人工智能在结肠镜成像中用于多分析的应用- 一个系统性审查
Elham Amirmohammadi1, Ahmad Shalbaf1, Ali Esteki1
1Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Computer methods and programs in biomedicine
|February 1, 2026
概括
深度学习 (DL) 和人工智能 (AI) 显著增强结肠镜检测结肠直肠多,提高准确性和一致性. 本综述分析了人工智能方法,它们的临床影响,以及结直肠癌预防的未来方向.
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:
- 结肠直肠癌是导致死亡的首要原因,因此早期检测聚合物至关重要.
- 结肠镜是黄金标准,但它的准确性因操作员而异.
- 深度学习 (DL) 提供了提高聚合物检测准确性,一致性和客观性的潜力.
研究的目的:
- 在结直肠多分析中提供DL应用的全面分析.
- 审查最先进的DL方法及其性能.
- 确定AI在这个领域的优势,局限性,临床相关性和挑战.
主要方法:
- 对DL架构 (CNN,变压器,混合模型) 的系统审查.
- 对公开基准数据集的性能进行检查.
- 临床相关性和当前挑战的分析.
主要成果:
- 基于人工智能的系统显示出减少观察者之间的变化和提高诊断效率的潜力.
- DL 提高了聚体检测,细分和分类的准确性,一致性和客观性.
- 当前的技术面临着数据不平衡和通用性等挑战.
结论:
- DL正在改变结肠直肠病变的评估,改善多体的检测和诊断.
- 人工智能辅助工具对决策具有重要的临床相关性.
- 未来的研究应该解决在现实环境中优化AI部署的挑战.
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