The Use of Artificial Intelligence to Program Cochlear Implants

Artificial Intelligence in Otology and Neurotology

Clinical applications of artificial intelligence (AI) have grown exponentially with increasing computational power and Big Data. Data rich fields such as Otology and Neurotology are still in the infancy of harnessing the power of AI but are increasingly involved in training and developing ways to incorporate AI into patient care. Current studies involving AI are focused on accessible datasets; health care wearables, tabular data from electronic medical records, electrophysiologic measurements, imaging, and "omics" provide huge amounts of data to utilize. Health care wearables, such as hearing aids and cochlear implants, are a ripe environment for AI implementation.

Breaking Sound Barriers: Exploring Tele-Audiology's Impact on Hearing Healthcare

Hearing impairment is a global issue, affecting billions of people; however, there is a gap between the population affected by hearing loss and those able to access hearing healthcare. Tele-audiology, the application of telemedicine in audiology, serves as a new form of technology which aims to provide synchronous or asynchronous hearing healthcare. In this article, we reviewed some recent studies of tele-audiology-related topics to have a glimpse of the current development, associated challenges, and future advancement. Through the utilization of tele-audiology, patients can conveniently access hearing healthcare, and thus save travel costs and time. Recent studies indicate that remote hearing screening and intervention are non-inferior to the performance of traditional clinical pathways. However, despite its potential benefits, the implementation of tele-audiology faces numerous challenges, and audiologists have varying attitudes on this technology. Overcoming obstacles such as high infrastructure costs, limited reimbursement, and the lack of quality standards calls for concerted efforts to develop effective strategies. Ethical concerns, reimbursement, and patient privacy are all crucial aspects requiring in-depth discussion. Enhancing the education and training of students and healthcare workers, along with providing relevant resources, will contribute to a more efficient, systematic hearing healthcare. Future research will aim to develop integrated models with evidence-based protocols and incorporating AI to enhance the affordability and accessibility of hearing healthcare.

Enhancing Postoperative Cochlear Implant Care With ChatGPT-4: A Study on Artificial Intelligence (AI)-Assisted Patient Education and Support

BACKGROUND

Cochlear implantation is a critical surgical intervention for patients with severe hearing loss. Postoperative care is essential for successful rehabilitation, yet access to timely medical advice can be challenging, especially in remote or resource-limited settings. Integrating advanced artificial intelligence (AI) tools like Chat Generative Pre-trained Transformer (ChatGPT)-4 in post-surgical care could bridge the patient education and support gap.

AIM

This study aimed to assess the effectiveness of ChatGPT-4 as a supplementary information resource for postoperative cochlear implant patients. The focus was on evaluating the AI chatbot's ability to provide accurate, clear, and relevant information, particularly in scenarios where access to healthcare professionals is limited.

MATERIALS AND METHODS

Five common postoperative questions related to cochlear implant care were posed to ChatGPT-4. The AI chatbot's responses were analyzed for accuracy, response time, clarity, and relevance. The aim was to determine whether ChatGPT-4 could serve as a reliable source of information for patients in need, especially if the patients could not reach out to the hospital or the specialists at that moment.

RESULTS

ChatGPT-4 provided responses aligned with current medical guidelines, demonstrating accuracy and relevance. The AI chatbot responded to each query within seconds, indicating its potential as a timely resource. Additionally, the responses were clear and understandable, making complex medical information accessible to non-medical audiences. These findings suggest that ChatGPT-4 could effectively supplement traditional patient education, providing valuable support in postoperative care.

CONCLUSION

The study concluded that ChatGPT-4 has significant potential as a supportive tool for cochlear implant patients post surgery. While it cannot replace professional medical advice, ChatGPT-4 can provide immediate, accessible, and understandable information, which is particularly beneficial in special moments. This underscores the utility of AI in enhancing patient care and supporting cochlear implantation.

Transhumanism: Integrating Cochlear Implants With Artificial Intelligence and the Brain-Machine Interface

The integration of cochlear implants (CI) with brain-machine interfaces (BMIs) and artificial intelligence (AI) within the framework of transhumanism is revolutionary and this editorial highlights how this synergy can transcend human sensory experiences and auditory rehabilitation. The potential of this amalgamation extends beyond restoring auditory function to enhancing human capabilities, marking a transformative step towards a future where technology harmoniously extends human faculties.

Effects of an evidence-based model for cochlear implant aftercare delivery on clinical efficiency and patient outcomes

OBJECTIVES

To assess an evidence-based model (EBM) approach to cochlear implant (CI) aftercare that includes a modified, reduced treatment schedule for newly-implanted adult CI recipients consisting of four appointments (initial activation, 1-, 3- and 6- months postactivation) in the first year post-surgery.

METHOD

This prospective multicenter proof-of-concept study was conducted across three clinics in the United States by five experienced CI clinicians. Seventeen newly-implanted adult patients with postlingual hearing loss enrolled in the study. Hearing outcomes were measured using objective speech testing and subjective self-report measures.

RESULTS

Most recipients (14/17; 82%) were able to follow the four-appointment EBM schedule. The reduced number of visits translated into an average time savings of 3 hours per patient. Significant improvements in speech perception were observed at both 3- and 6-months postactivation, as measured by CNC words in quiet and AzBio sentences at +10 dB SNR, consistent with published results achieved by traditional practices. Recipients were significantly satisfied with telephone, music, small group conversation, and television listening at 6 months postactivation. Recipient satisfaction with overall service was rated as "excellent" by 14/14 (100%) respondents.

CONCLUSION

The four-appointment EBM approach delivered efficient and effective audiological aftercare to CI recipients in the first year following CI implantation.

Effect of a CI Programming Fitting Tool with Artificial Intelligence in Experienced Cochlear Implant Patients

OBJECTIVE

Cochlear implants (CIs) are the treatment of choice for patients with severe to profound hearing loss. The hearing results, however, considerably vary across patients. This may partly be due to variability in the CI fitting. We investigated the effect of FOX, a software tool to program CIs using artificial intelligence (AI), on hearing outcomes.

METHODS

Forty-seven experienced CI patients who came to our tertiary CI center for their annual follow-up between 2017 and 2020 were recruited for this study. They received a new CI map created by the AI software tool. CI parameters and auditory outcomes obtained with this new map were compared with those of the initial manual map after 15 days of take-home experience. Within-patient differences were assessed. At the end of the study, the patients were offered a choice to continue using the AI map or to revert to their old manual map.

RESULTS

Several auditory outcomes improved with the AI map, namely, pure tone audiometric threshold at 6,000 Hz (median improvement 10 dB, range = -20 to 50 dB, Z = -2.608, p = 0.008), phonemic discrimination scores (median improvement 10%, range = 0% to 30%, Z = -4.061, p = 0.001), and soft-intensity (median improvement of 10%, range = -20% to 90%, Z = -4.412, p < 0.001) to normal-intensity (median improvement of 10%, range = -30% to 60%, Z = -3.35, p < 0.001) speech audiometric scores.

CONCLUSION

The AI-assisted CI mapping model as a potential assistive tool may improve audiological outcomes for experienced CI patients, including high-frequency pure tone audiometry and audiometric speech scores at low and normal presentation levels.Clinical trial registration: NCT03700268.

Prospective Comparison Between Manual and Computer-Assisted (FOX) Cochlear Implant Fitting in Newly Implanted Patients

OBJECTIVE

A prospective, longitudinal, randomized controlled trial with an original crossover design for 1 year was conducted to compare manual fitting to artificial intelligence-based fitting in newly implanted patients.

DESIGN

Twenty-four patients who received their first cochlear implant (CI) were randomly assigned to the manual or Fitting to Outcome eXpert (FOX) arm; they followed the corresponding fitting procedures for 1 year. After 1 year, each patient was switched to another arm. The number of fittings, auditory outcomes (pure-tone thresholds, loudness scaling curves, spectral discrimination scores, bisyllabic word recognition in quiet and noise, and speech tracking), fitting session questionnaire, and CI parameters (T level, C level, Threshold Sound Pressure Level (T-SPL), Comfortable Sound Pressure Level (C-SPL), and loudness growth value) were compared between the two groups. Differences between the two groups were analyzed using the Mann-Whitney test, and Holm corrections were applied for multiple statistical tests. At the end of the crossover session, patients were offered the choice to continue with their old or new map.

RESULTS

As early as 3 mo postactivation, the FOX group showed less variability and significantly better speech intelligibility in quiet conditions at 40 and 55 dB SPL and noise ( p < 0.05) with median phoneme scores of 50%, 70%, and 50% at 55, 70, and 85 dB SPL compared with 45%, 50%, and 40%, respectively. This group showed better results at 12 mo postactivation ( p < 0.05). In the manual group, 100% of the patients decided to keep the new FOX map, and 82% performed better with the FOX map. In the FOX group, 63% of the patients decided to keep the manual map, although the measurable outcome had not improved. In this group, participants reported to prefer the manual map because it felt more comfortable, even if the FOX map gave better measured outcome.

CONCLUSION

Although the study size remains relatively small, the AI-FOX approach was equivalent to or even outperformed the manual approach in hearing performance, comfort, and resources. Furthermore, FOX is a tool capable of continuous improvement by comparing its predictions with observed results and is continuously learning from clinicians' practice, which is why this technology promises major advances in the future.

Recent advancements in bioelectronic devices to interface with the peripheral vestibular system

Balance disorders affect approximately 30% of the population throughout their lives and result in debilitating symptoms, such as spontaneous vertigo, nystagmus, and oscillopsia. The main cause of balance disorders is peripheral vestibular dysfunction, which may occur as a result of hair cell loss, neural dysfunction, or mechanical (and morphological) abnormality. The most common cause of vestibular dysfunction is arguably vestibular hair cell damage, which can result from an array of factors, such as ototoxicity, trauma, genetics, and ageing. One promising therapy is the vestibular prosthesis, which leverages the success of the cochlear implant, and endeavours to electrically integrate the primary vestibular afferents with the vestibular scene. Other translational approaches of interest include stem cell regeneration and gene therapies, which aim to restore or modify inner ear receptor function. However, both of these techniques are in their infancy and are currently undergoing further characterization and development in the laboratory, using animal models. Another promising translational avenue to treating vestibular hair cell dysfunction is the potential development of artificial biocompatible hair cell sensors, aiming to replicate functional hair cells and generate synthetic 'receptor potentials' for sensory coding of vestibular stimuli to the brain. Recently, artificial hair cell sensors have demonstrated significant promise, with improvements in their output, such as sensitivity and frequency selectivity. This article reviews the history and current state of bioelectronic devices to interface with the labyrinth, spanning the vestibular implant and artificial hair cell sensors.

Analysis of the Influence of Network Continuous Care on the Quality of Life of Patients with Coronary Artery Disease (CAD) after PIC

Studies have shown that most patients after PCI cannot adhere to the cardiac rehabilitation program. The survey found that due to the lack of secondary prevention of cardiac rehabilitation, the phenomenon of drug reduction and withdrawal after PCI is very common after discharge, leading to recurrence of the patient's disease or worsen and repeated hospitalizations, so continuity of care is very important. In this paper, in addition to proposing a network care continuum with artificial intelligence handler in order to improve the healthcare system and provide new ideas for improving the postoperative recovery of CHD patients, we analyze the impact of PCI on the coronary heart disease patients' PCI postoperative quality of life. In the method part, this article introduces the concepts of continuation care and PCI after surgery, introduces the marker delivery algorithm in the field of artificial intelligence, and introduces the SF-36 scale for patient quality of life analysis. This article designs an experiment combining artificial intelligence processors to carry out network continuity care for patients and divides 100 eligible patients into an experimental group and a control group. In the analysis part, the two groups of patients were analyzed in terms of general data comparison, physical function, biochemical indicators, quality of life, and dependence. It can be seen from the experimental analysis that the anxiety and depression of the two groups of subjects have different degrees of decline. The HAMA value of the experimental group is 9.06 ± 0.77, and the HAMD value is 9.18 ± 1.20, which is significantly lower than that of the control group, P < 0.05. It can be seen that the use of network continuation care can reduce the negative emotions of patients more than general care. Through psychological counseling and postoperative follow-up, it can improve the optimism and positive emotions of the patients, reduce the negative emotions of the patients, and improve it to a certain extent.

Algorithm-Based Hearing and Speech Therapy Rehabilitation after Cochlear Implantation

INTRODUCTION

Due to the changes in the indication range for cochlear implants and the demographic development towards an aging society, more and more people are in receipt of cochlear implants. An implantation requires a close-meshed audiological and logopedic aftercare. Hearing therapy rehabilitation currently requires great personnel effort and is time consuming. Hearing and speech therapy rehabilitation can be supported by digital hearing training programs. However, the apps currently on the market are to a limited degree personalized and structured. Increasing digitalization makes it possible, especially in times of pandemics, to decouple hearing therapy treatment from everyday clinical practice.

MATERIAL AND METHODS

For this purpose, an app is in development that provides hearing therapy tailored to the patient. The individual factors that influence hearing outcome are considered. Using intelligent algorithms, the app determines the selection of exercises, the level of difficulty and the speed at which the difficulty is increased.

RESULTS

The app works autonomously without being connected to local speech therapists. In addition, the app is able to analyze patient difficulties within the exercises and provides conclusions about the need for technical adjustments.

CONCLUSIONS

The presented newly developed app represents a possibility to support, replace, expand and improve the classic outpatient hearing and speech therapy after CI implantation. The way the application works allows it to reach more people and provide a time- and cost-saving alternative to traditional therapy.

Investigation of an Outcomes-Driven, Computer-Assisted Approach to CI Fitting in Newly Implanted Patients

OBJECTIVE

To evaluate the efficacy of outcomes received by newly implanted cochlear implant recipients when an outcomes-driven, computer-assisted approach to cochlear implantation fitting was used. This approach, referred to as "Fitting to Outcome eXpert," or FOX, was developed by Otoconsult in Antwerp, Belgium.

DESIGN

Thirty-one newly implanted subjects participated in a nonrandomized, single-subject, repeated measures design that involved a within-subject comparison of preoperative and postoperative speech recognition scores. Sound processors for all subjects were programmed using the FOX software that utilized the evidence-based results of various psychoacoustic tests to adjust MAP parameters and improve performance. Additionally, mean word and sentence recognition scores obtained by the subjects programmed with FOX were compared to results obtained by newly implanted patients enrolled in the Nucleus CI532 clinical trial whose devices were programmed using traditional methods.

RESULTS

Subjects whose sound processors were programmed using FOX obtained a mean 6-month postactivation Consonant Nucleus Consonant word score of 60.2% correct. This represented an improvement of 46% age points when mean preactivation and postactivation scores were compared and represented a statistically significant change in score (p < 0.001). This mean score is similar to the mean 6-month Consonant Nucleus Consonant Word score of 61% obtained by 96 subjects enrolled in the Nucleus CI532 trial. Additionally, subjects in this study obtained a mean 6-month postactivation AzBio Sentence score of 42.7% correct when stimuli were presented at 65 dBA using a +10 signal-to-noise ratio. This score is also similar to the mean score of 43% obtained by 96 subjects enrolled in the Nucleus CI532 trial using the same test material and signal-to-noise ratio. Patients enrolled in this study attended 43% fewer programming appointments than the number reported by cochlear implantation centers in recent surveys of clinical care.

CONCLUSION

The results of this study support the use of an outcomes-driven, computer-assisted approach to supplement the management of newly implanted cochlear implant recipients. Subjects whose devices were programmed using such an approach demonstrated mean postoperative word in quiet and sentence in noise scores comparable to those obtained by subjects in the Nucleus CI532 clinical trial whose devices were programmed using traditional programming techniques. Use of this approach positively impacted patient care by reducing the number of postoperative visits needed to optimize sound processor programs, simplified patient testing via the use of direct streaming, and ensured that patients received consistent programming of their sound processor, regardless of the location where the programming was performed.

Cochlear implant telemedicine: Remote fitting based on psychoacoustic self-tests and artificial intelligence

Objective: This study aims to assess the feasibility of autonomous cochlear implant (CI) fitting by adult CI recipients based on psychoacoustic self-testing and artificial intelligence (AI). Design: A feasibility study was performed on six adult CI recipients implanted with a Nucleus device. Two weeks after processor activation in the clinic, a 'self-fitting' session was organized in a supervised simulated home environment. The CI recipient performed pure tone audiometry and spectral discrimination tests as self-tests. The AI application FOX analysed the results and recommended a new map. The participants filled out a questionnaire and were tested again after 2 months of take-home experience. Results: Four out of six patients performed the self-tests without any help from the audiologist and four were fitted by FOX without any manual intervention. All patients were comfortable with the concept of self-testing and automated fitting. Patients acknowledged that at this stage the remote supervision of an audiologist remains essential. Conclusions: The study showed that audiological self-assessment and remote CI fitting with AI under the supervision of an audiologist is feasible, at least in a number of CI recipients. Currently, there are still some technical and regulatory challenges to be addressed before this can become routine practice.