Identification of COPD from primary care spirometry with artificial intelligence (AI) interpretation software : a retrospective, diagnostic accuracy study
Sunjaya, Anthony Paulo Edwards, George Harvey, Jennifer Sylvester, Karl Purvis, Joanna Rutter, Matthew Shakespeare, Joanna Moore, Vicky El-Emir, Ethaar Doe, Gillian
Sunjaya, Anthony Paulo
Edwards, George
Harvey, Jennifer
Sylvester, Karl
Purvis, Joanna
Rutter, Matthew
Shakespeare, Joanna
Moore, Vicky
El-Emir, Ethaar
Doe, Gillian
Affiliation
UNSW Sydney, Australia; 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; University Hospitals Birmingham NHS Foundation Trust; et al.
Other Contributors
Publication date
2024-10-30
Subject
Research Projects
Organizational Units
Journal Issue
Abstract
Conference abstract OA1047 from the session “Innovative perspectives on cellular mechanisms in lung diseases” of the 2024 ERS Congress, Vienna, Austria, 7-11 September 2024.
Citation
Sunjaya AP, Edwards G, 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, Evans RA, Morgan MD, Russell R, Jarrold I, Spain N, Taylor S, Scott DA, Prevost T, Hopkinson N, Kon S, Topalovic M, Man WD. Identification of COPD from primary care spirometry with artificial intelligence (AI) interpretation software: a retrospective, diagnostic accuracy study. Eur Respir J. 2024;64(Suppl 68):OA1047. doi: 10.1183/13993003.congress-2024.OA1047.
Type
Conference Output