To investigate the imaging characteristics, surgical approaches, and outcomes of cochlear implantation (CI) in patients with special inner ear malformations (IEMs) that show transitional forms between cochlear hypoplasia (CH) and common cavity (CC).

Twelve children (eight males, four females), aged 10 to 43 months, with special IEMs were enrolled, and their inner ear structures were analyzed using detailed segmentation. Two surgical approaches were employed: the transmastoid slot labyrinthotomy approach (TSLA) for cases requiring customized electrodes, and the round window or cochleostomy approach for the remaining cases. Outcomes were evaluated using Categories of Auditory Performance (CAP), Speech Intelligibility Rating (SIR), and Meaningful Auditory Integration Scale (MAIS/IT-MAIS) at 12 months post-implantation.

Two main types of malformed cochleae were identified: common cavity-like and primitive CH types. All patients exhibited cochlear nerve deficiency and significant bilateral differences in their inner ear structures. Four patients underwent TSLA with customized electrodes, while the remaining patients received lateral wall electrodes via the round window or cochleostomy approach. Most patients showed improvement in auditory and speech capabilities following implantation.

Inner ear malformations with transitional forms between CH and CC present unique challenges, requiring detailed preoperative evaluation and customized surgical plans. Even in severe cases, carefully planned surgery can lead to meaningful auditory rehabilitation.

The aims of this study were to investigate the clinical applicability of 3D segmentation in measuring cochlear anatomical parameters, explore factors that influence the insertion angle of cochlear implant electrodes in patients with inner ear malformations, and determine the value of 3D segmentation in predicting cochlear implant electrode insertion depth by simulating electrode implantation in a reconstructed 3D model.

Data from 208 temporal bone CT scans of patients with a variety of inner ear malformations (including the CH, IP-I, IP-II, and IP-III types) who underwent cochlear implantation at our center were retrospectively analyzed. Preoperative temporal bone CT data were subjected to three-dimensional (3D) segmentation of the cochlea with a 3D slicer.

Cochlear malformation types, including IP types I (42 ears), II (278 ears), III (20 ears), and CH (65 ears), were diagnosed and measured in 208 preoperative CT datasets. Cochlear anatomical parameters and electrode length were correlated, which partially explained the variations in electrode insertion angle. The mean angle of implantation among the enrolled patients was 564.33°, and the mean implantation angle prediction error in the 3D segmentation was |23.74|°.

Three-dimensional segmentation from temporal bone CT is valuable for surgeons, especially in treating patients with inner ear malformation. Such insights will help surgeons understand overall anatomical variations, predict electrode implantation depth, and complete preoperative imaging assessments for cochlear implant insertion depth in patients with inner ear malformations.

The causal association between presbycusis and changes in cerebral cortex structure was evaluated through Mendelian randomization (MR).

Presbycusis data, serving as the exposure trait, was analyzed using data from the ninth release of the FinnGen biobank. Genome-wide association study (GWAS) datasets for cortical surface area (SA) and thickness (TH) were sourced from the ENIGMA Consortium, whereas cortical volume (V) GWAS data came from the UK Biobank. The inverse-variance weighted (IVW) approach was adopted as the principal analytical method. To ensure robustness, sensitivity analyses were systematically implemented to evaluate potential heterogeneity and pleiotropic effects.

Following rigorous filtering through IVW and sensitivity analyses, eleven robust MR associations emerged, offering preliminary evidence for a causal link between presbycusis and cortical architecture, including frontal pole SA with global weighted (GW) (β=1.858 mm², P=0.004) and without GW (β=1.778 mm², P=0.012), pars orbitalis SA with GW (β=2.717 mm², P=0.042), transverse temporal SA with GW (β=2.349 mm², P=0.033), pars orbitalis TH with GW (β=-0.009 mm, P=0.008) and without GW (β=-0.011 mm, P=0.008), rostral middle frontal TH with GW (β=-0.005 mm, P=0.020) and without GW (β=-0.007 mm, P=0.012), precuneus TH without GW (β=-0.006 mm, P=0.049), left hippocampus V (β=-12.296 mm³, P=0.033) and right hippocampus V (β=-11.991 mm³, P=0.049).

From a genetic standpoint, our findings indicate region-specific neuroanatomical modifications in presbycusis patients, supporting the notion of neurodegenerative or adaptive alterations in brain structure from a genetic perspective.

To assess the effectiveness of machine learning in automating the prediction of vestibular abnormalities after cochlear implantation (CI) in patients with sensorineural hearing loss (SNHL), with the goal of developing a practical model that can accurately predict long-term vestibular function outcomes and identify associated risk factors.

Clinical data, including imaging, vestibular evoked myogenic potentials (VEMPs), and auditory information, were collected from patients with sensorineural hearing loss (SNHL) before and after CI. The decision tree algorithm was employed to address missing values and screen pre-CI clinical features. Six machine learning methods were subsequently utilized to predict the relationships between the extracted features and post-CI vestibular dysfunction. The best-performing method determined the ranking of feature importance, which was regarded as risk factors for predicting symptoms and VEMPs results after CI.

Logistic regression models effectively predicted both post-CI vestibular dysfunction and abnormal cervical VEMP (cVEMP), with accuracies of 80% and 78%, respectively. The relative importance of the features, in descending order, was as follows: cVEMP latency, cVEMP amplitude, and residual hearing threshold. Moreover, the support vector machine (SVM) model attained an accuracy of 88% in predicting abnormal ocular VEMP (oVEMP) post-CI. For the SVM model, the feature importance ranking was as follows: oVEMP latency, oVEMP amplitude, and residual hearing threshold.

This study successfully leverages machine learning techniques, specifically support vector machines (SVM) and logistic regression models, to predict the impact of CI on vestibular function. These predictive models provide valuable insights for presurgical planning and decision-making in CI procedures. Moreover, the findings highlight the critical risk factors associated with vestibular dysfunction, offering a robust reference for guiding vestibular rehabilitation strategies.