内镜外套胃形成术:胃的位置和任务分类,以使用人工智能进行评估.
James Dials1, Doga Demirel2, Reinaldo Sanchez-Arias3
1Department of Computer Science, Florida Polytechnic University, 4700 Research Way, Lakeland, FL, 33805, USA.
International journal of computer assisted radiology and surgery
|January 11, 2024
概括
通过深度学习,内镜套胃形成术 (ESG) 的自动化分析准确地识别了胃的位置和手术任务. 这项技术是开发ESG实习生自动化绩效反的关键.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 机器学习 机器学习
背景情况:
- 客观的绩效指标对于内镜外套胃形成术 (ESG) 培训至关重要.
- 在ESG中自动化绩效测量是主要目标.
- 准确识别手术任务和解剖位置是必要的自动反.
研究的目的:
- 在ESG程序期间开发和验证用于自动化任务和位置分类的深度学习模型.
- 评估图像和视频分析用于ESG任务识别的性能.
- 在ESG培训中为自动反系统奠定基础.
主要方法:
- 使用了来自专家ESG程序对猪标本的屏幕截图和视频片段的数据集.
- 雇员数据增量和合成少数群体过量抽样 (SMOTE) 用于数据集平衡.
- 应用深度学习用于图像分析和计算机视觉用于视频分析,以分类胃位置和手术任务.
主要成果:
- 基于图像的胃部定位通过SMOTE实现了97%的准确性.
- 基于视频的胃部定位通过SMOTE.实现了99%的准确性.
- 任务分类准确度达到97%的图像和100%的视频.
结论:
- 开发的模型准确地分类ESG期间的胃区域和外科手术任务.
- 图像和视频分析的高精度表明了自动反的潜力.
- 这种自动化系统对于推进培训和提高内镜师在ESG方面的表现至关重要.
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