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

NCT ID: NCT06364670

Last Updated: 2024-04-15

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

500 participants

Study Classification

INTERVENTIONAL

Study Start Date

2024-05-31

Study Completion Date

2026-12-31

Brief Summary

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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.

Detailed Description

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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

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Tinnitus

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Primary Study Purpose

TREATMENT

Blinding Strategy

NONE

Study Groups

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The acupuncture group

Subjects in the acupuncture group will undergo acupuncture treatment, which is administered 3 times a week for 4 weeks.

Group Type EXPERIMENTAL

acupuncture

Intervention Type PROCEDURE

Acupuncture will be performed at acupoints including TE17 (Yifeng), SI19 (Tinggong), GB2 (Tinghui), TE5 (Waiguan), TE3 (Zhongzhu), ST36 (Zusanli ), KI3 (Taixi), and etc.

Interventions

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acupuncture

Acupuncture will be performed at acupoints including TE17 (Yifeng), SI19 (Tinggong), GB2 (Tinghui), TE5 (Waiguan), TE3 (Zhongzhu), ST36 (Zusanli ), KI3 (Taixi), and etc.

Intervention Type PROCEDURE

Eligibility Criteria

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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.
Minimum Eligible Age

18 Years

Maximum Eligible Age

60 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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The Third Affiliated hospital of Zhejiang Chinese Medical University

OTHER

Sponsor Role lead

Responsible Party

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Hantong Hu

M.D.

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Hantong Hu, M.D

Role: PRINCIPAL_INVESTIGATOR

The Third Affiliated hospital of Zhejiang Chinese Medical University

Locations

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the Third Affiliated Hospital of Zhejiang Chinese Medical University

Hangzhou, Zhejiang, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Xiaohan Huang, M.M

Role: CONTACT

18334334738 ext. 86

Facility Contacts

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Hantong Hu, M.D

Role: primary

18667103032 ext. 86

Xiaohan Huang, M.M

Role: backup

18334334738 ext. 86

References

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Huang X, Jiang D, Kong D, Liu H, Chen L, Li Y, Zhou J, Gao H, Hu H. Predicting Individual Response to Acupuncture in Sensorineural Tinnitus Using Integrated Functional Near-Infrared Spectroscopy and Machine Learning: Protocol for a Model Development and Validation Study. J Multidiscip Healthc. 2025 Oct 9;18:6579-6593. doi: 10.2147/JMDH.S550296. eCollection 2025.

Reference Type DERIVED
PMID: 41089742 (View on PubMed)

Other Identifiers

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ZSLL-KY-2023-011-01

Identifier Type: -

Identifier Source: org_study_id

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