Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning

Area of Science:

• Neuroscience
• Biomedical Engineering
• Artificial Intelligence

Background:

• Tinnitus, a perception of sound without an external source, lacks a pharmaceutical cure.
• Current tinnitus management focuses on therapies to alleviate distress and anxiety.
• Predicting individual patient response to therapy remains a challenge.

Purpose of the Study:

• To develop and validate an Artificial Intelligence (AI) algorithm for predicting patient responses to tinnitus therapies.
• To utilize electroencephalographic (EEG) data to model and forecast therapy outcomes.
• To identify optimal EEG configurations for monitoring treatment effectiveness.

Main Methods:

• Collected EEG data from tinnitus patients before and after a 3-month sound-based therapy.
• Applied feature selection techniques to identify predictive EEG variables.
• Trained AI models using frequency and functional connectivity EEG features to classify patients as therapy responders or non-responders based on Tinnitus Functional Index (TFI) scores.

Main Results:

• AI models achieved prediction accuracies ranging from 98% to 100% in differentiating therapy responders from non-responders.
• Identified specific informative EEG electrodes and frequency/connectivity patterns crucial for accurate classification.
• Demonstrated the potential of AI, including deep learning, in predicting tinnitus treatment outcomes.

Conclusions:

• AI-driven analysis of EEG data shows high efficacy in predicting tinnitus therapy outcomes.
• The study suggests optimal EEG sensor configurations for capturing treatment-related brain changes.
• This approach holds promise for personalized tinnitus management and remote patient monitoring.