A Hybrid Deep Learning Approach to Identify Preventable Childhood Hearing Loss.

ABSTRACT:

Objective

Childhood hearing loss has well-known, lifelong consequences. Infection-related hearing loss disproportionately affects underserved communities yet can be prevented with early identification and treatment. This study evaluates the utility of machine learning in automating tympanogram classifications of the middle ear to facilitate layperson-guided tympanometry in resource-constrained communities.

Design

Diagnostic performance of a hybrid deep learning model for classifying narrow-band tympanometry tracings was evaluated. Using 10-fold cross-validation, a machine learning model was trained and evaluated on 4810 pairs of tympanometry tracings acquired by an audiologist and layperson. The model was trained to classify tracings into types A (normal), B (effusion or per

SUBMITTER: Jin FQ 

PROVIDER: S-EPMC10426782 | biostudies-literature | 2023 Sep-Oct 01

REPOSITORIES: biostudies-literature