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Predicting Individual Response to Acupuncture in Sensorineural Tinnitus Using Integrated Functional Near-Infrared
Xiaohan Huang1, Da Jiang1, Debiao Kong1
1The Third Clinical College, Zhejiang Chinese Medical University, Hangzhou, People's Republic of China.
Journal of Multidisciplinary Healthcare
|October 15, 2025
Summary
This study uses functional near-infrared spectroscopy (fNIRS) and machine learning to predict acupuncture success for sensorineural tinnitus (SNT) patients. The goal is to personalize SNT treatment by identifying patients likely to benefit from acupuncture.
Area of Science:
- Neuroscience
- Medical Technology
- Computational Biology
Background:
- Chronic tinnitus affects 20% of adults, with limited treatment efficacy.
- Sensorineural tinnitus (SNT) patients show variable responses to acupuncture.
- Predicting acupuncture effectiveness for SNT is a clinical challenge.
Purpose of the Study:
- Develop and validate a machine learning model using fNIRS data.
- Predict acupuncture treatment outcomes for SNT patients.
- Enhance personalized tinnitus treatment strategies.
Main Methods:
- 500 SNT subjects will be enrolled.
- fNIRS will scan temporal and frontal lobes pre-treatment.
- Machine learning (SVM) will analyze data to predict treatment response.
Main Results:
- Anticipate an accurate fNIRS-based model to predict acupuncture response.
- Expect to identify predictive neurofunctional features in temporal and frontal lobes.
- Aim to provide an objective tool for clinical decision-making.
Conclusions:
- First study integrating fNIRS and machine learning for SNT acupuncture efficacy prediction.
- Methodology addresses challenges in acupuncture research with advanced analytics.
- Findings may enable personalized SNT treatment and guide future neuroimaging research.
