Our results show that many patients greatly benefited from an ABI, and most of the patients used their implants even though the hearing improvements usually consisted of awareness of surrounding sounds and improved lip-reading. No severe side effects were observed from implant stimulation.

Background: Individuals with unilateral hearing loss show poor spatial hearing, but individual variability is high. Aims/objectives: To investigate if the degree of hearing loss in unilateral ear canal atresia affects horizontal sound localization and speech recognition. Materials and methods: Twelve subjects with unilateral ear canal atresia without childhood hearing intervention. Previously published data from eight normal-hearing subjects in normal binaural as well as experimentally induced unilateral hearing loss served as a reference. Horizontal sound localization and recognition of speech in spatially separate competing speech were assessed.Results: Linear regression analysis demonstrated a relationship between sound localization accuracy (SLA) and the air conduction pure tone average of the atretic ear (r ¼ 0.85, p¼.007). The large proportion of variability in SLA (72%) explained by the degree of hearing loss of the atretic ear indicates that binaural processing is possible. SLA was worse than for normal hearing individuals (p<.0001), and comparable to moderate simulated unilateral hearing loss (p¼.13). Speech discrimination was significantly worse than normal (p<.0001) and not dependent on degree of hearing loss of the atretic ear. Conclusions and significance: Individuals with congenital unilateral ear canal atresia show impaired horizontal SLA and recognition of speech in competing speech.

IntroductionAcoustic radiation is one of the most important white matter fiber bundles of the human auditory system. However, segmenting the acoustic radiation is challenging due to its small size and proximity to several larger fiber bundles. TractSeg is a method that uses a neural network to segment some of the major fiber bundles in the brain. This study aims to train TractSeg to segment the core of acoustic radiation.MethodsWe propose a methodology to automatically extract the acoustic radiation from human connectome data, which is both of high quality and high resolution. The segmentation masks generated by TractSeg of nearby fiber bundles are used to steer the generation of valid streamlines through tractography. Only streamlines connecting the Heschl's gyrus and the medial geniculate nucleus were considered. These streamlines are then used to create masks of the core of the acoustic radiation that is used to train the neural network of TractSeg. The trained network is used to automatically segment the acoustic radiation from unseen images.ResultsThe trained neural network successfully extracted anatomically plausible masks of the core of the acoustic radiation in human connectome data. We also applied the method to a dataset of 17 patients with unilateral congenital ear canal atresia and 17 age- and gender-paired controls acquired in a clinical setting. The method was able to extract 53/68 acoustic radiation in the dataset acquired with clinical settings. In 14/68 cases, the method generated fragments of the acoustic radiation and completely failed in a single case. The performance of the method on patients and controls was similar.DiscussionIn most cases, it is possible to segment the core of the acoustic radiations even in images acquired with clinical settings in a few seconds using a pre-trained neural network.