DL model performance
atelectasis, OME
ATELECTASIS, RETRACTION POCKET
We used threefold cross-validation for developing and testing the CNN model to detect OME referable attic retraction pocket and atelectasis. The AUC of classifying the normal pars flaccida and attic retraction pocket was 0.89, and the average accuracy, sensitivity, and specificity were 89%, 0.93, and 0.62, respectively (Fig. Receiver–operator characteristic (ROC) curves and corresponding area under the ROC curve (AUC) of the deep learning model for the detection of attic retraction pocketClass activation maps of the deep learning model for the detection attic retraction pocket. Class activation maps of the identification of attic retraction pockets. The red-colored area represents the discriminative region in the otoscopic images, whereas the blue-colored area represents the non-specific region in the otoscopic images. 