中国临床医学2025，Vol.32Issue(2)：207-211,5.DOI:10.12025/j.issn.1008-6358.2025.20250219
基于双流ViT+ConvNeXt架构的前庭功能校准识别模型构建与价值
Construction and value of a vestibular function calibration test recognition model based on dual-stream ViT and ConvNeXt architecture
摘要
Abstract
Objective To improve the efficiency and accuracy of videonystagmography calibration test results while enabling effective recognition of saccadic undershoot waveform by developing a dual-stream architecture-based deep learning model.Methods A vestibular function calibration test recognition model with cross-modal feature fusion was constructed by integrating vision transformer(ViT)and a modified ConvNeXt convolutional network.The model utilized trajectory pictures and spatial distribution maps as inputs,employed a multi-task learning framework to classify calibration data,and to directly evaluate undershoot waveform.Results The model showed outstanding performance in assessing calibration compliance.The accuracy,sensitivity,specificity of the model in left side,middle,and right side were all greater than 90%,and AUC values were all greater than 0.99,with 97.66%of optimal accuracy(middle),98.98%of optimal sensitivity(middle),96.87%of optimal specificity(right side),and 0.997 of AUC(right side).The model also showed promising performance in undershoot waveform recognition with 87.50%of accuracy,89.66%of sensitivity,85.71%of specificity,86.67%of F1 score,and 0.931 of AUC.Conclusions The proposed method not only significantly enhances the efficiency and accuracy of calibration test results,but also provides a novel solution for undershoot waveform recognition.关键词
前庭功能/校准试验/眼震视图/深度学习模型Key words
vestibular function/calibration test/videonystagmography/deep learning model分类
医药卫生引用本文复制引用
罗旭,吴沛霞,郝维明,屈寅弘,陈寒..基于双流ViT+ConvNeXt架构的前庭功能校准识别模型构建与价值[J].中国临床医学,2025,32(2):207-211,5.基金项目
国家重点研发计划项目(2023YFC2508004).Supported by National Key Research and Development Program of China(2023YFC2508004). （2023YFC2508004）
