Predicting three-month and 12-month post-fitting real-world hearing-aid outcome using pre-fitting acceptable noise level (ANL)

Abstract

Objective Determine the extent to which pre-fitting acceptable noise level (ANL), with or without other predictors such as hearing aid experience, can predict real-world hearing aid outcomes at 3 and 12 months post-fitting. Design ANLs were measured before hearing aid fitting. Post-fitting outcome was assessed using the International Outcome Inventory for Hearing Aids (IOI-HA) and a hearing aid use questionnaire. Models that predicted outcomes (successful vs. unsuccessful) were built using logistic regression and several machine learning algorithms, and were evaluated using the cross-validation technique. Study sample 132 adults with hearing impairment. Results The prediction accuracy of the models ranged from 61% to 68% (IOI-HA) and from 55% to 61% (hearing aid use questionnaire). The models performed more poorly in predicting 12-month than 3-month outcomes. The ANL cutoff between successful and unsuccessful users was higher for experienced (~18 dB) than first-time hearing aid users (~10 dB), indicating that most experienced users will be predicted as successful users regardless of their ANLs. Conclusions Pre-fitting ANL is more useful in predicting short-term (3 months) hearing aid outcomes for first-time users, as measured by the IOI-HA. The prediction accuracy was lower than the accuracy reported by some previous research that used a cross-sectional design.

Efficacy of a Hearing Aid Noise Reduction Function

“… Nabelek, Freyaldenhoven, Tampas, Burchfiel, and Muenchen (2006) showed that unaided ANLs had an 85% accuracy rate at predicting successful hearing aid use in individuals who had worn hearing aids for 3 months to 3 years. Wu, Ho, Hsiao, Brummet, and Chipara (2016) measured ANL in 132 adults before hearing aid fitting and reported 55% to 68% accuracy at predicting real-word hearing aid outcomes as evaluated by the International Outcome Inventory for Hearing Aids (IOI-HA; Cox & Alexander, 2002 ). These studies hypothesized that, if NR algorithms could increase noise tolerance, they would turn individuals with high ANLs into more successful hearing device users.…”

Comparison of Acceptable Noise Level Generated Using Different Transducers and Response Modes

“…The acceptable noise level (ANL) test was developed to quantify the critical amount of background noise that subjects could accept while listening to speech [1–6]. ANL is defined as the lowest signal-to-noise ratio (SNR) that a subject could accept when the target speech was presented at the most comfortable listening level (MCL) [6, 7].…”

“…ANL is defined as the lowest signal-to-noise ratio (SNR) that a subject could accept when the target speech was presented at the most comfortable listening level (MCL) [6, 7]. ANL is derived by subtracting the background noise level (BNL) that the subject can accept from the MCL.…”

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

“…AI has also been used to support clinical diagnoses and treatments, decision-making, the Table 4. Continued prediction of prognoses [98][99][100]125,126], disease profiling, the construction of mass spectral databases [43,[127][128][129], the identification or prediction of disease progress [101,105,[107][108][109][110]130], and the confirmation of diagnoses and the utility of treatments [102][103][104]112,131]. Although many algorithms have been applied, some are not consistently reliable, and certain challenges remain.…”

“…In medical device-based analyses, AI is used to evaluate tissue and blood test results, as well as the outcomes of otorhinolaryngology-specific tests (e.g., polysomnography) [ 72 , 73 , 122 ] and audiometry [ 123 , 124 ]. AI has also been used to support clinical diagnoses and treatments, decision-making, the prediction of prognoses [ 98 - 100 , 125 , 126 ], disease profiling, the construction of mass spectral databases [ 43 , 127 - 129 ], the identification or prediction of disease progress [ 101 , 105 , 107 - 110 , 130 ], and the confirmation of diagnoses and the utility of treatments [ 102 - 104 , 112 , 131 ].…”