设计和验证一个人工智能系统,以检测结肠清洁的质量在结肠镜检查之前
Antonio Z Gimeno-García1, Silvia Alayón-Miranda2, Federica Benítez-Zafra1
1Gastroenterology Department, Hospital Universitario de Canarias, Instituto Universitario de Tecnologías Biomédicas (ITB) & Centro de Investigación Biomédica de Canarias (CIBICAN), Internal Medicine Department, Universidad de La Laguna, Tenerife, Spain.
Gastroenterologia y hepatologia
|December 28, 2023
概括
一个新的卷积神经网络 (CNN) 从患者图像准确地分类了肠道准备质量. 这种人工智能工具可以通过识别不充分的清洁来帮助优化结肠镜检查程序.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 患者对肠道清洁质量的看法对于优化结肠镜准备至关重要.
- 目前用于评估肠道准备的方法可以是主观的,并且在准确度上有所不同.
研究的目的:
- 开发和验证一个卷积神经网络 (CNN) 用于直肠排泄物图像的自动分类.
- 评估CNN在结肠镜检查前区分适当和不适当的肠道清洗的能力.
主要方法:
- 从660名接受肠道准备的患者收集了1203张直肠排泄物图像.
- 在初始数据集 (799张图像) 上训练并验证了CNN模型.
- 在第二个数据集 (404张图像) 上测试了CNN,并从前性地将其性能与波士顿肠道准备量表 (BBPS) 对200名患者进行了比较.
主要成果:
- 在初始 (97.49%准确度) 和第二个数据集 (95%准确度) 上,CNN在分类清洗质量方面实现了高准确度,灵敏度和特异性.
- 美国有线电视新闻网 (CNN) 的预测显示,与BBPS分数有显著的关联 (P<0.001).
- 该模型正确识别了77.78%的肠道准备不良的患者.
结论:
- 开发的CNN在从患者提交的图像中分类肠道清洗的充分性方面表现出高准确性.
- 这种由人工智能驱动的工具显示出对肠道准备质量的客观和有效评估的希望.
- CNN可以帮助识别需要救援清洁策略的患者,从而有可能改善结肠镜检查结果.
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