. [PMID: 34914601 DOI: 10.1109/jbhi.2021.3135838] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
BACKGROUND Artificial Intelligence (AI) has proven to be an invaluable asset in the healthcare domain, where massive amounts of data are produced. Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous chronic condition with multiscale manifestations and complex interactions that represents an ideal target for AI. OBJECTIVE The aim of this review article is to appraise the adoption of AI in COPD research, and more specifically its applications to date along with reported results, potential challenges and future prospects. METHODS We performed a review of the literature from PubMed and DBLP and assembled studies published up to 2020, yielding 156 articles relevant to the scope of this review. RESULTS The resulting articles were assessed and organized into four basic contextual categories, namely: i) COPD diagnosis, ii) COPD prognosis, iii) Patient classification, iv) COPD management, and subsequently presented in an orderly manner based on a set of qualitative and quantitative criteria. CONCLUSIONS We observed considerable acceleration of research activity utilizing AI techniques in COPD research, especially in the last couple of years, nevertheless, the massive production of large and complex data in COPD calls for broader adoption of AI and more advanced techniques.
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Ebrahimian S, Digumarthy SR, Bizzo B, Primak A, Zimmermann M, Tarbiah MM, Kalra MK, Dreyer KJ. Artificial Intelligence has Similar Performance to Subjective Assessment of Emphysema Severity on Chest CT. Acad Radiol 2021; 29:1189-1195. [PMID: 34657812 DOI: 10.1016/j.acra.2021.09.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/03/2021] [Accepted: 09/06/2021] [Indexed: 12/12/2022]
Abstract
RATIONALE AND OBJECTIVES To compare an artificial intelligence (AI)-based prototype and subjective grading for predicting disease severity in patients with emphysema. METHODS Our IRB approved HIPAA-compliant study included 113 adults (71±8 years; 47 females, 66 males) who had both non-contrast chest CT and pulmonary function tests performed within a span of 2 months. The disease severity was classified based on the forced expiratory volume in 1 second (FEV1 as % of predicted) into mild, moderate, and severe. 2 thoracic radiologists (RA), blinded to the clinical and AI results, graded severity of emphysema on a 5-point scale suggested by the Fleischner Society for each lobe. The whole lung scores were derived from the summation of lobar scores. Thin-section CT images were processed with the AI-Rad Companion Chest prototype (Siemens Healthineers) to quantify low attenuation areas (LAA < - 950 HU) in whole lung and each lobe separately. Bronchial abnormality was assessed by both radiologists and a fully automated software (Philips Healthcare). RESULTS Both AI (AUC of 0.77; 95% CI: 0.68 - 0.85) and RA (AUC: 0.76, 95% CI: 0.65 - 0.84) emphysema quantification could differentiate mild, moderate, and severe disease based on FEV1. There was a strong positive correlation between AI and RA (r = 0.72 - 0.80; p <0.001). The combination of emphysema and bronchial abnormality quantification from radiologists' and AI assessment could differentiate between different severities with AUC of 0.80 - 0.82 and 0.87, respectively. CONCLUSION The assessed AI-prototypes can predict the disease severity in patients with emphysema with the same predictive value as the radiologists.
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Mondal S, Sadhu AK, Dutta PK. Automated diagnosis of pulmonary emphysema using multi-objective binary thresholding and hybrid classification. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Exarchos KP, Kostikas K. Artificial intelligence in COPD: Possible applications and future prospects. Respirology 2021; 26:641-642. [PMID: 33851496 DOI: 10.1111/resp.14061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Accepted: 03/29/2021] [Indexed: 11/27/2022]
Affiliation(s)
- Konstantinos P Exarchos
- Respiratory Medicine Department, University of Ioannina School of Medicine, Ioannina, Greece
| | - Konstantinos Kostikas
- Respiratory Medicine Department, University of Ioannina School of Medicine, Ioannina, Greece
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The Importance of Appropriate Diagnosis in the Practical Management of Chronic Obstructive Pulmonary Disease. Diagnostics (Basel) 2021; 11:diagnostics11040618. [PMID: 33808229 PMCID: PMC8067197 DOI: 10.3390/diagnostics11040618] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 03/25/2021] [Accepted: 03/28/2021] [Indexed: 12/25/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is projected to continue to contribute to an increase in the overall worldwide burden of disease until 2030. Therefore, an accurate assessment of the risk of airway obstruction in patients with COPD has become vitally important. Although the Global Initiative for Chronic Obstructive Lung Disease (GOLD), the American Thoracic Society (ATS) and European Respiratory Society (ERS), and the Japanese Respiratory Society (JRS) provide the criteria by which to diagnose COPD, many studies suggest that it is in fact underdiagnosed. Its prevalence increases, while the impact of COPD-related systemic comorbidities is also increasingly recognized in clinical aspects of COPD. Although a recent report suggests that spirometry should not be used to screen for airflow limitation in individuals without respiratory symptoms, the early detection of COPD in patients with no, or few, symptoms is an opportunity to provide appropriate management based on COPD guidelines. Clinical advances have been made in pharmacotherapeutic approaches to COPD. This article provides a current understanding of the importance of an appropriate diagnosis in the real-world management of COPD.
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Lam S, Bryant H, Donahoe L, Domingo A, Earle C, Finley C, Gonzalez AV, Hergott C, Hung RJ, Ireland AM, Lovas M, Manos D, Mayo J, Maziak DE, McInnis M, Myers R, Nicholson E, Politis C, Schmidt H, Sekhon HS, Soprovich M, Stewart A, Tammemagi M, Taylor JL, Tsao MS, Warkentin MT, Yasufuku K. Management of screen-detected lung nodules: A Canadian partnership against cancer guidance document. CANADIAN JOURNAL OF RESPIRATORY CRITICAL CARE AND SLEEP MEDICINE 2020. [DOI: 10.1080/24745332.2020.1819175] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Affiliation(s)
- Stephen Lam
- British Columbia Cancer Agency & the University of British Columbia, Vancouver, British Columbia, Canada
| | - Heather Bryant
- Screening and Early Detection, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Laura Donahoe
- Division of Thoracic Surgery, Department of Surgery, University Health Network, Toronto, Ontario, Canada
| | - Ashleigh Domingo
- Screening and Early Detection, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Craig Earle
- Screening and Early Detection, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Christian Finley
- Department of Thoracic Surgery, St. Joseph's Healthcare, McMaster University, Hamilton, Ontario, Canada
| | - Anne V. Gonzalez
- Division of Respiratory Medicine, McGill University, Montreal, Quebec, Canada
| | - Christopher Hergott
- Division of Respiratory Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Rayjean J. Hung
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Ontario, Canada
| | - Anne Marie Ireland
- Patient and Family Advocate, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Michael Lovas
- Patient and Family Advocate, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Daria Manos
- Department of Diagnostic Radiology, Dalhousie University, Halifax, Nova Scotia, Canada
| | - John Mayo
- Department of Radiology, Vancouver Coastal Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Donna E. Maziak
- Surgical Oncology Division of Thoracic Surgery, Ottawa Hospital, Ottawa, Ontario, Canada
| | - Micheal McInnis
- Joint Department of Medical Imaging, University Health Network, Toronto, Ontario, Canada
| | - Renelle Myers
- British Columbia Cancer Agency & the University of British Columbia, Vancouver, British Columbia, Canada
| | - Erika Nicholson
- Screening and Early Detection, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Christopher Politis
- Screening and Early Detection, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Heidi Schmidt
- University Health Network and Princess Margaret Cancer Centre, Toronto, Ontario, Canada
| | - Harman S. Sekhon
- Department of Pathology and Laboratory Medicine, University of Ottawa, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Marie Soprovich
- Patient and Family Advocate, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Archie Stewart
- Patient and Family Advocate, Canadian Partnership Against Cancer, Toronto, Ontario, Canada
| | - Martin Tammemagi
- Department of Health Sciences, Brock University, St. Catharines, Ontario, Canada
| | - Jana L. Taylor
- Department of Radiology, McGill University, Montreal, Quebec, Canada
| | - Ming-Sound Tsao
- Department of Laboratory Medicine and Pathobiology, University Health Network and Princess Margaret Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Matthew T. Warkentin
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Ontario, Canada
| | - Kazuhiro Yasufuku
- Division of Thoracic Surgery, Department of Surgery and Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto General Hospital, University Health Network, Toronto, Ontario, Canada
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Agusti A, Alcazar B, Cosio B, Echave JM, Faner R, Izquierdo JL, Marin JM, Soler-Cataluña JJ, Celli B. Time for a change: anticipating the diagnosis and treatment of COPD. Eur Respir J 2020; 56:56/1/2002104. [DOI: 10.1183/13993003.02104-2020] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 06/15/2020] [Indexed: 01/12/2023]
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Bibault JE, Xing L. Screening for chronic obstructive pulmonary disease with artificial intelligence. LANCET DIGITAL HEALTH 2020; 2:e216-e217. [PMID: 33328053 DOI: 10.1016/s2589-7500(20)30076-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 03/20/2020] [Indexed: 12/15/2022]
Affiliation(s)
- Jean-Emmanuel Bibault
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Lei Xing
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA 94305, USA.
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