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Anatomy of the Ear

Anatomy of the Ear

Auditory sensation, commonly called hearing, involves the transformation of sonic waves into neural impulses facilitated by the structures of the auditory organ. The prominent, flesh-like structure on the side of the head, called the auricle, directs sound waves towards the auditory canal. The auricle is often mislabeled as the pinna, a term more aligned with mobile structures like a feline's external ear. The auditory canal penetrates the cranium via the external auditory meatus of the...

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Age-Stratified Classification of Common Middle Ear Pathologies Using Pressure-Less Acoustic Immittance (PLAI™) and

Aleksandar Miladinović¹, Francesco Bassi², Miloš Ajčević²

¹Institute for Maternal and Child Health-IRCCS "Burlo Garofolo", 34137 Trieste, Italy.

Healthcare (Basel, Switzerland)

|August 14, 2025

Summary

This summary is machine-generated.

Pressure-Less Acoustic Immittance (PLAI™) offers a novel, non-invasive method for diagnosing middle ear conditions. Age-specific models significantly improve diagnostic accuracy, especially in pediatric audiology.

Keywords:

Otitis Media Pressure-Less Acoustic Immittance (PLAI)age-specific classification ear canal acoustics false negatives middle ear diagnostics pediatric audiology

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Area of Science:

Audiology
Medical Diagnostics
Acoustic Engineering

Background:

Conventional tympanometry for middle ear diagnostics can be invasive.
There is a need for non-invasive, accurate diagnostic tools for pediatric middle ear pathologies.

Purpose of the Study:

To explore Pressure-Less Acoustic Immittance (PLAI™) as a novel, non-invasive diagnostic approach.
To evaluate the efficacy of age-stratified machine learning models for diagnosing middle ear pathologies using PLAI™ data.

Main Methods:

Collected 516 ear measurements stratified by age (0-3, 3-12, 12+ years).
Analyzed PLAI™ acoustic parameters (resonant frequency, peak admittance, canal volume) using Random Forest classifiers.
Employed SMOTE for class imbalance and SHAP values for feature importance.

Main Results:

Age-specific models achieved superior diagnostic accuracy (macro F1-scores: 0.79-0.84) compared to non-stratified models.
Resonant frequency, ear canal volume, and peak admittance were key diagnostic features.
Age-based stratification reduced false negatives for Otitis Media with Effusion and tympanic membrane retractions.

Conclusions:

PLAI™ shows promise for early, pressure-free middle ear diagnostics.
Age-aware machine learning models enhance accuracy in pediatric audiology.
Further validation on larger cohorts is recommended for integrating these frameworks.