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Giri PC, Chowdhury AM, Bedoya A, Chen H, Lee HS, Lee P, Henriquez C, MacIntyre NR, Huang YCT. Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going? Front Physiol 2021; 12:678540. [PMID: 34248665 PMCID: PMC8264499 DOI: 10.3389/fphys.2021.678540] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 06/02/2021] [Indexed: 12/24/2022] Open
Abstract
Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the lung (DLCO). The results are usually interpreted by the clinicians using discrete numeric data according to published guidelines. PFT interpretations by clinicians, however, are known to have inter-rater variability and the inaccuracy can impact patient care. This variability may be caused by unfamiliarity of the guidelines, lack of training, inadequate understanding of lung physiology, or simply mental lapses. A rules-based automated interpretation system can recapitulate expert’s pattern recognition capability and decrease errors. ML can also be used to analyze continuous data or the graphics, including the flow-volume loop, the DLCO and the nitrogen washout curves. These analyses can discover novel physiological biomarkers. In the era of wearables and telehealth, particularly with the COVID-19 pandemic restricting PFTs to be done in the clinical laboratories, ML can also be used to combine mobile spirometry results with an individual’s clinical profile to deliver precision medicine. There are, however, hurdles in the development and commercialization of the ML-assisted PFT interpretation programs, including the need for high quality representative data, the existence of different formats for data acquisition and sharing in PFT software by different vendors, and the need for collaboration amongst clinicians, biomedical engineers, and information technologists. Hurdles notwithstanding, the new developments would represent significant advances that could be the future of PFT, the oldest test still in use in clinical medicine.
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Affiliation(s)
- Paresh C Giri
- Division of Pulmonary and Critical Care Medicine, Loma Linda University Medical Center, Loma Linda, CA, United States
| | - Anand M Chowdhury
- Division of Pulmonary, Allergy and Critical Care Medicine, Duke University Medical Center, Durham, NC, United States
| | - Armando Bedoya
- Division of Pulmonary, Allergy and Critical Care Medicine, Duke University Medical Center, Durham, NC, United States
| | - Hengji Chen
- Department of Mechanical Engineering and Materials Science, Pratt School of Engineering, Duke University Medical Center, Durham, NC, United States
| | - Hyun Suk Lee
- Hartford HealthCare, Hartford, CT, United States
| | - Patty Lee
- Division of Pulmonary, Allergy and Critical Care Medicine, Duke University Medical Center, Durham, NC, United States
| | - Craig Henriquez
- Department of Mechanical Engineering and Materials Science, Pratt School of Engineering, Duke University Medical Center, Durham, NC, United States
| | - Neil R MacIntyre
- Division of Pulmonary, Allergy and Critical Care Medicine, Duke University Medical Center, Durham, NC, United States
| | - Yuh-Chin T Huang
- Division of Pulmonary, Allergy and Critical Care Medicine, Duke University Medical Center, Durham, NC, United States
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Topalovic M, Das N, Burgel PR, Daenen M, Derom E, Haenebalcke C, Janssen R, Kerstjens HAM, Liistro G, Louis R, Ninane V, Pison C, Schlesser M, Vercauter P, Vogelmeier CF, Wouters E, Wynants J, Janssens W. Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir J 2019; 53:13993003.01660-2018. [PMID: 30765505 DOI: 10.1183/13993003.01660-2018] [Citation(s) in RCA: 86] [Impact Index Per Article: 17.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 01/25/2019] [Indexed: 12/29/2022]
Abstract
The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion that relies on the recognition of patterns and the clinical context for detection of specific diseases. In this study, we aimed to explore the accuracy and interrater variability of pulmonologists when interpreting PFTs compared with artificial intelligence (AI)-based software that was developed and validated in more than 1500 historical patient cases.120 pulmonologists from 16 European hospitals evaluated 50 cases with PFT and clinical information, resulting in 6000 independent interpretations. The AI software examined the same data. American Thoracic Society/European Respiratory Society guidelines were used as the gold standard for PFT pattern interpretation. The gold standard for diagnosis was derived from clinical history, PFT and all additional tests.The pattern recognition of PFTs by pulmonologists (senior 73%, junior 27%) matched the guidelines in 74.4±5.9% of the cases (range 56-88%). The interrater variability of κ=0.67 pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6±8.7% of the cases (range 24-62%) with a large interrater variability (κ=0.35). The AI-based software perfectly matched the PFT pattern interpretations (100%) and assigned a correct diagnosis in 82% of all cases (p<0.0001 for both measures).The interpretation of PFTs by pulmonologists leads to marked variations and errors. AI-based software provides more accurate interpretations and may serve as a powerful decision support tool to improve clinical practice.
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Affiliation(s)
- Marko Topalovic
- Respiratory Medicine, University Hospital Leuven, Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Nilakash Das
- Respiratory Medicine, University Hospital Leuven, Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Pierre-Régis Burgel
- Cochin Hospital, AP-HP, Université Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Marc Daenen
- Dept of Respiratory Medicine, Hospital Oost-Limburg, Genk, Belgium
| | - Eric Derom
- Dept of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
| | | | - Rob Janssen
- Dept of Pulmonary Medicine, Canisius Wilhelmina Hospital, Nijmegen, The Netherlands
| | - Huib A M Kerstjens
- Dept of Pulmonary Medicine and Tuberculosis, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Giuseppe Liistro
- Dept of Pneumology, Cliniques Universitaires St-Luc, Université Catholique de Louvain, Brussels, Belgium
| | - Renaud Louis
- Dept of Respiratory Medicine, University Hospital, Liege, Belgium
| | - Vincent Ninane
- Dept of Respiratory Medicine, Saint-Pierre Hospital, Université Libre de Bruxelles, Brussels, Belgium
| | - Christophe Pison
- Service Hospitalier Universitaire de Pneumologie et Physiologie, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France
| | - Marc Schlesser
- Dept of Pulmonary Medicine, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg
| | - Piet Vercauter
- Dept of Respiratory Medicine, Onze-Lieve-Vrouw Hospital, Aalst, Belgium
| | - Claus F Vogelmeier
- Dept of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Member of the German Center for Lung Research (DZL), Marburg, Germany
| | - Emiel Wouters
- Dept of Respiratory Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Jokke Wynants
- Dept of Pneumology, Jessa Hospital, Hasselt, Belgium.,For a full list of Pulmonary Function Study Investigators, please refer to the Acknowledgements section
| | - Wim Janssens
- Respiratory Medicine, University Hospital Leuven, Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
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