Validation of artificial intelligence spirometry diagnostic support software in primary care : a blinded diagnostic accuracy study.

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dc.contributor.affiliationUNSW Sydney, Australia; Imperial College London; Guy's and St Thomas' NHS Foundation Trust, London; Cambridge University Hospitals NHS Foundation Trust; George Eliot Hospital NHS Trust, Nuneaton; University Hospitals Coventry and Warwickshire NHS Trust; et al.
dc.contributor.authorSunjaya, Anthony
dc.contributor.authorEdwards, George D
dc.contributor.authorHarvey, Jennifer
dc.contributor.authorSylvester, Karl
dc.contributor.authorPurvis, Joanna
dc.contributor.authorRutter, Matthew
dc.contributor.authorShakespeare, Joanna
dc.contributor.authorMoore, Vicky
dc.contributor.authorEl-Emir, Ethaar
dc.contributor.authorDoe, Gillian
dc.contributor.authorVan Orshoven, Karolien
dc.contributor.authorPatel, Suhani
dc.contributor.authorde Vos, Maarten
dc.contributor.authorElmahy, Ahmed
dc.contributor.authorCuyvers, Benoit
dc.contributor.authorDesbordes, Paul
dc.contributor.authorSehdev, Satesh
dc.contributor.authorEvans, Rachael A
dc.contributor.authorMorgan, Michael D
dc.contributor.authorRussell, Richard
dc.contributor.authorJarrold, Ian
dc.contributor.authorSpain, Nannette
dc.contributor.authorTaylor, Stephanie
dc.contributor.authorScott, David A
dc.contributor.authorPrevost, A Toby
dc.contributor.authorHopkinson, Nicholas S
dc.contributor.authorKon, Samantha
dc.contributor.authorTopalovic, Marko
dc.contributor.authorMan, William D-C
dc.contributor.departmentCardio Respiratory Unit
dc.contributor.roleHealthcare Scientists
dc.contributor.trustauthorPurvis, Joanna
dc.date.accessioned2025-12-19T14:39:22Z
dc.date.available2025-12-19T14:39:22Z
dc.date.issued2025-09-29
dc.descriptionCopyright ©The authors 2025 This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. For commercial reproduction rights and permissions contact permissions@ersnet.org
dc.description.abstractOBJECTIVE AND DESIGN: The objective of the present study was to assess the discriminative accuracy of artificial intelligence (AI) software to identify COPD and other chronic respiratory diseases from primary care spirometry. This was a diagnostic study with blinded analysis. METHODS: Retrospective hand-held spirometry data from consecutive patients attending primary care clinics in Hillingdon (London, UK) between September 2015 and March 2019 were used. The index diagnosis was the "preferred" diagnosis determined by AI software (highest probability) using supervised random-forest machine learning to interpret raw spirometry data and basic demographics. The reference diagnosis was based on the consensus of expert pulmonologists with access to primary and secondary care medical notes and results of relevant investigations. Cross-tabulation of the index test results by the results of the reference standard for COPD and other respiratory disease categories provided the main outcome measures. RESULTS: In this primary care spirometry dataset from 1113 patients, 543 (48.8%) had a reference diagnosis of COPD. AI preferred diagnosis detected 456, achieving a sensitivity of 84.0% (95% CI 80.6-87.0%), specificity of 86.8% (83.8-89.5%), accuracy of 85.4% (83.2-87.5%) with area under curve (AUC) of 0.914 (0.896-0.930). AI preferred diagnosis identified 187 out of 249 patients with reference diagnosis of interstitial lung disease and 59 out of 107 patients with asthma, with AUCs of 0.900 (0.880-0.916) and 0.814 (0.790-0.836), respectively. CONCLUSION: AI software achieved high sensitivity and specificity in identifying COPD using spirometry and basic demographic data and may support accurate diagnosis of COPD in primary care. AI software performed less well for other chronic respiratory disease categories.
dc.identifier.FullTexthttps://westmid.openrepository.com/entities/publication/34214276-a2f0-451b-9f33-0226912be541
dc.identifier.citationSunjaya A, Edwards GD, Harvey J, Sylvester K, Purvis J, Rutter M, Shakespeare J, Moore V, El-Emir E, Doe G, Van Orshoven K, Patel S, de Vos M, Elmahy A, Cuyvers B, Desbordes P, Sehdev S, Evans RA, Morgan MD, Russell R, Jarrold I, Spain N, Taylor S, Scott DA, Prevost AT, Hopkinson NS, Kon S, Topalovic M, Man WD. Validation of artificial intelligence spirometry diagnostic support software in primary care: a blinded diagnostic accuracy study. ERJ Open Res. 2025 Sep 29;11(5):00116-2025. doi: 10.1183/23120541.00116-2025.
dc.identifier.doi10.1183/23120541.00116-2025
dc.identifier.eissn2312-0541
dc.identifier.pmid41031099
dc.identifier.urihttps://westmid.openrepository.com/handle/20.500.14200/9345
dc.language.isoen
dc.publisherEuropean Respiratory Society
dc.relation.urlhttps://pmc.ncbi.nlm.nih.gov/articles/PMC12477482/
dc.rightsAttribution-NonCommercial 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/
dc.source.journaltitleERJ Open Research
dc.subjectDiagnostic techniques, respiratory system
dc.subjectSpirometry
dc.subjectArtificial intelligence
dc.titleValidation of artificial intelligence spirometry diagnostic support software in primary care : a blinded diagnostic accuracy study.
dc.typeArticle
dspace.entity.typePublication
oa.grant.openaccessyes
rioxxterms.licenseref.startdate2025-09-28
rioxxterms.versionVoR
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