深度学习和最小侵入性内镜:在囊内镜中自动分类形胃病变
Miguel Mascarenhas1,2,3, Francisco Mendes1,2, Tiago Ribeiro1,2
1Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal.
Clinical and translational gastroenterology
|July 5, 2023
概括
使用卷积神经网络 (CNN) 的新人工智能算法可以自动从囊内镜 (CE) 图像中高精度地检测各种胃病变. 这种人工智能工具显著改善了胃肠道评估的诊断产量.
科学领域:
- 胃肠病学 胃肠病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 囊内镜 (CE) 是胃肠道评估的一个关键工具.
- 目前通过CE对胃病变的诊断产量不足于最佳水平.
- 人工智能,特别是CNN在分析胃病理的CE图像中的应用仍然未被充分探索.
研究的目的:
- 开发和验证基于CNN的算法,用于通过无线囊内镜 (WCE) 检测到的各种胃病变的自动分类.
- 评估开发的CNN算法在识别胃异常方面的诊断性能.
主要方法:
- 一个CNN算法是使用来自三个不同的CE设备的12,918张胃图像数据集开发的.
- 图像包括各种病变,如血管异常,突出的病变,,侵蚀和正常的粘膜.
- 美国有线电视新闻网的模型接受了培训和验证,对其性能进行了评估,并与专家胃肠病学家的共识相对应.
主要成果:
- 对于胃病变,CNN实现了高诊断性能:97.4%的灵敏度,95.9%的特异性,95.0%的正预测值,97.8%的负预测值.
- 病变检测的整体准确率为96.6%.
- 该算法以每秒115张图像的速度进行了快速图像处理.
结论:
- 这项研究介绍了首个CNN算法,能够自动检测CE中的多形胃病变.
- 开发的AI工具显示出显著的潜力,可以提高使用WCE的胃评估的诊断准确性和效率.
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