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

Overview

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.

Conditions

Interventions

Eligibility

Locations (1)

Outcomes

Central Contacts