选择最好的人工智能技术来分析胃肠道内镜图像
概括
本研究确定和排名了用于胃肠内镜图像分析的人工智能 (AI) 方法. 计算机辅助检测 (CAD) 系统和CIECAM模型是提高诊断性能的顶级AI技术.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 胃肠内镜产生了大量的图像数据,需要进行高级分析.
- 为图像分析选择最佳的人工智能 (AI) 技术对于临床应用至关重要.
- 这项研究解决了对人工智能方法在该领域进行系统评估和优先考虑的需求.
研究的目的:
- 为了全面识别和优先考虑用于胃肠道内镜图像分析的AI技术.
- 引入最好的AI技术,使用混合模糊分析层次流程 (FAHP) 和通过类似于理想解决方案 (TOPSIS) 方法的订单偏好技术.
- 为选择适当的AI方法提供一个框架,以提高诊断能力.
主要方法:
- 一项系统的文献搜索确定了70种人工智能技术.
- 德尔菲方法建立了七个选择标准:有效性,准确性,全面性,处理时间,成本,简单性和执行能力.
- 模糊分析层次过程 (FAHP) 确定了标准权重,并通过与理想解决方案相似的订单偏好技术 (TOPSIS) 排名了AI技术.
主要成果:
- 定义了七个关键标准,用于评估胃肠内镜中的AI技术.
- 排名第一的AI方法包括计算机辅助检测 (CAD) 系统 (0.8203),CIECAM (0.8122) 和组合方法 (PD-CNN-PCC-EELM: 0.8109; DNN-CAD: 0.7928).
- 在ResNet18深度学习模型也显示了高性能 (0.7921).
结论:
- 该研究提供了用于胃肠内镜图像分析的AI技术的优先列表.
- 这些发现可以指导开发更有效的AI驱动的诊断工具.
- 该方法为评估和选择医学成像中的AI技术提供了强大的方法.
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