We envision various uses for such as dataset:
Training and Education: Synthetic audiograms can be used to train audiology students, ENT residents, and other healthcare professionals. These artificial datasets can represent a wide range of hearing loss patterns, including rare or complex cases that may not be frequently encountered in clinical practice. This can help improve diagnostic skills and pattern recognition without relying on real patient data.
Algorithm Development and Testing: In the development of automated audiogram interpretation systems or AI-assisted diagnostic tools, synthetic data can be crucial. It allows researchers and developers to test and refine their algorithms on a diverse set of hearing loss patterns without the need for large amounts of real patient data, which can be difficult to obtain due to privacy concerns.
Research on Hearing Aid Fitting Algorithms: Researchers developing new hearing aid fitting algorithms can use synthetic audiogram data to test and refine their methods. This allows for the evaluation of fitting strategies across a wide range of hearing loss configurations without the need for extensive clinical trials in the early stages of development.
Predictive Modeling: Synthetic audiogram data could be used to develop predictive models for hearing loss progression. These models could help clinicians anticipate future changes in a patient’s hearing based on current audiometric data and other factors, potentially allowing for earlier interventions.
In all these applications, synthetic audiogram data provides a valuable resource for advancing audiology practice, research, and education while maintaining patient privacy and overcoming limitations in data availability. The goal is to improve the speed, accuracy, and accessibility of hearing healthcare.
The synthetic data only has air-conduction measures. Further work is required to generate synthetic bone-conduction measures.