Application of Machine Learning Based on fNIRS in Predicting Acupuncture's Efficacy in Treating Tinnitus

Application of Machine Learning Based on fNIRS in Predicting Acupuncture's Efficacy in Treating Tinnitus

This trial aims to use machine learning to analyze fNIRS imaging data of specific brain regions of tinnitus patients, thereby constructing a predictive model of the clinical efficacy of acupuncture for SNT.

This study will recruit 500 subjects with tinnitus. Functional near-infrared spectroscopy (fNIRS) will be employed to examine specific brain regions, and the corresponding fNIRS imaging data from all detection channels will be extracted. Subsequently, the subjects will undergo a course of acupuncture treatment. Based on the recovery status of tinnitus at the conclusion of the acupuncture course, all subjects will be categorized into a "good prognosis group" and a "poor prognosis group" according to relevant efficacy criteria. The entire dataset will then be randomly divided into a training set (70%) and a test set (30%) following a 7:3 ratio.

Inclusion Criteria:

• Bilateral tinnitus that meets the diagnostic criteria for chronic subjective tinnitus.
• Male and female, aged between 18 and 60 years.
• Right-handed subjects who are able to comply with the study protocol and sign written informed consent.
• Not participating in other clinical trials concurrently.

Exclusion Criteria:

• Participants with objective tinnitus.
• Participants have nervous system diseases or neuropsychiatric diseases that can significantly affect brain blood oxygen metabolism assessed by fNIRS.
• Participants with severe cardiovascular and cerebrovascular diseases, malignant liver and kidney diseases, and other serious diseases.
• Participants have any contraindications for acupuncture (such as a bleeding tendency).
• Pregnant or lactating women.
• Participants have received tinnitus treatment with drugs or other therapies in the last four weeks.