Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests
| Autor: | Topalovic, Marko, Das, Nilakash, Burgel, Pierre-Regis, Daenen, Marc, Derom, Eric, Haenebalcke, Christel, Janssen, Rob, Kerstjens, Huib AM, Liistro, Giuseppe, Louis, Renaud, Ninane, Vincent, Pison, Christophe, Schlesser, Marc, Vercauter, Piet, Vogelmeier, Claus F, Wouters, Emiel, Wynants, Jokke, Janssens, Wim, De Pauw, R, Depuydt, C, Haenebalcke, C, Muyldermans, S, Ringoet, V, Stevens, D, Bayat, S, Benet, J, Catho, E, Claustre, J, Fedi, A, Ferjani, MA, Guzun, R, Isnard, M, Nicolas, S, Pierret, T, Pison, C, Rouches, S, Wuyam, B, Corhay, JL, Guiot, J, Ghysen, K, Renaud, L, Sibille, A, De La Barriere, H, Charpentier, C, Corhut, S, Hamdan, KA, Schlesser, M, Wirtz, G, Alabadan, E, Birsen, G, Burgel, PR, Chohra, A, Hamard, C, Lemarie, B, Lothe, MN, Martin, C, Sainte-Marie, AC, Sebane, L, Berk, Y, de Brouwer, B, Janssen, R, Kerkhoff, J, Spaanderman, A, Stegers, M, Termeer, A, van Grimbergen, I, van Veen, A, van Ruitenbeek, L, Vermeer, L, Zaal, R, Zijlker, M, Aumann, J, Cuppens, K, Degraeve, D, Demuynck, K, Dieriks, B, Pat, K, Spaas, L, Van Puijenbroek, R, Weytjens, K, Wynants, J, Adam, V, Berendes, BJ, Hardeman, E, Jordens, P, Munghen, E, Tournoy, K, Vercauter, P, Alame, T, Bruyneel, M, Gabrovska, M, Muylle, I, Ninane, V, Rozen, D, Rummens, P, Van den Broecke, S, Froidure, A, Gohy, S, Liistro, G, Pieters, T, Pilette, C, Pirson, F, Kerstjens, H, Van den Berge, M, Ten Hacken, N, Duiverman, M, Koster, D, Vosse, B, Conemans, L, Maus, M, Bischoff, M, Rutten, M, Agterhuis, D, Sprooten, R, Beutel, B, Jerrentrup, A, Klemmer, A, Viniol, C, Vogelmeier, C, Bode, H, Dooms, C, Gullentops, D, Janssens, W, Nackaerts, K, Rutens, D, Wauters, E, Wuyts, W, Derom, E, Dobbelaere, S, Loof, S, Serry, G, Putman, B, Van Acker, L, Vandeweygaerde, Y, Criel, M, Daenen, M, Gubbelmans, R, Klerkx, S, Michiels, E, Thomeer, M, Vanhauwaert, A |
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| Přispěvatelé: | UCL - (SLuc) Service de pneumologie, Groningen Research Institute for Asthma and COPD (GRIAC), Lifestyle Medicine (LM), Hôpital Cochin [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre Hospitalier Universitaire [Grenoble] (CHU), Laboratory of Fundamental and Applied Bioenergetics = Laboratoire de bioénergétique fondamentale et appliquée (LBFA), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), RS: NUTRIM - R3 - Respiratory & Age-related Health, MUMC+: MA Longziekten (3), Pulmonologie, MUMC+: MA Med Staf Spec Longziekten (9), MUMC+: MA Med Staf Artsass Longziekten (9) |
| Rok vydání: | 2018 |
| Předmět: |
Pulmonary and Respiratory Medicine
Adult Male Pulmonary function STRATEGIES Pulmonary Function Study Investigators Context (language use) DIAGNOSIS GUIDELINES [SDV.MHEP.PSR]Life Sciences [q-bio]/Human health and pathology/Pulmonology and respiratory tract Pulmonary function testing 03 medical and health sciences 0302 clinical medicine Clinical history Artificial Intelligence Pulmonary Medicine Medicine Humans 030212 general & internal medicine Prospective Studies Medical diagnosis Pulmonologists Aged Aged 80 and over Interpretation (logic) business.industry Gold standard (test) STANDARDIZATION PERFORMANCE Middle Aged 3. Good health Respiratory Function Tests Clinical Practice 030228 respiratory system Female Artificial intelligence business [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology Software |
| Zdroj: | The European Respiratory Journal, Vol. 11, no.53, p. 4 (2019) European Respiratory Journal, 53(4):1801660. EUROPEAN RESPIRATORY SOC JOURNALS LTD European Respiratory Journal European Respiratory Journal, European Respiratory Society, 2019, 53 (4), pp.1801660. ⟨10.1183/13993003.01660-2018⟩ European Respiratory Journal, 53(4):1801660. European Respiratory Society |
| ISSN: | 1399-3003 0903-1936 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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