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Explainable AI Method for Tinnitus Diagnosis via Neighbor-Augmented Knowledge Graph and Traditional Chinese Medicine:

Ziming Yin¹, Zhongling Kuang¹, Haopeng Zhang²

¹School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China.

JMIR Medical Informatics

|June 10, 2024

Summary

This study introduces an explainable AI diagnostic model for tinnitus, achieving high accuracy in identifying subtypes. The knowledge graph approach enhances diagnostic reliability for otolaryngology professionals.

Keywords:

AI TCM algorithm artificial intelligence audiology diagnosis ear explainable knowledge graph syndrome differentiation tinnitus traditional Chinese medicine

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Area of Science:

Otolaryngology
Artificial Intelligence
Medical Informatics

Background:

Tinnitus diagnosis is challenging due to complex pathogenesis and lack of objective methods.
Current clinical practice lacks explainable auxiliary diagnostic tools for tinnitus.

Purpose of the Study:

To develop an explainable artificial intelligence (AI) diagnostic model for tinnitus.
To improve the accuracy of tinnitus diagnosis using AI.

Main Methods:

A knowledge graph-based tinnitus diagnostic method was developed, integrating clinical knowledge and electronic medical records.
Patient similarity was measured using mutual information values within the knowledge graph.
A collaborative neighbor algorithm was proposed for diagnosis recommendation.

Main Results:

The model achieved 99.4% accuracy, 98.5% sensitivity, 99.6% specificity, 98.7% precision, 98.6% F1-score, and 99% AUC for tinnitus subtype inference.
The method demonstrated strong interpretability, with knowledge graph topology explaining patient similarity.
The model was validated against state-of-the-art graph algorithms and explainable machine learning models.

Conclusions:

The developed method offers a reliable and explainable diagnostic tool for tinnitus.
This AI-driven approach is expected to significantly improve tinnitus diagnosis accuracy in clinical settings.