开发用于使用基于深度学习的对象检测来解释音频图的诊断支持系统
Titipat Achakulvisut1, Suchanon Phanthong2, Thanawut Timpitak1
1Department of Biomedical Engineering, Faculty of Engineering, Mahidol University.
Journal of otology
|October 10, 2025
概括
用于数字化音频图和分类听力损失的自动化系统表现出高精度,与传统方法相比. 这项技术可以帮助耳鼻喉科医生更快,更有效地治疗患者.
科学领域:
- 听力学 听力学是指听力学.
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 准确的听力图解读对于诊断听力损失至关重要.
- 传统的方法可能是耗时和主观的.
- 自动化音频记录分析提供了提高效率和准确性的潜力.
研究的目的:
- 开发和评估用于数字化音频录制的自动化系统.
- 使用机器学习对听力损失水平进行分类.
- 将自动化系统的性能与传统方法和专家解释进行比较.
主要方法:
- 一项回顾性研究使用了1,959张音频图像.
- 对象检测方法用于音频录像的数字化.
- 为听力损失分类开发了多种机器学习模型,数据分为训练和测试集.
主要成果:
- 对象检测模型在听力损失分类中获得了96.43%的F1分数.
- 随机森林分类器模型显示了96.43%的准确性,精度和回忆率.
- 性能与手动提取方法和耳鼻喉科医生的解释相美.
结论:
- 自动音频录像数字化和分类系统的性能与现有方法相比.
- 该系统有可能帮助耳鼻喉科医生及时有效地诊断和治疗听力损失.
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