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开发基于人工智能的喉癌诊断平台,使用喉镜图像
Hye-Bin Jang1, Seung Bae Park2, Sang Jun Lee2
1Departments of Otolaryngology-Head and Neck Surgery, Chonnam National University Medical School & Hwasun Hospital, Hwasun 58128, Republic of Korea.
人工智能 (AI) 模型可以从喉镜图像中准确地检测喉癌. 这个人工智能平台集成了声带选择和病变检测,以快速,可靠的诊断.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 喉癌的诊断依赖于视觉检查喉腔镜图像.
- 自动检测系统可以提高诊断的准确性和效率.
研究的目的:
- 开发和评估人工智能模型用于用喉镜图像检测喉癌.
- 评估深度学习模型在识别和定位喉癌方面的表现.
主要方法:
- 两个FCN-ResNet101深度学习模型被设计用于声带选择和喉癌局部化.
- 数据集由耳鼻喉科医生进行注释,并使用裁剪,正常化和增强技术进行预处理.
- 绩效指标包括交叉与联盟 (IoU),子得分,准确性,精度,回忆,F1得分和推断时间.
主要成果:
- 声带选择模型实现了0.6534的平均IOU和0.7692的Dice得分,准确度为0.9972.
- 喉癌检测模型实现了0.6469的平均IOU和0.7515的Dice得分,准确度为0.9860 .
- 实现实时推断,每张图像的处理时间在0.0244-0.0284秒之间.
结论:
- 集成的人工智能平台结合了声带选择和病变检测,可以准确和快速地检测喉癌.
- 开发的模型在从喉镜图像中识别喉癌方面表现出很高的性能.
- 这种由人工智能驱动的方法对改善耳鼻喉科的诊断工作流程充满希望.
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