Application of machine learning model in ABR waveform interpretation
10.3969/j.issn.1006-7299.2025.04.020
- VernacularTitle:机器学习模型在ABR波形解读中的应用研究进展
- Author:
Manman DOU
1
;
Jing GUAN
;
Qiuju WANG
Author Information
1. 解放军总医院第六医学中心耳鼻咽喉头颈外科医学部耳鼻咽喉内科国家耳鼻咽喉疾病临床医学研究中心(北京 100048);浙江中医药大学医学技术与信息工程学院(杭州 310053)
- Publication Type:Journal Article
- Keywords:
Waveforms of ABR;
Classification of ABR;
Machine learning
- From:
Journal of Audiology and Speech Pathology
2025;33(4):395-398
- CountryChina
- Language:Chinese
-
Abstract:
Objective Auditory brainstem response(ABR)is an objective electrophysiological test that evalu-ates a patient's hearing,and the ABR is interpreted primarily by recognizing the latency of waves Ⅰ through Ⅴ.The latency of wave Ⅰ to wave Ⅴ has important reference value for clinicians to judge the status of patients' auditory neu-ral pathways,so accurate interpretation of the results of this examination is particularly important.However,the a-nalysis of the result of the ABR entirely depends on the auditory teacher's ability to recognize and interpret the hu-man eye,which makes it difficult for inexperienced auditory teachers to interpret ABR waveforms.In order to solve this problem,many scholars proposed to use machine learning(ML)model to objectively interpret ABR results.ML has strong learning ability and can independently complete an objective and accurate interpretation of ABR re-sults,which brings the dawn for the objective and accurate interpretation of ABR results.
