{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T18:02:44Z","timestamp":1789581764855,"version":"build-2803163510"},"reference-count":91,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) has expanded into respiratory disease diagnosis, subtyping, and prognosis, enabling early detection and precision care. However, AI applications in Preserved Ratio Impaired Spirometry (PRISm) remain nascent. This study analyzes this gap to guide future research.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A systematic review was conducted to analyze the application of AI in PRISm, searching across PubMed, Cochrane Library, Web of Science, Ovid Medline, Scopus and Embase.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of eleven studies were included, all of which focused on diagnostic and classification tasks. Among these, three utilized radiomics models, four employed machine learning algorithms, two integrated machine learning with radiomics, and two applied deep learning approaches. Nine studies were published within the past two years, with results demonstrating the high performance and developmental potential of AI technologies in this domain. AI research on PRISm spans multiple disciplines, including exhaled metabolomics, environmental exposure assessment, radiomics, and deep learning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Existing studies have preliminarily validated the technical feasibility of artificial intelligence for the early identification of PRISm from multiple perspectives, including imaging, metabolism, and environmental exposure. Modeling strategies that integrate multi-source data have demonstrated superior discriminatory performance compared to single-modality approaches. In the future, with the integration and sharing of multicenter data under privacy-compliant conditions, coupled with the continuous evolution of algorithm architectures toward enhanced generalizability, AI applications for PRISm are expected to transition from static identification to dynamic early warning, thereby providing more robust technical support for precise risk stratification of this condition.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0353021","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T17:21:23Z","timestamp":1789579283000},"page":"e0353021","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comprehensive application of artificial intelligence in preserved ratio impaired spirometry: A systematic literature review"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0025-0116","authenticated-orcid":true,"given":"Qian","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruihan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhuan","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayajiang","family":"Jingesi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"issue":"2","key":"pone.0353021.ref001","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1097\/MCP.0000000000001140","article-title":"Abnormal spirometry in individuals with a smoking history and no known obstructive lung disease: current understanding and clinical implications","volume":"31","author":"K Tong","year":"2025","journal-title":"Curr Opin Pulm Med"},{"issue":"1","key":"pone.0353021.ref002","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1186\/s12890-023-02451-2","article-title":"Longitudinal mortality of preserved ratio impaired spirometry in a middle-aged Asian cohort","volume":"23","author":"S Sin","year":"2023","journal-title":"BMC Pulm Med"},{"issue":"2","key":"pone.0353021.ref003","doi-asserted-by":"crossref","first-page":"621","DOI":"10.3233\/JAD-210162","article-title":"Lung Function Impairment and the Risk of Incident Dementia: The Rotterdam Study","volume":"82","author":"T Xiao","year":"2021","journal-title":"J Alzheimers Dis"},{"issue":"1","key":"pone.0353021.ref004","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1186\/s12890-024-03150-2","article-title":"COPD, PRISm and lung function reduction affect the brain cortical structure: a Mendelian randomization study","volume":"24","author":"C Fang","year":"2024","journal-title":"BMC Pulm Med"},{"issue":"8","key":"pone.0353021.ref005","doi-asserted-by":"crossref","first-page":"4141","DOI":"10.1007\/s00415-022-11027-9","article-title":"Lung function impairment in relation to cognition and vascular brain lesions: the Rotterdam Study","volume":"269","author":"T Xiao","year":"2022","journal-title":"J Neurol"},{"issue":"18","key":"pone.0353021.ref006","doi-asserted-by":"crossref","first-page":"2478","DOI":"10.1111\/1759-7714.14095","article-title":"Impact of coexistent preserved ratio impaired spirometry on the survival of patients with lung cancer: Analysis of data from the Korean Association for Lung Cancer Registry","volume":"12","author":"IR Heo","year":"2021","journal-title":"Thorac Cancer"},{"issue":"9","key":"pone.0353021.ref007","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.21037\/tlcr-22-63","article-title":"Lung function impairment in the German Lung Cancer Screening Intervention Study (LUSI): prevalence, symptoms, and associations with lung cancer risk, tumor histology and all-cause mortality","volume":"11","author":"R Kaaks","year":"2022","journal-title":"Transl Lung Cancer Res"},{"issue":"1","key":"pone.0353021.ref008","doi-asserted-by":"crossref","first-page":"00895","DOI":"10.1183\/23120541.00895-2023","article-title":"The many faces of COPD in real life: a longitudinal analysis of the NOVELTY cohort","volume":"10","author":"A Agust\u00ed","year":"2024","journal-title":"ERJ Open Res"},{"key":"pone.0353021.ref009","article-title":"Relationship between symptoms and results on spirometry in adults seen in non-tertiary public health facilities presenting with preserved ratio impaired spirometry","author":"M Martinelli","year":"2024","journal-title":"Monaldi Arch Chest D"},{"key":"pone.0353021.ref010","doi-asserted-by":"crossref","first-page":"767","DOI":"10.2147\/COPD.S506115","article-title":"Associated Factors and Pulmonary Function Outcomes of Preserved Ratio Impaired Spirometry: A Scoping Review","volume":"20","author":"H Xu","year":"2025","journal-title":"Int J Chron Obstruct Pulmon Dis"},{"key":"pone.0353021.ref011","first-page":"753","article-title":"Preserved Ratio Impaired Spirometry (PRISm): A Global Epidemiological Overview, Radiographic Characteristics, Comorbid Associations, and Differentiation from Chronic Obstructive Pulmonary Disease","volume":"19","author":"J Huang","year":"2024","journal-title":"Int J Chronic Obstr"},{"issue":"1","key":"pone.0353021.ref012","doi-asserted-by":"crossref","first-page":"450","DOI":"10.21037\/jtd-24-1582","article-title":"Preserved ratio impaired spirometry: clinical, imaging and artificial intelligence perspective","volume":"17","author":"Q Jin","year":"2025","journal-title":"J Thorac Dis"},{"key":"pone.0353021.ref013","doi-asserted-by":"crossref","first-page":"105812","DOI":"10.1016\/j.ijmedinf.2025.105812","article-title":"Deep learning and machine learning in CT-based COPD diagnosis: Systematic review and meta-analysis","volume":"196","author":"Q Wu","year":"2025","journal-title":"Int J Med Inform"},{"issue":"2","key":"pone.0353021.ref014","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.pccm.2023.03.004","article-title":"Twenty years of changes in the definition of early chronic obstructive pulmonary disease","volume":"1","author":"D Chen","year":"2023","journal-title":"Chin Med J Pulm Crit Care Med"},{"issue":"1","key":"pone.0353021.ref015","doi-asserted-by":"crossref","first-page":"2988","DOI":"10.1038\/s41598-023-29922-0","article-title":"Preserved ratio impaired spirometry with or without restrictive spirometric abnormality","volume":"13","author":"S Miura","year":"2023","journal-title":"Sci Rep"},{"issue":"11","key":"pone.0353021.ref016","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1513\/AnnalsATS.202301-050OC","article-title":"Longitudinal Changes and Association of Respiratory Symptoms with Preserved Ratio Impaired Spirometry (PRISm): The Nagahama Study","volume":"20","author":"M Kogo","year":"2023","journal-title":"Ann Am Thorac Soc"},{"issue":"1","key":"pone.0353021.ref017","doi-asserted-by":"crossref","first-page":"58","DOI":"10.4187\/respcare.07712","article-title":"Preserved Ratio Impaired Spirometry in a Spirometry Database","volume":"66","author":"A Schwartz","year":"2021","journal-title":"Respir Care"},{"issue":"1","key":"pone.0353021.ref018","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/s12931-014-0089-y","article-title":"Epidemiology, genetics, and subtyping of preserved ratio impaired spirometry (PRISm) in COPDGene","volume":"15","author":"ES Wan","year":"2014","journal-title":"Respir Res"},{"issue":"6","key":"pone.0353021.ref019","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1016\/j.chest.2024.12.025","article-title":"Preserved Ratio Impaired Spirometry Prevalence, Risk Factors, and Outcomes: A Systematic Review and Meta-Analysis","volume":"167","author":"NM Robertson","year":"2025","journal-title":"Chest"},{"issue":"10","key":"pone.0353021.ref020","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.arbres.2024.05.033","article-title":"Spirometric Transition of at Risk Individuals and Risks for Progression to Chronic Obstructive Pulmonary Disease in General Population","volume":"60","author":"YS Jo","year":"2024","journal-title":"Arch Bronconeumol"},{"issue":"1","key":"pone.0353021.ref021","doi-asserted-by":"crossref","DOI":"10.1136\/bmjresp-2022-001298","article-title":"Prevalence and risk factors for COPD in subjects with preserved ratio impaired spirometry","volume":"9","author":"R Kanetake","year":"2022","journal-title":"BMJ Open Respiratory Research"},{"issue":"3","key":"pone.0353021.ref022","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.chest.2021.09.021","article-title":"Significant spirometric transitions and preserved ratio impaired spirometry among ever smokers","volume":"161","author":"ES Wan","year":"2022","journal-title":"Chest"},{"key":"pone.0353021.ref023","doi-asserted-by":"crossref","first-page":"2791","DOI":"10.2147\/COPD.S384074","article-title":"The Prognostic Performance of Lung Diffusing Capacity in Preserved Ratio Impaired Spirometry: An Observational Cohort Study","volume":"17","author":"H Ogata","year":"2022","journal-title":"Int J Chron Obstruct Pulmon Dis"},{"key":"pone.0353021.ref024","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.2147\/COPD.S425934","article-title":"Clinical Characteristics and Medical Utilization of Smokers with Preserved Ratio Impaired Spirometry","volume":"18","author":"Y-Y Shin","year":"2023","journal-title":"Int J Chron Obstruct Pulmon Dis"},{"issue":"2","key":"pone.0353021.ref025","doi-asserted-by":"crossref","first-page":"18","DOI":"10.3390\/medsci12020018","article-title":"Analysis of Endocrine and Inflammatory Markers in Preserved Ratio Impaired Spirometry","volume":"12","author":"FR Jesus","year":"2024","journal-title":"Med Sci (Basel)"},{"issue":"12","key":"pone.0353021.ref026","doi-asserted-by":"crossref","first-page":"2460","DOI":"10.21037\/tlcr-23-548","article-title":"Serum-based biomarkers associated with lung cancer risk and cause-specific mortality in the German randomized Lung Cancer Screening Intervention (LUSI) trial","volume":"12","author":"FO Cort\u00e9s-Ib\u00e1\u00f1ez","year":"2023","journal-title":"Transl Lung Cancer Res"},{"issue":"1","key":"pone.0353021.ref027","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1186\/s40644-024-00720-9","article-title":"Thoracic high resolution computed tomography evaluation of imaging abnormalities of 108 lung cancer patients with different pulmonary function","volume":"24","author":"L Zhu","year":"2024","journal-title":"Cancer Imaging"},{"issue":"1","key":"pone.0353021.ref028","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1186\/s12931-022-02113-7","article-title":"Subtyping preserved ratio impaired spirometry (PRISm) by using quantitative HRCT imaging characteristics","volume":"23","author":"J Lu","year":"2022","journal-title":"Respir Res"},{"issue":"1","key":"pone.0353021.ref029","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1186\/s12916-024-03836-8","article-title":"Impaired pulmonary function increases the risk of gout: evidence from a large cohort study in the UK Biobank","volume":"22","author":"Z Kang","year":"2024","journal-title":"BMC Med"},{"issue":"5","key":"pone.0353021.ref030","first-page":"400","article-title":"Subtypes of COPD Have Unique Distributions and Differential Risk of Mortality","volume":"6","author":"KA Young","year":"2019","journal-title":"Chronic Obstr Pulm Dis"},{"issue":"1","key":"pone.0353021.ref031","doi-asserted-by":"crossref","first-page":"2","DOI":"10.3390\/arm93010002","article-title":"The Role of Impulse Oscillometry in Detection of Preserved Ratio Impaired Spirometry (PRISm)","volume":"93","author":"C Liwsrisakun","year":"2025","journal-title":"Adv Respir Med"},{"issue":"11","key":"pone.0353021.ref032","first-page":"1087","article-title":"Correlation of Impulse oscillometry system indices with conventional pulmonary function tests in patients with obstructive pulmonary ventilation dysfunction","volume":"63","author":"B Wei","year":"2024","journal-title":"Zhonghua Nei Ke Za Zhi"},{"issue":"1","key":"pone.0353021.ref033","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1186\/s12890-025-03668-z","article-title":"Regional lung function assessment using electrical impedance tomography in COPD, PRISm, and normal spirometry subjects: insights into early diagnostic potential","volume":"25","author":"J Li","year":"2025","journal-title":"BMC Pulm Med"},{"issue":"9","key":"pone.0353021.ref034","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1136\/thorax-2022-219301","article-title":"Characterising airway obstructive, dysanaptic and PRISm phenotypes of prematurity-associated lung disease","volume":"78","author":"M Cousins","year":"2023","journal-title":"Thorax"},{"issue":"9","key":"pone.0353021.ref035","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.1002\/ppul.26019","article-title":"Impaired exercise outcomes with significant bronchodilator responsiveness in children with prematurity-associated obstructive lung disease","volume":"57","author":"M Cousins","year":"2022","journal-title":"Pediatr Pulmonol"},{"key":"pone.0353021.ref036","doi-asserted-by":"crossref","first-page":"111912","DOI":"10.1016\/j.ejrad.2024.111912","article-title":"Computed tomography-based absolute delta radiomics nomogram for predicting perineural invasion in hypopharyngeal squamous cell carcinoma","volume":"183","author":"J Li","year":"2025","journal-title":"Eur J Radiol"},{"issue":"1","key":"pone.0353021.ref037","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1007\/s10916-025-02156-5","article-title":"Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images","volume":"49","author":"VH Le","year":"2025","journal-title":"J Med Syst"},{"issue":"4","key":"pone.0353021.ref038","doi-asserted-by":"crossref","first-page":"102032","DOI":"10.1016\/j.xcrm.2025.102032","article-title":"A CT-based deep learning-driven tool for automatic liver tumor detection and delineation in patients with cancer","volume":"6","author":"M Balaguer-Montero","year":"2025","journal-title":"Cell Rep Med"},{"key":"pone.0353021.ref039","first-page":"1","article-title":"Advancing pulmonary infection diagnosis: A comprehensive review of deep learning approaches in radiological data analysis","author":"S Yadav","year":"2025","journal-title":"Arch Comput Method E"},{"issue":"3","key":"pone.0353021.ref040","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.240541","article-title":"AI for Multistructure Incidental Findings and Mortality Prediction at Chest CT in Lung Cancer Screening","volume":"312","author":"AM Marcinkiewicz","year":"2024","journal-title":"Radiology"},{"issue":"1","key":"pone.0353021.ref041","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.233030","article-title":"A Machine Learning Model Using Cardiac CT and MRI Data Predicts Cardiovascular Events in Obstructive Coronary Artery Disease","volume":"314","author":"T Pezel","year":"2025","journal-title":"Radiology"},{"issue":"3","key":"pone.0353021.ref042","doi-asserted-by":"crossref","first-page":"485","DOI":"10.3390\/cancers17030485","article-title":"Cone-Beam CT Segmentation for Intraoperative Electron Radiotherapy Based on U-Net Variants with Transformer and Extended LSTM Approaches","volume":"17","author":"S Vockner","year":"2025","journal-title":"Cancers (Basel)"},{"issue":"3","key":"pone.0353021.ref043","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1161\/CIRCULATIONAHA.124.070413","article-title":"ADMET-AI Enables Interpretable Predictions of Drug-Induced Cardiotoxicity","volume":"151","author":"S Mukherjee","year":"2025","journal-title":"Circulation"},{"issue":"4","key":"pone.0353021.ref044","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1002\/psp4.13313","article-title":"Comparing Scientific Machine Learning With Population Pharmacokinetic and Classical Machine Learning Approaches for Prediction of Drug Concentrations","volume":"14","author":"D Valderrama","year":"2025","journal-title":"CPT Pharmacometrics Syst Pharmacol"},{"key":"pone.0353021.ref045","doi-asserted-by":"crossref","first-page":"1583486","DOI":"10.3389\/fphar.2025.1583486","article-title":"Editorial: Machine learning advancements in pharmacology: transforming drug discovery and healthcare","volume":"16","author":"MR Roosan","year":"2025","journal-title":"Front Pharmacol"},{"issue":"9","key":"pone.0353021.ref046","doi-asserted-by":"crossref","first-page":"1652","DOI":"10.1164\/rccm.202501-0028OC","article-title":"Oscillometry-defined Small Airway Dysfunction in Tobacco-exposed Adults with Impaired or Preserved Airflow","volume":"211","author":"M Abdo","year":"2025","journal-title":"Am J Respir Crit Care Med"},{"key":"pone.0353021.ref047","doi-asserted-by":"crossref","first-page":"100580","DOI":"10.1016\/j.ejro.2024.100580","article-title":"A clinical-radiomics nomogram based on automated segmentation of chest CT to discriminate PRISm and COPD patients","volume":"13","author":"T Zhou","year":"2024","journal-title":"Eur J Radiol Open"},{"issue":"1","key":"pone.0353021.ref048","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1186\/s12931-024-02964-2","article-title":"CT-based whole lung radiomics nomogram for identification of PRISm from non-COPD subjects","volume":"25","author":"T Zhou","year":"2024","journal-title":"Respir Res"},{"issue":"1","key":"pone.0353021.ref049","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.acra.2024.08.030","article-title":"Quantitative CT Imaging Features Associated with Stable PRISm using Machine Learning","volume":"32","author":"L Lukhumaidze","year":"2025","journal-title":"Acad Radiol"},{"issue":"1","key":"pone.0353021.ref050","doi-asserted-by":"crossref","first-page":"32","DOI":"10.3390\/jcm10010032","article-title":"Volatile Organic Compounds in Exhaled Breath as Fingerprints of Lung Cancer, Asthma and COPD","volume":"10","author":"IA Ratiu","year":"2020","journal-title":"J Clin Med"},{"key":"pone.0353021.ref051","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.2147\/COPD.S159684","article-title":"Validation of exhaled volatile organic compounds analysis using electronic nose as index of COPD severity","volume":"13","author":"P Finamore","year":"2018","journal-title":"Int J Chron Obstruct Pulmon Dis"},{"issue":"1","key":"pone.0353021.ref052","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1186\/s12931-025-03242-5","article-title":"Exhaled volatile organic compounds as novel biomarkers for early detection of COPD, asthma, and PRISm: a cross-sectional study","volume":"26","author":"J Tian","year":"2025","journal-title":"Respir Res"},{"key":"pone.0353021.ref053","doi-asserted-by":"crossref","first-page":"118686","DOI":"10.1016\/j.ecoenv.2025.118686","article-title":"Exploring the potential associations between single and mixed volatile compounds and preserved ratio impaired spirometry using five different approaches","volume":"302","author":"C Deng","year":"2025","journal-title":"Ecotoxicol Environ Saf"},{"issue":"1","key":"pone.0353021.ref054","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1186\/s12944-025-02674-8","article-title":"A comprehensive investigation of the relationship between dietary fatty acid intake and preserved ratio impaired spirometry: multimethodology based on NHANES","volume":"24","author":"C Deng","year":"2025","journal-title":"Lipids Health Dis"},{"key":"pone.0353021.ref055","doi-asserted-by":"crossref","first-page":"118701","DOI":"10.1016\/j.ecoenv.2025.118701","article-title":"Potential effects of endocrine-disrupting chemicals on preserved ratio impaired spirometry revealed by five different approaches","volume":"302","author":"C Deng","year":"2025","journal-title":"Ecotoxicol Environ Saf"},{"issue":"1","key":"pone.0353021.ref056","doi-asserted-by":"crossref","first-page":"39409","DOI":"10.1038\/s41598-025-21995-3","article-title":"Machine learning models using dual-phase CT radiomics for early detection of PRISm","volume":"15","author":"L Fu","year":"2025","journal-title":"Sci Rep"},{"issue":"11","key":"pone.0353021.ref057","doi-asserted-by":"crossref","first-page":"1786","DOI":"10.1007\/s11547-025-02061-4","article-title":"Chest CT imaging for differentiating normal, PRISm, and COPD in comparison with pulmonary function tests","volume":"130","author":"Z Ma","year":"2025","journal-title":"Radiol Med"},{"key":"pone.0353021.ref058","doi-asserted-by":"crossref","first-page":"04028","DOI":"10.7189\/jogh.16.04028","article-title":"Multimodal data-driven multitask learning for enhanced identification and classification of chronic obstructive pulmonary disease: a retrospective study","volume":"16","author":"Q Wu","year":"2026","journal-title":"J Glob Health"},{"key":"pone.0353021.ref059","first-page":"573618","article-title":"PulmoClass-3DAtt: A Self-Attention Network for Classification of COPD, PRISm and Normal","volume":"21","author":"Q Wu","year":"2026","journal-title":"Int J Chron Obstruct Pulmon Dis"},{"key":"pone.0353021.ref060","doi-asserted-by":"crossref","first-page":"107421","DOI":"10.1016\/j.cmpb.2023.107421","article-title":"Physics-informed neural entangled-ladder network for inhalation impedance of the respiratory system","volume":"231","author":"AK Kumar","year":"2023","journal-title":"Comput Methods Programs Biomed"},{"key":"pone.0353021.ref061","doi-asserted-by":"crossref","first-page":"105845","DOI":"10.1016\/j.bspc.2023.105845","article-title":"An automated deep learning pipeline for detecting user errors in spirometry test","volume":"90","author":"S Bonthada","year":"2024","journal-title":"Biomedical Signal Processing and Control"},{"issue":"2","key":"pone.0353021.ref062","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.221488","article-title":"Deep Learning-based Approach to Predict Pulmonary Function at Chest CT","volume":"307","author":"H Park","year":"2023","journal-title":"Radiology"},{"issue":"7","key":"pone.0353021.ref063","doi-asserted-by":"crossref","first-page":"4379","DOI":"10.1007\/s00330-023-10540-3","article-title":"Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT","volume":"34","author":"SD Almeida","year":"2024","journal-title":"Eur Radiol"},{"issue":"8","key":"pone.0353021.ref064","article-title":"A deep learning-based model to estimate pulmonary function from chest x-rays: multi-institutional model development and validation study in Japan","volume":"6","author":"D Ueda","year":"2024","journal-title":"Lancet Digit Health"},{"issue":"3","key":"pone.0353021.ref065","doi-asserted-by":"crossref","first-page":"1510","DOI":"10.21037\/qims-22-70","article-title":"A machine learning approach for preoperatively assessing pulmonary function with computed tomography in patients with lung cancer","volume":"13","author":"H Meng","year":"2023","journal-title":"Quant Imaging Med Surg"},{"issue":"1","key":"pone.0353021.ref066","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1007\/s12194-024-00877-9","article-title":"Radiomics and dosiomics approaches to estimate lung function after stereotactic body radiation therapy in patients with lung tumors","volume":"18","author":"Y Ieko","year":"2025","journal-title":"Radiol Phys Technol"},{"key":"pone.0353021.ref067","doi-asserted-by":"crossref","first-page":"678540","DOI":"10.3389\/fphys.2021.678540","article-title":"Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going","volume":"12","author":"PC Giri","year":"2021","journal-title":"Front Physiol"},{"issue":"6","key":"pone.0353021.ref068","article-title":"Evaluating the cumulative benefit of inspiratory CT, expiratory CT, and clinical data for COPD diagnosis and staging through deep learning","volume":"6","author":"AN Lee","year":"2024","journal-title":"Radiology"},{"issue":"1","key":"pone.0353021.ref069","doi-asserted-by":"crossref","first-page":"10447","DOI":"10.1038\/s41598-025-93359-w","article-title":"Nomogram to predict progression from preserved ratio impaired spirometry to chronic obstructive pulmonary disease","volume":"15","author":"J Wu","year":"2025","journal-title":"Sci Rep"},{"issue":"1","key":"pone.0353021.ref070","doi-asserted-by":"crossref","first-page":"2300337","DOI":"10.1183\/13993003.00337-2023","article-title":"Genome-wide association study of preserved ratio impaired spirometry (PRISm)","volume":"63","author":"DH Higbee","year":"2024","journal-title":"Eur Respir J"},{"key":"pone.0353021.ref071","doi-asserted-by":"crossref","first-page":"111393","DOI":"10.1016\/j.ejrad.2024.111393","article-title":"Artificial intelligence and explanation: How, why, and when to explain black boxes","volume":"173","author":"E Marcus","year":"2024","journal-title":"Eur J Radiol"},{"issue":"1","key":"pone.0353021.ref072","doi-asserted-by":"crossref","DOI":"10.1093\/medlaw\/fwaf005","article-title":"The fifty shades of black: about black box AI and explainability in healthcare","volume":"33","author":"VL Raposo","year":"2025","journal-title":"Med Law Rev"},{"key":"pone.0353021.ref073","doi-asserted-by":"crossref","first-page":"103113","DOI":"10.1016\/j.inffus.2025.103133","article-title":"Reflections and attentiveness on eXplainable Artificial Intelligence (XAI). The journey ahead from criticisms to human-AI collaboration","volume":"121","author":"F Herrera","year":"2025","journal-title":"Information Fusion"},{"issue":"9","key":"pone.0353021.ref074","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1007\/s10462-024-10852-w","article-title":"A review of evaluation approaches for explainable AI with applications in cardiology","volume":"57","author":"AM Salih","year":"2024","journal-title":"Artif Intell Rev"},{"key":"pone.0353021.ref075","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.csbj.2024.08.005","article-title":"Unveiling the black box: A systematic review of Explainable Artificial Intelligence in medical image analysis","volume":"24","author":"D Muhammad","year":"2024","journal-title":"Comput Struct Biotechnol J"},{"issue":"5","key":"pone.0353021.ref076","doi-asserted-by":"crossref","first-page":"2201720","DOI":"10.1183\/13993003.01720-2022","article-title":"Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation","volume":"61","author":"N Das","year":"2023","journal-title":"Eur Respir J"},{"key":"pone.0353021.ref077","doi-asserted-by":"crossref","first-page":"103470","DOI":"10.1016\/j.redox.2024.103470","article-title":"Machine learning and SHAP value interpretation for predicting comorbidity of cardiovascular disease and cancer with dietary antioxidants","volume":"79","author":"X Qi","year":"2025","journal-title":"Redox Biol"},{"key":"pone.0353021.ref078","doi-asserted-by":"crossref","first-page":"111403","DOI":"10.1016\/j.ejrad.2024.111403","article-title":"Explainable AI-based Deep-SHAP for mapping the multivariate relationships between regional neuroimaging biomarkers and cognition","volume":"174","author":"P Bhattarai","year":"2024","journal-title":"Eur J Radiol"},{"key":"pone.0353021.ref079","doi-asserted-by":"crossref","first-page":"1349373","DOI":"10.3389\/fmed.2024.1349373","article-title":"Explainable AI-driven model for gastrointestinal cancer classification","volume":"11","author":"F Binzagr","year":"2024","journal-title":"Front Med (Lausanne)"},{"key":"pone.0353021.ref080","first-page":"1","article-title":"An efficient explanation of individual classifications using game theory","volume":"11","author":"E Strumbelj","year":"2010","journal-title":"J Mach Learn Res"},{"key":"pone.0353021.ref081","first-page":"4768","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"SM Lundberg","year":"2017","journal-title":"Adv Neur In"},{"key":"pone.0353021.ref082","doi-asserted-by":"crossref","first-page":"100606","DOI":"10.1016\/j.jbo.2024.100606","article-title":"Automatic classification of spinal osteosarcoma and giant cell tumor of bone using optimized DenseNet","volume":"46","author":"J He","year":"2024","journal-title":"J Bone Oncol"},{"key":"pone.0353021.ref083","doi-asserted-by":"crossref","first-page":"106473","DOI":"10.1016\/j.neunet.2024.106473","article-title":"Cluster-CAM: Cluster-weighted visual interpretation of CNNs\u2019 decision in image classification","volume":"178","author":"Z Feng","year":"2024","journal-title":"Neural Netw"},{"issue":"16","key":"pone.0353021.ref084","doi-asserted-by":"crossref","first-page":"1699","DOI":"10.3390\/diagnostics14161699","article-title":"Machine-Learning-Enabled Diagnostics with Improved Visualization of Disease Lesions in Chest X-ray Images","volume":"14","author":"MF Rahman","year":"2024","journal-title":"Diagnostics (Basel)"},{"issue":"1","key":"pone.0353021.ref085","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1038\/s41598-020-79336-5","article-title":"A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects","volume":"11","author":"TT Ho","year":"2021","journal-title":"Sci Rep"},{"key":"pone.0353021.ref086","doi-asserted-by":"crossref","first-page":"122079","DOI":"10.1016\/j.apenergy.2023.122079","article-title":"Harnessing explainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP","volume":"353","author":"C Van Z","year":"2024","journal-title":"Appl Energ"},{"issue":"1","key":"pone.0353021.ref087","doi-asserted-by":"crossref","DOI":"10.1186\/s40537-024-00911-y","article-title":"Blind Federated Learning without initial model","volume":"11","author":"JL Salmeron","year":"2024","journal-title":"J Big Data"},{"issue":"5","key":"pone.0353021.ref088","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3637868","article-title":"Federated Learning for Mobility Applications","volume":"56","author":"M Gecer","year":"2024","journal-title":"ACM Comput Surv"},{"issue":"4","key":"pone.0353021.ref089","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MC.2023.3324975","article-title":"Exploring threats, defenses, and privacy-preserving techniques in federated learning: A survey","volume":"57","author":"RY Huang","year":"2024","journal-title":"Computer"},{"issue":"3","key":"pone.0353021.ref090","doi-asserted-by":"crossref","first-page":"5983","DOI":"10.1109\/TCE.2024.3385440","article-title":"A Review of Federated Learning Methods in Heterogeneous Scenarios","volume":"70","author":"J Pei","year":"2024","journal-title":"IEEE Trans Consumer Electron"},{"issue":"6","key":"pone.0353021.ref091","doi-asserted-by":"crossref","first-page":"901","DOI":"10.3390\/math12060901","article-title":"Challenges and Countermeasures of Federated Learning Data Poisoning Attack Situation Prediction","volume":"12","author":"J Wu","year":"2024","journal-title":"Mathematics"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0353021","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T17:21:35Z","timestamp":1789579295000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0353021"}},"subtitle":[],"editor":[{"given":"Vin\u00edcius Silva","family":"Belo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,9,16]]},"references-count":91,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9,16]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0353021","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,16]]}}}