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Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

The European respiratory journal, 2019-04, Vol.53 (4), p.1801660 [Peer Reviewed Journal]

Copyright ©ERS 2019. ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0903-1936 ;ISSN: 1399-3003 ;EISSN: 1399-3003 ;DOI: 10.1183/13993003.01660-2018 ;PMID: 30765505

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Title:
Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests
Author: Topalovic, Marko ; Das, Nilakash ; Burgel, Pierre-Régis ; Daenen, Marc ; Derom, Eric ; Haenebalcke, Christel ; Janssen, Rob ; Kerstjens, Huib A M ; Liistro, Giuseppe ; Louis, Renaud ; Ninane, Vincent ; Pison, Christophe ; Schlesser, Marc ; Vercauter, Piet ; Vogelmeier, Claus F ; Wouters, Emiel ; Wynants, Jokke ; Janssens, Wim
Subjects: Cardiovascular & respiratory systems ; Human health and pathology ; Human health sciences ; Life Sciences ; Pulmonology and respiratory tract ; Sciences de la santé humaine ; Systèmes cardiovasculaire & respiratoire
Is Part Of: The European respiratory journal, 2019-04, Vol.53 (4), p.1801660
Description: 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.
Publisher: England: European Respiratory Society
Language: English
Identifier: ISSN: 0903-1936
ISSN: 1399-3003
EISSN: 1399-3003
DOI: 10.1183/13993003.01660-2018
PMID: 30765505
Source: Alma/SFX Local Collection

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Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests

Copyright ©ERS 2019. ;Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0903-1936 ;ISSN: 1399-3003 ;EISSN: 1399-3003 ;DOI: 10.1183/13993003.01660-2018 ;PMID: 30765505

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