Detection of abnormalities within the inner ear is a challenging task even for experienced clinicians. In this study, we propose an automated method for automatic abnormality detection to provide support for the diagnosis and clinical management of various otological disorders. We propose a framework for inner ear abnormality detection based on deep reinforcement learning for landmark detection which is trained uniquely in normative data.
View Article and Find Full Text PDFInt J Pediatr Otorhinolaryngol
November 2021
Objectives: This study investigated the long-term postoperative spontaneous formation of a bone bed in pediatric cochlear implant patients for whom no bone bed was drilled during the surgery.
Methods: A cross-sectional observational study of skull thickness under and on the edges of the cochlear implant receiver/stimulator in children with computed tomography (CT scan) ≥6 months after implantation was performed. In total, 37 pediatric patients from a single tertiary center underwent cochlear implantation without bone bed drilling and with screw fixation of the receiver/stimulator.