接触内镜 - 窄带成像 (CE-NBI) 数据集用于喉损伤评估
Nazila Esmaeili1,2,3, Nikolaos Davaris4,5, Axel Boese6
1Department of Otorhinolaryngology, Head and Neck Surgery, Justus Liebig University of Giessen, 35392, Giessen, Germany. nazila.esmaeili@tum.de.
Scientific data
|October 21, 2023
概括
这项研究引入了新的声图像数据集,以改善喉癌的诊断. 机器学习模型用于接触内镜与窄带成像 (CE-NBI) 结合,以更好地进行光学活检分析.
科学领域:
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 良性声病变和喉癌之间的准确区分对于有效的患者管理至关重要.
- 接触内镜与窄带成像 (CE-NBI) 结合,可实时可视化亚皮质血管模式,有助于诊断.
- 对CE-NBI图像的主观解释是一个挑战,需要客观的,基于计算机的解决方案.
研究的目的:
- 引入首个公开可访问的CE-NBI数据集,用于声下皮质血管系统.
- 促进机器学习模型的开发,用于喉损伤的评估.
- 为了提高光学活检在诊断喉癌的准确性.
主要方法:
- 编制了一套新型数据集,包括来自210名患有病态声状况的成年患者的11,144张CE-NBI图像.
- 将CE-NBI图像注释为监督学习的三个不同的标签类别.
- 开发和实施用于图像分析的各种机器学习分类场景.
主要成果:
- CE-NBI数据集提供了对子皮质血管的增强和放大可视化.
- 该数据集已被验证为其在临床评估中用于喉癌诊断的有用性.
- 机器学习方法在治疗喉损伤评估中的临床挑战方面显示出潜力.
结论:
- CE-NBI数据集是研究声病理学和喉癌检测的宝贵资源.
- 该数据集上的机器学习应用程序可以导致更客观,更准确的诊断工具.
- 这项工作支持人工智能驱动的光学活检的进步,以改善患者的治疗结果.
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