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Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

Marko Topalovic, Nilakash Das, Pierre- Régis Burgel, Marc Daenen, Eric Derom, Christel Haenebalcke, Rob Janssen, Huib A. M. Kerstjens, Giuseppe Liistro, Renaud Louis, Vincent Ninane, Christophe Pison, Marc Schlesser, Piet Vercauter, Claus F. Vogelmeier, Emiel Wouters, Jokke Wynants, Wim Janssens on behalf of the Pulmonary Function Study Investigators
European Respiratory Journal 2019; DOI: 10.1183/13993003.01660-2018
Marko Topalovic
1Respiratory Medicine, University Hospital Leuven, Chronic Diseases, Metabolism and Ageing, KU Leuven, Belgium
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Nilakash Das
1Respiratory Medicine, University Hospital Leuven, Chronic Diseases, Metabolism and Ageing, KU Leuven, Belgium
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Pierre- Régis Burgel
2Cochin Hospital, Assistance Publique Hôpitaux de Paris, Université Paris Descartes, Sorbonne Paris Cité, Paris, France
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Marc Daenen
3Department of Respiratory Medicine, Hospital Oost-Limburg, Genk, Belgium
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Eric Derom
4Department of Respiratory Medicine, Ghent University Hospital, Ghent, Belgium
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Christel Haenebalcke
5Department of Respiratory Medicine, AZ Sint-Jan Hospital, Bruges, Belgium
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Rob Janssen
6Department of Pulmonary Medicine, Canisius Wilhelmina Hospital, Nijmegen, The Netherlands
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Huib A. M. Kerstjens
7Department of Pulmonary Medicine and Tuberculosis, University of Groningen, and University Medical Center Groningen, Groningen, The Netherlands
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Giuseppe Liistro
8Université Catholique de Louvain (UCL), Department of Pneumology, Cliniques universitaires St-Luc, Brussels, Belgium
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Renaud Louis
9Department of Respiratory Medicine, University Hospital, Liege, Belgium
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Vincent Ninane
10Department of Respiratory Medicine, Saint-Pierre Hospital, Université Libre de Bruxelles, Brussels, Belgium
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Christophe Pison
11Service hospitalier universitaire de Pneumologie et Physiologie, Centre Hospitalier Universitaire Grenoble Alpes, Université Grenoble Alpes, France
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Marc Schlesser
12Department of Pulmonary Medicine, Centre Hospitalier de Luxembourg, Luxembourg
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Piet Vercauter
13Department of Respiratory Medicine, Onze-Lieve-Vrouw Hospital, Aalst, Belgium
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Claus F. Vogelmeier
14Department of Medicine, Pulmonary and Critical Care Medicine, University Medical Center Giessen and Marburg, Marburg, Germany, member of the German Center for Lung Research (DZL)
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Emiel Wouters
15Department of Respiratory Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
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Jokke Wynants
16Department of Pneumology, Jessa Hospital, Hasselt, Belgium
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Wim Janssens
1Respiratory Medicine, University Hospital Leuven, Chronic Diseases, Metabolism and Ageing, KU Leuven, Belgium
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Abstract

The interpretation of pulmonary function tests (PFTs) to diagnose respiratory diseases is built on expert opinion which relies on the recognition of patterns and clinical context for the detection of specific diseases. In the study, we aimed to explore the accuracy and inter-rater variability of pulmonologists when interpreting PFTs and compared it against that of artificial intelligence (AI)-based software which was developed and validated in more than 1500 historical patient cases.

120 pulmonologists from 16 European hospitals evaluated 50 cases comprising with PFT and clinical information resulting in 6000 independent interpretations. AI software examined the same data. ATS/ERS 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 inter-rater variability of 0.67 (kappa) pointed to a common agreement. Pulmonologists made correct diagnoses in 44.6% (±8.7) of the cases (range: 24–62%) with a large inter-rater variability (kappa=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.

Footnotes

This manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article.

Conflict of interest: Dr. Topalovic has nothing to disclose.

Conflict of interest: Nilakash Das has nothing to disclose.

Conflict of interest: Dr. Burgel reports personal fees from Astra-Zeneca, personal fees from Boehringer Ingelheim, personal fees from Chiesi, personal fees from Novartis, personal fees from TEVA, personal fees from VERTEX, outside the submitted work.

Conflict of interest: Dr. Daenen has nothing to disclose.

Conflict of interest: Dr. Derom has nothing to disclose.

Conflict of interest: Dr. Haenebalcke reports personal fees from NOVARTIS, personal fees from CHIESI, personal fees from GLAXO SMITH KLINE , personal fees from ASTRA ZENECA, outside the submitted work.

Conflict of interest: Dr. Janssen has nothing to disclose.

Conflict of interest: Dr. Kerstjens has nothing to disclose.

Conflict of interest: Dr. Liistro has nothing to disclose.

Conflict of interest: Dr. LOUIS reports grants and personal fees from GSK, personal fees from AZ, grants and personal fees from Novartis, grants from Chiesi, outside the submitted work.

Conflict of interest: Dr. Ninane has nothing to disclose.

Conflict of interest: Dr. Pison has nothing to disclose.

Conflict of interest: Marc Schlesser

Conflict of interest: Dr. Vercauter has nothing to disclose.

Conflict of interest: Dr. Vogelmeier reports personal fees from Almirall, grants and personal fees from AstraZeneca, grants and personal fees from Boehringer Ingelheim, grants and personal fees from Chiesi, grants and personal fees from GlaxoSmithKline, grants and personal fees from Grifols, grants and personal fees from Mundipharma, grants and personal fees from Novartis, grants and personal fees from Takeda, personal fees from Cipla, personal fees from Berlin Chemie/Menarini, personal fees from CSL Behring, personal fees from Teva, grants from German Federal Ministry of Education and Research (BMBF) Competence Network Asthma and COPD (ASCONET), grants from Bayer Schering Pharma AG, grants from MSD, grants from Pfizer, outside the submitted work.

Conflict of interest: E. Wouters

Conflict of interest: Dr. Wynants has nothing to disclose.

Conflict of interest: Dr. Janssens has nothing to disclose.

This is a PDF-only article. Please click on the PDF link above to read it.

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Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests
Marko Topalovic, Nilakash Das, Pierre- Régis Burgel, Marc Daenen, Eric Derom, Christel Haenebalcke, Rob Janssen, Huib A. M. Kerstjens, Giuseppe Liistro, Renaud Louis, Vincent Ninane, Christophe Pison, Marc Schlesser, Piet Vercauter, Claus F. Vogelmeier, Emiel Wouters, Jokke Wynants, Wim Janssens
European Respiratory Journal Jan 2019, 1801660; DOI: 10.1183/13993003.01660-2018

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Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests
Marko Topalovic, Nilakash Das, Pierre- Régis Burgel, Marc Daenen, Eric Derom, Christel Haenebalcke, Rob Janssen, Huib A. M. Kerstjens, Giuseppe Liistro, Renaud Louis, Vincent Ninane, Christophe Pison, Marc Schlesser, Piet Vercauter, Claus F. Vogelmeier, Emiel Wouters, Jokke Wynants, Wim Janssens
European Respiratory Journal Jan 2019, 1801660; DOI: 10.1183/13993003.01660-2018
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