Additional file 1 of Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears

Additional file 1: Figure S1 Training curves of the 2D (A) and 3D (B) CNN models

Additional file 3 of Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears

Additional file 3: Table S1 Diagnostic performance of 2D and 3D CNN models and participated reading clinicians on surgery andinternal test sets

Additional file 5 of Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears

Additional file 5: Table S2 Diagnostic performance of 2D CNN models and reading clinicians on 1.5T and 3.0T MRIexaminations

Additional file 2 of Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears

Additional file 2: Figure S2 Different ST subtypes. (a) Histological damage diagram. (b) Representative images onMRI and arthroscopy. ST, supraspinatus tear

Additional file 4 of Development and clinical validation of deep learning for auto-diagnosis of supraspinatus tears

Additional file 4: Figure S3 2-class confusion matrices of models on test set. (A) 2-class confusionmatrices of 2D model on internal test set. (B) 2-class confusion matrices of 3D model on internaltest set. (C) 2-class confusion matrices of 2D model on surgery test set. (D) 2-class confusionmatrices of 3D model on surgery test set. CNN, convolutional neural network; ROC, receiveroperating characteristic