在清醒时进行头骨切除术时的言语停顿和言语障碍的分类:多个数据库分析分析
Ilias Maoudj1, Atsushi Kuwano2,3, Céline Panheleux4,5
1Laboratoire de Traitement de l'Information Médicale, UMR INSERM 1101, Brest, France. ilias.maoudj@etudiant.univ-brest.fr.
International journal of computer assisted radiology and surgery
|December 9, 2024
概括
这项研究引入了一个深度学习模型,用于在清醒时进行骨切割时客观地评估语音停止. 该模型显示出有希望的结果,即使在不同的语言,提高手术精度和患者的结果.
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能在医学中的应用
- 语音处理 语音处理
背景情况:
- 醒着的骨切除术对于在脑瘤手术期间保持言语功能至关重要.
- 在这些程序期间目前的语音功能评估是主观的,依赖于临床医生的观察.
- 需要客观的实时评估工具来提高手术精度和患者的治疗结果.
研究的目的:
- 探索深度学习模型的使用,以客观地评估语音停顿和语音障碍.
- 在清醒时进行切手术过程中实时评估模型的性能.
- 调查模型在语言内和跨语言场景中的有效性.
主要方法:
- 从日本和法国的23个清醒的骨切割中提取了1883个音频片段.
- 训练了一个基于Wav2Vec2的深度学习模型来检测语音停顿和损伤.
- 在不同的数据集设置和预处理技术中使用F1评分评估模型性能.
主要成果:
- 在日本数据上训练和测试时,F1得分为84.12%.
- 在跨语言环境中获得74.68%的F1分数 (在日语中接受培训,在法语中进行测试).
- 降噪预处理显著改善了模型结果.
结论:
- 深度学习模型在清醒时进行骨切割时对客观语音评估的表现令人鼓舞.
- 该模型甚至在跨语言应用中也显示出潜力,表明了更广泛的实用性.
- 进一步评估是有必要的,预处理技术,如降噪提高效率.
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