Machine Learning-Based Hearing Aid Fitting Personalization Using Clinical Fitting Data

Abstract

The initial software fitting prescribed by the fitting formula largely depends on the patient’s hearing loss, which may not be the optimal preference for a particular user. Certain criteria must also be readjusted by an audiologist to meet the user-specific requirements. Therefore, this study focuses on the novel application of a neural network (NN) technique to build a suitable fitting algorithm with prescribed hearing loss and the corresponding preferred gain to minimize the gap between optimized fittings. The algorithm intended to learn the hearing preferences of an individual user such that the initial fitting may be optimized. These findings demonstrate the efficiency of the algorithm, with and without additional features. Using the clinical fitting data, the average mean square error (MSE) for the simple NN algorithm was 5.4183%. By adding additional features to the data, the algorithm performed better, and the average MSE was as low as 5.2530%. However, the algorithm outperformed Company A fitting software, as the MSE was the highest at 5.4748%. As the company’s automatic fitting has a noticeable discrepancy with clinical fitting records, the impeccable results from this study can lead to a better path towards fitting satisfaction, thus benefiting the hearing-impaired community to a larger extent.

A Review of Machine Learning Approaches for the Personalization of Amplification in Hearing Aids

“…The study covered in [ 31 ] built upon the previous work by the same research group. It involved the evaluation of a neural-network-based personalized hearing-aid-fitting algorithm.…”

Personalized Treatments Based on Laryngopharyngeal Reflux Patient Profiles: A Narrative Review

“…Because developing and implementing personalized therapeutic strategies may be resource-intensive, requiring advanced diagnostic tools and technologies, the current resource limitations of many healthcare systems may be considered the primary barrier to the widespread adoption of personalized approaches. The development of artificial intelligence software supporting the physician's tasks may be an important issue for widespread future personalized approaches [121][122][123][124][125].…”