Reliability of an extended version of the 3m™ Eargage tool to assess earcanal size and assist earplug selection.

OBJECTIVE: Evaluate the ability of an extended version of the 3 MTM Eargage to estimate the earcanal size and assess the likelihood that a particular earplug can fit an individual's earcanal, ultimately serving as a tool for selecting earplugs in the field. DESIGN: Earcanal morphology, assessed through earcanal earmolds scans, is compared to earcanal size assessed with the extended eargage (EE) via box plots and Pearson linear correlations coefficients. Relations between attenuation measured on participants (for 6 different earplugs) and their earcanal size assessed with the EE are established via comparison tests. STUDY SAMPLE: 121 participants exposed to occupational noise (103 men, 18 women, mean age 47 years). RESULTS: The earcanal size assessed with the EE allows for estimating the area of the earcanal's first bend cross-section (correlation coefficient  r = 0.533, p < 0.001). Extremely large earcanals (12.7% of earcanals in our sample) lead to significantly lower earplug attenuation (potentially inadequate) than smaller earcanals. CONCLUSIONS: The EE is a simple and inexpensive tool easily deployable in the field to assist earplugs selection. When extended with sizes larger than the maximum size of the commercial tool, it allows for detecting individuals with extremely large earcanals who are most likely to be under-protected.

Morphologic clustering of earcanals using deep learning algorithm to design artificial ears dedicated to earplug attenuation measurement.

Designing earplugs adapted for the widest number of earcanals requires acoustical test fixtures (ATFs) geometrically representative of the population. Most existing ATFs are equipped with unique sized straight cylindrical earcanals, considered representative of average human morphology, and are therefore unable to assess how earplugs can fit different earcanal morphologies. In this study, a methodology to cluster earcanals as a function of their morphologies with the objective of designing artificial ears dedicated to sound attenuation measurement is developed and applied to a sample of Canadian workers' earcanals. The earcanal morphologic indicators that correlate with the attenuations of six models of commercial earplugs are first identified. Three clusters of earcanals are then produced using statistical analysis and an artificial intelligence-based algorithm. In the sample of earcanals considered in this study, the identified clusters differ by the earcanal length and by the surface and ovality of the first bend cross section. The cluster that comprises earcanals with small girth and round first bend cross section shows that earplugs induced attenuation significantly higher than the cluster that includes earcanals with a bigger and more oval first bend cross section.