探索使用深度学习在视频强光镜中实时跟踪声的息肉.
Sanjana Kaza1, Abhinita S Mohanty2, Aisha Serpedin3
1Cornell Tech, New York, New York, USA.
The Laryngoscope
|February 2, 2026
概括
使用具有时间跟踪的YOLO12的深度学习系统有效地识别了视频中的声息肉. 这种人工智能辅助的方法实现了接近实时的性能,用于检测声病变.
科学领域:
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
背景情况:
- 声的息肉是可以影响语音质量的病变.
- 准确及时检测声的息肉对于诊断和治疗至关重要.
- 当前的检测方法可能会从人工智能驱动的改进中受益.
研究的目的:
- 开发和评估一种深度学习对象检测系统,用于识别用"只看一次" (You Only Look Once,简称YOLO) 的视频镜头中的声多.
- 评估时间跟踪对声多的检测性能的额外好处.
- 探索实时人工智能辅助检测声病变的潜力.
主要方法:
- 使用了来自55个喉腔镜视频的12742个注释的数据集.
- 经过预先训练的YOLO11和YOLO12模型被微调以检测多重体.
- 开发了一个时间跟踪算法,以提高跨的检测一致性.
主要成果:
- 在聚合物检测指标上,YOLO12显著优于YOLO11.
- 在测试装置上,YOLO12获得了83.1%的精度和67.6%的F1得分.
- 使用YOLO12进行时间跟踪,在接近实时速度 (21.4 fps) 的情况下,平均平均精度 (mAP@0.5) 提高到70.4%.
结论:
- 与时间跟踪相结合的YOLO12为声多检测提供了增强的性能.
- 该系统展示了近乎实时的功能,促进了潜在的临床应用.
- 人工智能辅助检测有望改善声病变的识别.
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