超光谱成像通过人工智能辅助结直肠多瘤诊断来促进切除和丢弃策略:一项试点研究
Cheng Peng1, Chong Xuan Tian2, Yijun Mu1
1Department of Gastroenterology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Cancer medicine
|September 25, 2024
概括
使用人工智能的高光谱成像 (HSI) 准确地在新鲜组织中区分结直肠聚,改善初学者内镜诊断,并支持节省成本的策略.
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:
- 对于切除和丢弃策略而言,结直肠多的精确光学诊断面临着挑战.
- 超光谱成像 (HSI) 提供了在结肠镜检查期间实时组织分析的潜力.
研究的目的:
- 调查HSI的可行性,以识别结直肠多性质.
- 评估HSI在结肠镜检查期间在新鲜组织中诊断结肠直肠癌的能力.
主要方法:
- 一个残余神经网络模型使用转移学习训练了来自161个结直肠多的高光谱图像的144,900张图像.
- 将HSI-AI模型的诊断性能与内镜师进行了比较,并评估了其辅助效率.
主要成果:
- 定量HSI揭示了结肠直肠多体的组织学差异.
- 在实验室中,HSI-AI模型在区分聚类型方面表现出高灵敏度和特异性.
- 在HSI-AI模型的帮助下,初学者显示,瘤多的负预测值显著增加.
结论:
- HSI是一种可行的体外诊断工具,用于区分瘤结直肠多.
- 在结肠镜检查期间,人工智能辅助的HSI诊断显示出提高诊断准确性的潜力,特别是在初学者内镜师身上.
- 这项技术可能有助于切除和丢弃策略,可能降低医疗保健成本.
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