Correction: Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT
Nikos Sourlos, GertJan Pelgrim, Hendrik Joost Wisselink, Xiaofei Yang, Gonda de Jonge, Mieneke Rook, Mathias Prokop, Grigory Sidorenkov, Marcel van Tuinen, Rozemarijn Vliegenthart, Peter M. A. van Ooijen
Abstract
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
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Taxonomy
TopicsLung Cancer Diagnosis and Treatment · Radiomics and Machine Learning in Medical Imaging · Chronic Obstructive Pulmonary Disease (COPD) Research
Correction to: European Radiology Experimental
10.1186/s41747-024-00459-9, published online 20 May 2024
In the original article, the results section “Performance evaluation and comparison” displays two statements that the authors wish to clarify to remove ambiguity:
- On page 6, “Sensitivity of AI was not significantly different for either the emphysema (p = 0.320) or the non-emphysema group (p = 0.090).”, should instead read: “Sensitivity was not significantly different between the emphysema and non-emphysema group for either AI (p = 0.80) or human reader (p = 0.54).”
- On page 7, “Also, the nodule detection sensitivity in emphysema tended to be higher for AI than the human reader, but there were no significant differences for either the emphysema (p = 0.310) or the non-emphysema group (p = 1.000).” should instead read: “Also, the nodule detection sensitivity in emphysema tended to be higher for AI than the human reader, but no significant differences were found between the emphysema and the non-emphysema group for either AI (0.94) or human reader (p = 0.29).”