Retrospective Validation of an AI-Based Spirometry Decision Support Tool in a COPD Diagnostic Pathway

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Introduction Accurate interpretation of spirometry is essential for diagnosing chronic obstructive pulmonary disease (COPD). AI-based tools have the potential to enhance diagnostic accuracy and streamline workflow. ArtiQ. Spiro is an AI-based software that provides spirometry quality feedback and diagnostic support. As part of efforts to integrate ArtiQ. Spiro into our COPD diagnostic pathway in Glasgow, we conducted a retrospective validation study comparing its performance with ARTP-registered clinician interpretation and diagnostic pathway outcomes. Methods We retrospectively analysed spirometry data from 2 separate cohorts totalling 248 patients who attended for direct-access COPD diagnostic pathway in 2022 and 2024. The diagnostic support outputs of ArtiQ. Spiro were compared to interpretations by ARTP-registered clinicians and final diagnostic pathway outcomes to determine concordance. Results ArtiQ. Spiro demonstrated excellent concordance with pathway outcomes. There was near-complete agreement between a 'normal' AI spirometry interpretation and a 'normal' clinician-reported spirometry and pathway outcome (NPV = 0.942). Additionally, clinical user experience feedback on Artiq. Spiro was positive, highlighting its potential to enhance workflow efficiency. Conclusion Based on these results, we will proceed with implementing ArtiQ. Spiro in our COPD diagnostic pathway, which currently manages over 600 patients per month. AI-assisted interpretation has the potential to support junior or inexperienced clinicians in providing pathway outcomes, optimising resource utilisation, enhancing early accurate diagnosis of COPD, and releasing senior clinician time for complex cases.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.