Classification of high riding jugular bulb using a pre-trained convolutional neural network

Objectives: High-riding jugular bulb (HRJB) is an anatomical variation in the petrous temporal bone (PTB) that can be defined as the presence of the jugular bulb at the level of the basal turn of the cochlea in the axial plane. The presence of HRJB can increase the risk of injury during middle ear surgery and may contribute to the pathogenesis of conductive and sensorineural hearing loss. This study investigated the accuracy of a deep learning convolutional neural network (CNN) algorithm in identifying HRJB on axial PTB computed tomography (CT) scans. Methods: Petrous bone CT scans were retrospectively obtained from consecutive patients imaged in January 2024 from an Australian tertiary hospital. Two blinded investigators – a board-certified otolaryngologist and an otolaryngology resident – labelled the images as either HRJB or normal. Training and test sets were created in a 2:1 ratio. Microsoft Azure's Custom Vision platform was utilised to devise the deep learning algorithm. Results: 2400 images were collected from left, right and flipped axial PTB CT scans of 600 patients. After exclusions, 2367 final images were used. The CNN achieved an overall accuracy of 0.948 (95% CI 0.930–0.962), with a sensitivity of 92.9% and specificity of 95.5% for HRJB identification. Conclusion: The CNN successfully identified HRJB on axial PTB CT scan images with a high degree of accuracy. This approach's robustness is premised on accurate labelling of datasets and rigorous cross-validation with a dedicated testing set. Future studies could explore CNNs for detecting other anatomical variations, potentially enhancing diagnostic accuracy and improving patient outcomes in Otolaryngology.