Automated Detection and Classification of Pediatric Middle Ear Diseases from CT using Entropy Projection and Feature Interaction

Existing methods for diagnosing middle ear diseases using temporal bone computed tomography (CT) imaging primarily focus on adult datasets and require labor-intensive manual input from radiologists to label and select regions of interest (ROIs). These methods rely on prior knowledge and introduce inter- and intra-observer variability. Additionally, the selected ROIs typically consist of a few 2D slices, underutilizing the 3D capabilities of CT imaging. Moreover, these methods are not reproducible on pediatric datasets, where rapidly developing heads exhibit greater morphological variability. To address these challenges, we developed a fully automated framework for precise diagnosis of pediatric chronic suppurative otitis media (CSOM) and cholesteatoma (MEC) using temporal bone CT imaging. Our method automatically detects the most informative 3D ROIs by calculating entropy changes in the 3D anatomical structures of the middle ear, eliminating the need for annotations or prior knowledge. We also introduce a multi-scale classification network that incorporates a global-local feature interaction strategy and uses a wide and deep multi-layer transformer for feature extraction, effectively learning feature dependencies. Experimental results on a dataset of CT images from 603 pediatric patients show that our method achieves a classification accuracy of 93.17% and an AUC-ROC of 96.84%, outperforming state-of-the-art methods. This innovative approach reduces radiologists’ workload, aids automated surgical navigation, and has the potential to transform diagnostic workflows in otolaryngology.