An Ontology-Based Expert System Approach for Hearing Aid Fitting in a Chaotic Environment
Background/Objectives: Hearing aid fitting is critical for hearing loss rehabilitation but involves complex, interdependent parameters, while AI-based technologies offer promise, their reliance on large datasets and cloud infrastructure limits their use in low-resource settings. In such cases, exper...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
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| Series: | Audiology Research |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2039-4349/15/2/39 |
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| Summary: | Background/Objectives: Hearing aid fitting is critical for hearing loss rehabilitation but involves complex, interdependent parameters, while AI-based technologies offer promise, their reliance on large datasets and cloud infrastructure limits their use in low-resource settings. In such cases, expert knowledge, manufacturer guidelines, and research findings become the primary sources of information. This study introduces DHAFES (Dynamic Hearing Aid Fitting Expert System), a personalized, ontology-based system for hearing aid fitting. Methods: A dataset of common patient complaints was analyzed to identify typical auditory issues. A multilingual self-assessment questionnaire was developed to efficiently collect user-reported complaints. With expert input, complaints were categorized and mapped to corresponding hearing aid solutions. An ontology, the Hearing Aid Fitting Ontology (HAFO), was developed using OWL 2. DHAFES, a decision support system, was then implemented to process inputs and generate fitting recommendations. Results: DHAFES supports 33 core complaint classes and ensures transparency and traceability. It operates offline and remotely, improving accessibility in resource-limited environments. Conclusions: DHAFES is a scalable, explainable, and clinically relevant solution for hearing aid fitting. Its ontology-based design enables adaptation to diverse clinical contexts and provides a foundation for future AI integration. |
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| ISSN: | 2039-4349 |