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Developing a diagnostic support system for audiogram interpretation using deep learning-based object detection.

Titipat Achakulvisut¹, Suchanon Phanthong², Thanawut Timpitak¹

¹Department of Biomedical Engineering, Faculty of Engineering, Mahidol University.

Journal of Otology

|October 10, 2025

Summary

An automated system for digitizing audiograms and classifying hearing loss demonstrates high accuracy, comparable to traditional methods. This technology can aid otolaryngologists in faster, more effective patient treatment.

Keywords:

Audiogram Automatic Machine Learning (AutoML)Deep machine learning Random Forest Classifier (RFC)Support Vector Machine (SVM)Testing set Training set Validation set XGBoost

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Area of Science:

Audiology
Medical Imaging
Machine Learning

Background:

Accurate audiogram interpretation is crucial for diagnosing hearing loss.
Traditional methods can be time-consuming and subjective.
Automating audiogram analysis offers potential for improved efficiency and accuracy.

Purpose of the Study:

To develop and evaluate an automated system for digitizing audiograms.
To classify hearing loss levels using machine learning.
To compare the automated system's performance against traditional methods and expert interpretations.

Main Methods:

A retrospective study utilized 1,959 audiogram images.
An object detection approach was employed for audiogram digitization.
Multiple machine learning models were developed for hearing loss classification, with data split into training and testing sets.

Main Results:

The object detection model achieved a 96.43% F1-score in hearing loss classification.
The Random Forest Classifier model demonstrated 96.43% accuracy, precision, and recall.
Performance was comparable to manual extraction methods and otolaryngologists' interpretations.

Conclusions:

The automated audiogram digitization and classification system performs comparably to existing methods.
This system has the potential to assist otolaryngologists in timely and effective hearing loss diagnosis and treatment.