From manual to artificial intelligence fitting: Two cochlear implant case studies

Wathour, Justine; Govaerts, Paul; Deggouj, Naïma

doi:10.1080/14670100.2019.1667574

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…based technology, and it is gradually proving its worth. Indeed, results comparable to those obtained with manual programming have been found in patients initially programmed with FOX (Battmer et al 2015; Wathour et al 2016; Meeuws et al 2017; Zwolan et al 2020) as well as in experienced patients with CI (Buechner et al 2015; Waltzman and Kelsall 2020; Wathour et al 2019). Zwolan et al (2020) reported equivalent results for speech comprehension in quiet and noisy conditions between their new patients initially programmed with FOX and their historical data of manually programmed patients with CI, as also stated by Buchman et al (2020).…”

Section: Discussionsupporting

confidence: 65%

“…Waltzman and Kelsall (2020) reported that speech performance outcomes in 55 experienced CI users after 1 mo of use of the FOX2G map were equivalent compared with their clinician-created maps, and the majority of patients (82%) preferred the new FOX map. Complementary, improved hearing outcomes with FOX2G maps were found in two cases (Wathour et al 2019) and in a larger group of experienced CI recipients with poor to moderate performance (Wathour et al 2021). Furthermore, another recent study showed equivalent hearing scores in quiet and noise in 31 newly fitted patients with FOX compared with their former patients with CI532 (Zwolan et al 2020).…”

Section: Introductionmentioning

confidence: 85%

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

et al. 2022

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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.

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Section: Discussionsupporting

confidence: 65%

Section: Introductionmentioning

confidence: 85%

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

et al. 2022

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“…One of the key components for the development of artificial intelligence-based algorithms is the availability and volume of high-quality data inputs. High-performing deep machine learning algorithms require high-quality data to learn which data set variables are most important for maximizing algorithm accuracy and minimizing errors ( Vaerenberg et al, 2011 ; Wang, 2017 ; Wathour et al, 2020 ). Training the deep machine learning algorithm will be effective as the target and conflicting ranges are the individualized “right” answer for each consonant to the algorithm.…”

Section: Experiments 2: Measure Consonant Enhancementmentioning

confidence: 99%

Effect of the Target and Conflicting Frequency and Time Ranges on Consonant Enhancement in Normal-Hearing Listeners

Yoon

2021

Front. Psychol.

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In this paper, the effects of intensifying useful frequency and time regions (target frequency and time ranges) and the removal of detrimental frequency and time regions (conflicting frequency and time ranges) for consonant enhancement were determined. Thirteen normal-hearing (NH) listeners participated in two experiments. In the first experiment, the target and conflicting frequency and time ranges for each consonant were identified under a quiet, dichotic listening condition by analyzing consonant confusion matrices. The target frequency range was defined as the frequency range that provided the highest performance and was decreased 40% from the peak performance from both high-pass filtering (HPF) and low-pass filtering (LPF) schemes. The conflicting frequency range was defined as the frequency range that yielded the peak errors of the most confused consonants and was 20% less than the peak error from both filtering schemes. The target time range was defined as a consonant segment that provided the highest performance and was decreased 40% from that peak performance when the duration of the consonant was systematically truncated from the onset. The conflicting time ranges were defined on the coincided target time range because, if they temporarily coincide, the conflicting frequency ranges would be the most detrimental factor affecting the target frequency ranges. In the second experiment, consonant recognition was binaurally measured in noise under three signal processing conditions: unprocessed, intensified target ranges by a 6-dB gain (target), and combined intensified target and removed conflicting ranges (target-conflicting). The results showed that consonant recognition improved significantly with the target condition but greatly deteriorated with a target-conflicting condition. The target condition helped transmit voicing and manner cues while the target-conflicting condition limited the transmission of these cues. Confusion analyses showed that the effect of the signal processing on consonant improvement was consonant-specific: the unprocessed condition was the best for /da, pa, ma, sa/; the target condition was the best for /ga, fa, va, za, ʒa/; and the target-conflicting condition was the best for /na, ʃa/. Perception of /ba, ta, ka/ was independent of the signal processing. The results suggest that enhancing the target ranges is an efficient way to improve consonant recognition while the removal of conflicting ranges negatively impacts consonant recognition.

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“…Consonant-specific and listener-specific datasets are also necessary to train a neural network-based deep machine learning algorithm which is currently in progress in our laboratory. Training the deep machine learning algorithm will be effective with our consonant-by-consonant datasets for maximizing algorithm accuracy and minimizing errors ( Vaerenberg et al, 2011 ; Wang, 2017 ; Wathour et al, 2020 ). Hence, the present study findings will aid in designing custom bimodal frequency maps for greater consonant intelligibility based on residual hearing available in the hearing aid ear.…”

Section: Discussionmentioning

confidence: 99%

Dichotic spectral integration range for consonant recognition in listeners with normal hearing

Yoon

Morgan²

2022

Front. Psychol.

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Dichotic spectral integration range, or DSIR, was measured for consonant recognition with normal-hearing listeners. DSIR is defined as a frequency range needed from 0 to 8,000 Hz band in one ear for consonant recognition when low-frequency information of the same consonant was presented to the opposite ear. DSIR was measured under the three signal processing conditions: (1) unprocessed, (2) target: intensified target spectro-temporal regions by 6 dB responsible for consonant recognition, and (3) target minus conflicting: intensified target regions minus spectro-temporal regions that increase confusion. Each consonant was low-pass filtered with a cutoff frequency of 250, 500, 750, and 1,000 Hz, and then was presented in the left ear or low-frequency (LF) ear. To create dichotic listening, the same consonant was simultaneously presented to the right ear or high-frequency (HF) ear. This was high-pass filtered with an initial cutoff frequency of 7,000 Hz, which was adjusted using an adaptive procedure to find the maximum high-pass cutoff for 99.99% correct consonant recognition. Mean DSIRs spanned from 3,198–8,000 Hz to 4,668–8,000 Hz (i.e., mid-to-high frequencies were unnecessary), depending on low-frequency information in the LF ear. DSIRs narrowed (i.e., required less frequency information) with increasing low-frequency information in the LF ear. However, the mean DSIRs were not significantly affected by the signal processing except at the low-pass cutoff frequency of 250 Hz. The individual consonant analyses revealed that /ta/, /da/, /sa/, and /za/ required the smallest DSIR, while /ka/, /ga/, /fa/, and /va/ required the largest DSIRs. DSIRs also narrowed with increasing low-frequency information for the two signal processing conditions except for 250 vs. 1,000 Hz under the target-conflicting condition. The results suggest that consonant recognition is possible with large amounts of spectral information missing if complementary spectral information is integrated across ears. DSIR is consonant-specific and relatively consistent, regardless of signal processing. The results will help determine the minimum spectral range needed in one ear for consonant recognition if limited low spectral information is available in the opposite ear.

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From manual to artificial intelligence fitting: Two cochlear implant case studies

Cited by 13 publications

References 19 publications

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

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

Effect of the Target and Conflicting Frequency and Time Ranges on Consonant Enhancement in Normal-Hearing Listeners

Dichotic spectral integration range for consonant recognition in listeners with normal hearing

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