Correction: Diagnostic accuracy of deep learning vs. human raters for detecting osteoporotic vertebral compression fractures in routine CT scans

Le résumé fourni par la source

The authors regret to inform that an error occurred in the statistical analysis presented in Tables 1-4 of the original article.Due to a default setting in the statistical software (R), the negative class (no fracture) was incorrectly assigned as the 'positive class' for the calculation of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).Specifically, the values labeled as 'Sensitivity' in the original tables represent the 'Specificity' (detection of non-fractured vertebrae), and vice versa.Similarly, PPV and NPV values were transposed.This systematic labeling error affects all sub-analyses (in Tables 1234, as well as the corresponding Figures 3, 5, and 6, and the descriptions in the "Results" and the first paragraph of the "Discussion" sections).The authors emphasize that the Kappa, Accuracy and AUROC values, as well as the overall ranking of the models and human raters, remain unaffected by this labeling error.The core conclusion-that specifically trained deep learning models achieve diagnostic performance comparable to or exceeding that of human experts-remains valid.To correct this, the labels "Spec" & "Sens" and "NPV" & "PPV" were transposed in the graphical abstract, in Figures 3, 5 and 6, and in the first column of tables 1, 2, 3 and 4. Furthermore, the following changes were made to the text: Diagnostic performance at the vertebral levelIn the first paragraph, a sentence incorrectly stated "SpineQ and DL models were more sensitive, and human raters were more specific.PPV was comparably high for all groups

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