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Validation of artificial intelligence spirometry diagnostic support software in primary care : a blinded diagnostic accuracy study.
Sunjaya, Anthony Edwards, George D Harvey, Jennifer Sylvester, Karl Purvis, Joanna Rutter, Matthew Shakespeare, Joanna Moore, Vicky El-Emir, Ethaar Doe, Gillian
Sunjaya, Anthony
Edwards, George D
Harvey, Jennifer
Sylvester, Karl
Purvis, Joanna
Rutter, Matthew
Shakespeare, Joanna
Moore, Vicky
El-Emir, Ethaar
Doe, Gillian
Affiliation
UNSW 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.
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Publication date
2025-09-29
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Abstract
OBJECTIVE 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.
Citation
Sunjaya 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.
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Article
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