Method to estimate the basal turn length in inner ear malformation types

The mathematical equations to estimate cochlear duct length (CDL) using cochlear parameters such as basal turn diameter (A-value) and width (B-value) are currently applied for cochleae with two and a half turns of normal development. Most of the inner ear malformation (IEM) types have either  less than two and a half cochlear turns or have a cystic apex, making the current available CDL equations unsuitable for cochleae with abnormal anatomies. Therefore, this study aimed to estimate the basal turn length (BTL) from the cochlear parameters of different anatomical types, including normal anatomy; enlarged vestibular aqueduct; incomplete partition types I, II, and III; and cochlear hypoplasia. The lateral wall was manually tracked for 360° of the angular depth, along with the A and B values in the oblique coronal view for all anatomical types. A strong positive linear correlation was observed between BTL and the A- (r(2) = 0.74) and B-values (r(2) = 0.84). The multiple linear regression model to predict the BTL from the A-and B-values resulted in the following equation (estimated BTL = [A × 1.04] + [B × 1.89] − 0.92). The manually measured and estimated BTL differed by 1.12%. The proposed equation could be beneficial in adequately selecting an electrode that covers the basal turn in deformed cochleae

Cochlear Implantation: The Volumetric Measurement of Vestibular Aqueduct and Gusher Prediction

This study aimed to validate the role of 3D segmentation in measuring the volume of the vestibular aqueduct (VAD), and the inner ear, and to study the correlation between VAD volume and VAD linear measurements at the midpoint and operculum. The correlation with other cochlear metrics was also studied. We retrospectively recruited 21 children (42 ears) diagnosed with Mondini dysplasia (MD) plus enlarged vestibular aqueduct (EVA) from 2009 to 2021 and who underwent cochlear implantation (CI). Patients’ sociodemographic data were collected, and linear cochlear metrics were measured using Otoplan. Vestibular aqueduct width and vestibular aqueduct and inner ear volumes were measured by two independent neuro-otologists using 3D segmentation software (version 4.11.20210226) and high-resolution CT. We also conducted a regression analysis to determine the association between these variables and CT VAD and inner ear volumes. Among the 33 cochlear implanted ears, 13 ears had a gusher (39.4%). Regarding CT inner ear volume, we found that gender, age, A-value, and VAD at the operculum were statistically significant (p-Value = 0.003, <0.001, 0.031, and 0.027, respectively) by regression analysis. Moreover, we found that Age, H value, VAD at the midpoint, and VAD at the operculum were significant predictors of CT VAD volume (p-Value < 0.04). Finally, gender (OR: 0.092; 95%CI: 0.009–0.982; p-Value = 0.048) and VAD at the midpoint (OR: 0.106; 95%CI: 0.015–0.735; p-Value = 0.023) were significant predictors of gusher risk. Patients’ gusher risk was significantly differentiated by gender and VAD width at the midpoint

A novel radiological software prototype for automatically detecting the inner ear and classifying normal from malformed anatomy.

BACKGROUND To develop an effective radiological software prototype that could read Digital Imaging and Communications in Medicine (DICOM) files, crop the inner ear automatically based on head computed tomography (CT), and classify normal and inner ear malformation (IEM). METHODS A retrospective analysis was conducted on 2053 patients from 3 hospitals. We extracted 1200 inner ear CTs for importing, cropping, and training, testing, and validating an artificial intelligence (AI) model. Automated cropping algorithms based on CTs were developed to precisely isolate the inner ear volume. Additionally, a simple graphical user interface (GUI) was implemented for user interaction. Using cropped CTs as input, a deep learning convolutional neural network (DL CNN) with 5-fold cross-validation was used to classify inner ear anatomy as normal or abnormal. Five specific IEM types (cochlear hypoplasia, ossification, incomplete partition types I and III, and common cavity) were included, with data equally distributed between classes. Both the cropping tool and the AI model were extensively validated. RESULTS The newly developed DICOM viewer/software successfully achieved its objectives: reading CT files, automatically cropping inner ear volumes, and classifying them as normal or malformed. The cropping tool demonstrated an average accuracy of 92.25%. The DL CNN model achieved an area under the curve (AUC) of 0.86 (95% confidence interval: 0.81-0.91). Performance metrics for the AI model were: accuracy (0.812), precision (0.791), recall (0.8), and F1-score (0.766). CONCLUSION This study successfully developed and validated a fully automated workflow for classifying normal versus abnormal inner ear anatomy using a combination of advanced image processing and deep learning techniques. The tool exhibited good diagnostic accuracy, suggesting its potential application in risk stratification. However, it is crucial to emphasize the need for supervision by qualified medical professionals when utilizing this tool for clinical decision-making

Machine Learning and Cochlear Implantation: Predicting the Post-Operative Electrode Impedances

Cochlear implantation is the common treatment for severe to profound sensorineural hearing loss if there is no benefit from hearing aids. Measuring the electrode impedance along the electrode array at different time points after surgery is crucial in verifying the electrodes’ status, determining the compliance levels, and helping to identify the electric dynamic range. Increased impedance values without proper reprogramming can affect the patient’s performance. The prediction of acceptable levels of electrode impedance at different time points after the surgery could help clinicians during the fitting sessions through a comparison of the predicted with the measured levels. Accordingly, clinicians can decide if the measured levels are within the predicted normal range or not. In this work, we used a dataset of 80 pediatric patients who had received cochlear implants with the MED-EL FLEX 28 electrode array. We predicted the impedance of the electrode arrays in each channel at different time points: at one month, three months, six months, and one year after the date of surgery. We used different machine learning algorithms such as linear regression, Bayesian linear regression, decision forest regression, boosted decision tree regression, and neural networks. The used features include the patient’s age and the intra-operative electrode impedance at different electrodes. Our results indicated that the best algorithm varies depending on the channel, while the Bayesian linear regression and neural networks provide the best results for 75% of the channels. Furthermore, the accuracy level ranges between 83% and 100% in half of the channels one year after the surgery, when an error range between 0 and 3 KΩ is defined as an acceptable threshold. Moreover, the use of the patient’s age alone can provide the best prediction results for 50% of the channels at six months or one year after surgery. This reflects that the patient’s age could be a predictor of the electrode impedance after the surgery

Can Electrocochleography Help Preserve Hearing After Cochlear Implantation With Full Electrode Insertion?

Objectives To evaluate the utility of intracochlear electrocochleography (ECochG) monitoring during cochlear implant (CI) surgery on postoperative hearing preservation. Study Design Prospective, randomized clinical trial. Setting Ten high-volume, tertiary care CI centers. Patients Adult patients with sensorineural hearing loss meeting the CI criteria who selected an Advanced Bionics CI. Methods Patients were randomized to CI surgery either with audible ECochG monitoring available to the surgeon during electrode insertion or without ECochG monitoring. Hearing preservation was determined by comparing preoperative unaided low-frequency (125-, 250-, and 500-Hz) pure-tone average (LF-PTA) to postoperative LF-PTA at CI activation. Pre- and post-CI computed tomography was used to determine electrode scalar location and electrode translocation. Results Eighty-five adult CI candidates were enrolled. The mean (standard deviation [SD]) unaided preoperative LF-PTA across the sample was 54 (17) dB HL. For the whole sample, hearing preservation was "good" (i.e., LF-PTA change 0-15 dB) in 34.5%, "fair" (i.e., LF-PTA change >15-29 dB) in 22.5%, and "poor" (i.e., LF-PTA change >= 30 dB) in 43%. For patients randomized to ECochG "on," mean (SD) LF-PTA change was 27 (20) dB compared with 27 (23) dB for patients randomized to ECochG "off" (p = 0.89). Seven percent of patients, all of whom were randomized to ECochG off, showed electrode translocation from the scala tympani into the scala vestibuli. Conclusions Although intracochlear ECochG during CI surgery has important prognostic utility, our data did not show significantly better hearing preservation in patients randomized to ECochG "on" compared with ECochG "off.