Effect of a CI Programming Fitting Tool with Artificial Intelligence in Experienced Cochlear Implant Patients. Issue 3 (18th March 2023)

• Record Type: Journal Article
• Authors: Wathour, Justine; Govaerts, Paul J.; Lacroix, Emilie; Naïma, Deggouj
• Is Part Of: Otology & neurotology. Volume 44:Issue 3(2023)
• Page Start: 209
• Page End: 215
• Publication Date: 2023-03-18
• Subjects: Artificial intelligence -- Cochlear implant -- Fitting -- Hearing tests -- Parameters; Otology -- Periodicals; Ear -- Diseases -- Periodicals; Skull base -- Surgery -- Periodicals; 617.8005
• DOI: 10.1097/MAO.0000000000003810
• Languages: English
• ISSNs: 1531-7129

Abstract

Abstract : 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