用机器学习自动分析喉镜超声，识别声带麻痹
Machine Learning-Assisted Vocal Cord Ultrasound Examination: Project VIPR
- 用深度学习自动分割声带并分类正常与麻痹图像
- 分割模型准确率96%，分类模型最高达99%
- 适合临床医生快速辅助诊断声带功能异常
声带超声（VCUS）是一种创伤更小、患者耐受性更好的检查方法，但其准确性依赖操作者。本研究提出一种机器学习辅助算法，实现声带自动识别，并区分正常声带与声带麻痹（VCP）图像。从30名志愿者采集的VCUS视频中提取帧图像，经裁剪后作为训练数据，用于构建声带分割和VCP分类模型。结果表明，声带分割模型在验证集上准确率达96%；最优分类模型（VIPRnet）在验证集上准确率达到99%。研究显示，机器学习辅助分析VCUS在提升诊断准确性方面具有巨大潜力，可降低人为判断差异。
原文摘要 · Abstract (English)
Intro: Vocal cord ultrasound (VCUS) has emerged as a less invasive and better tolerated examination technique, but its accuracy is operator dependent. This research aims to apply a machine learning-assisted algorithm to automatically identify the vocal cords and distinguish normal vocal cord images from vocal cord paralysis (VCP). Methods: VCUS videos were acquired from 30 volunteers, which were split into still frames and cropped to a uniform size. Healthy and simulated VCP images were used as training data for vocal cord segmentation and VCP classification models. Results: The vocal cord segmentation model achieved a validation accuracy of 96%, while the best classification model (VIPRnet) achieved a validation accuracy of 99%. Conclusion: Machine learning-assisted analysis of VCUS shows great promise in improving diagnostic accuracy over operator-dependent human interpretation.
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