使用深度学习模型在结肠镜检查期间自动检测解剖学标志
Mahsa Taghiakbari1,2, Sina Hamidi Ghalehjegh3, Emmanuel Jehanno3
1Faculty of Medicine, Department of Biomedical Sciences, University of Montreal, Montreal, Quebec, Canada.
Journal of the Canadian Association of Gastroenterology
|August 4, 2023
概括
一个新的深层卷积神经网络 (DCNN) 模型可以在结肠镜检查期间自动识别乳房 (ICV) 和尾孔 (AO). 这种人工智能工具可以准确地将这些标志区分出正常组织和息肉,提高检查质量.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 胃肠病学 胃肠病学
背景情况:
- 大肠膜 (ICV) 和尾孔 (AO) 是确认全结肠镜检查的关键地标.
- 这些地标的自动识别可以提高文档和质量评估.
研究的目的:
- 开发和验证深度卷积神经网络 (DCNN) 模型,用于ICV和AO的自动识别.
- 评估模型将这些标志区分出正常的粘膜和结肠直肠多体的能力.
主要方法:
- 一个DCNN分类模型被训练和验证,使用来自318个结肠镜视频的25444.
- 该数据集包括AO,ICV,正常的粘膜和息肉的图像.
- 该模型在单独的数据集上进行了测试,以评估其性能.
主要成果:
- 在DCNN模型中,在85.7%的患者中确定了AO和ICV.
- 该模型在将AO/ICV与正常粘膜区分时达到86.4%的准确性.
- 它以88.6%的准确度区分了片和正常的粘膜.
结论:
- 开发的DCNN模型作为一种新型工具,用于在结肠镜检查期间自动识别AO和ICV.
- 该模型可靠地将解剖学地标与周围的组织和息肉区分开来.
- 实施自动报告和质量审计可以提高结肠镜报告的质量.
相关概念视频
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