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

作者：Xiaohan Huang, Da Jiang, Debiao Kong, Huaqiu Liu, Liao Chen, Yang Li, Jie Zhou, Hong Gao, Hantong Hu · 发表于：Journal of Multidisciplinary Healthcare · 年份：2025 · DOI：10.2147/jmdh.s550296 · 被引用次数：3 · 研究领域：Hearing, Cochlea, Tinnitus, Genetics、Vestibular and auditory disorders、Hearing Loss and Rehabilitation

Background: Research indicates that around 20% of adults experience chronic tinnitus, with about a fifth of these cases being severe. Although various treatments are available for tinnitus, their effectiveness is often limited, and each treatment has clinical constraints. While acupuncture has shown promise in treating tinnitus, individual responses vary significantly. Identifying methods to predict acupuncture's effectiveness on Sensorineural tinnitus (SNT) patients in advance remains a critical clinical challenge. Purpose: This study aims to develop and validate a machine learning model based on functional near-infrared spectroscopy (fNIRS) data to predict acupuncture treatment outcomes in SNT patients. Methods and Analysis: This study will enroll 500 subjects with SNT, with sample size determined via established machine learning feature-to-sample ratio method. Specific brain regions will be scanned using fNIRS pretreatment, collecting data from multiple temporal and frontal lobe channels. Subjects will receive standardized acupuncture over four weeks. Outcomes will be evaluated using validated measures including Tinnitus Severity Grading and Tinnitus Handicap Inventory. Based on treatment responses, subjects will be categorized into "favorable prognosis" or "poor prognosis" groups. The dataset will be randomly split into training (70%) and test (30%) sets. Support Vector Machine (SVM) algorithms will identify features and develop models, with performance evaluated through ...