基于深度学习的诊断系统,以视频光镜为基础,用于维洛缺陷的诊断系统,用于修复裂口口腔的患者
Jeong Hyun Ha1,2, Haeyun Lee3,4,5, Seok Min Kwon6
1Department of Plastic and Reconstructive Surgery, Biomedical Research Institute, Seoul National University Hospital.
The Journal of craniofacial surgery
|October 10, 2023
概括
深度学习算法在口腔裂修复后诊断腹腔缺陷 (VPI) 方面表现有前途,其性能与使用视频光镜成像的专家外科医生相当.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 语言病理学 语音病理学
背景情况:
- 耳鼻缺陷 (VPI) 是口腔裂修复后的常见并发症,需要准确的诊断以进行有效的管理.
- 评估VPI需要将图像分析与感知语音评估相结合.
- 目前的诊断方法依赖于视频光镜成像和语音评估的专家解释.
研究的目的:
- 通过视频光镜成像来评估深度学习算法的诊断性能,以检测使用视频光镜成像的喉功能缺陷 (VPI).
- 将深度学习模型的准确性与经验丰富的整形外科医生和语言病理学家的诊断进行比较.
- 评估基于深度学习的VPI诊断与临床症状的一致性.
主要方法:
- 对714名经过修复的口腔裂患者进行了回顾性审查,这些患者正在接受喉功能评估.
- 在视频光学数据上训练的六个深度学习算法 (VGGNet,ResNet,Xception,ResNext,DenseNet,SENet) 的应用.
- 深度学习算法性能与视频光成像和感知语音评估的专家诊断的比较.
主要成果:
- 深度学习算法实现了高的诊断准确性,ROC曲线下的区域范围从0.8758到0.9468 (保留) 和0.7992到0.8574 (交叉验证).
- 深度学习模型的性能与经验丰富的整形外科医生在从视频光镜图像中诊断VPI时的性能相当.
- 与语音病理学家的感知评估进行了一致性分析,以将成像发现与临床表现相关联.
结论:
- 深度学习技术为人类专家提供了可靠和可比的替代方案,以基于视频光镜成像来诊断腹腔缺陷 (VPI).
- 人工智能驱动的诊断工具有潜力提高VPI评估的效率和准确性,在裂口后的 palatal修复患者.
- 进一步的研究可以探索将深度学习与感知评估整合为全面的VPI诊断方法.
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