Predictive Study on Hearing Rehabilitation After Cochlear Implant

Overview

The aim of this study is to display the predictive factors of hearing rehabilitation after cochlear implant surgery in severely to profoundly deaf adults.

Cochlear implants are indicated in France in cases of severe to profound bilateral sensorineural hearing loss with an audiometric threshold of less than or equal to 50% speech discrimination in silence in the Fournier list (or equivalent) at 60 dB, in the free field, with well-fitted hearing aids. Cochlear implant represents a major advance in the management of severe to profoundly deaf patients and has also shown a benefit in the prevention of neurodegenerative diseases. An average of 1800 cochlear implants are placed in France per year, 58% of which are placed in patients over 18 years of age. The results of cochlear implants are in favour of a benefit in speech comprehension compared to hearing aids in cases of severe to profound deafness. However, there is a strong disparity in hearing performance after cochlear implantation from one patient to another, whether in silence or in noise. Several factors influencing the results of the implant have been identified. Some of them are linked to the patient: etiology of the deafness, duration of auditory deprivation, age at implantation, residual hearing, pre- or post-lingual status of the deafness, some others are related to implant surgery (insertion of the electrode in the tympanic ramp, complete insertion, presence of a translocation, depth of electrode insertion). Finally, there are factors related to the quality of the settings of the implant and to the brain plasticity of the patients.The 4 main factors seem to be the duration of the deafness, the age of onset of the deafness, its etiology and the duration of the patient's experience with the implant. It is assumed that the performance of cochlear implantation is strongly related to the individual's auditory processing abilities and the integrity of the central nervous system from the auditory nerve to the cortex. At present, it is very difficult to predict the outcome of cochlear implants in deaf patients with a cochlear implant indication prior to implantation. The results remain variable from one patient to another and, to date, both the etiology and the state of the central auditory pathways are not taken into account in the indication for cochlear implantation. Animal studies have demonstrated anterograde degenerative neural damage in cochlear deafness (presbycusis, endolymphatic hydrops) and such damage is likely to explain the functional variability observed in humans in the case of neural stimulation with cochlear implants. Multiple integration of clinical data to propose a predictive model can now be done using both supervised (Deep Learning) and unsupervised (Manifold Learning) Machine Learning techniques, including for predicting auditory recovery. It is now possible to extend machine learning models to include quantitative data from diffusion MRI with the goal of providing an objective functional parameter from the central auditory pathways, then combined with clinical parameters and genetic to obtain a predictive model of hearing recovery after cochlear implantation. This study will allow us, through the study of brain tractography, to specify the role of the central auditory pathways in the results of cochlear implantation, a role that has not been determined to date, and to evaluate their correlation with clinical and genetic in order to create a predictive model of good auditory rehabilitation in artificial intelligence. The objective is to better select patients who can benefit from a cochlear implant in order to implant them in an optimal timing and to improve indications for cochlear implant.

Conditions

Interventions

Eligibility

Locations (1)

Outcomes

Central Contacts