在人类视觉之外:在结肠镜检查中彻底改变了减小性息肉的局部化
Mahsa Dehghan Manshadi1, M Soltani1,2,3,4
1Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 1999143344, Iran.
Bioengineering (Basel, Switzerland)
|November 27, 2025
概括
这项研究引入了一种人工智能助理,用于在结肠镜检查期间检测小结直肠多,提高精度并帮助早期预防癌症. 人工智能工具实现了高精度和回忆,增强了诊断能力.
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:
- 结肠直肠癌 (CRC) 是一个重要的全球健康问题,发病率越来越高.
- 早期检测和去除聚,前体的CRC,对于预防至关重要.
- 传统的结肠镜是有效的,但容易受到人类错误的多重体检测.
研究的目的:
- 开发和评估基于人工智能的系统,用于在结肠镜图像中定位微小的状息肉.
- 提高聚合物检测的准确性,从而有助于预防结直肠癌.
主要方法:
- 用了YOLO-V8深度学习模型来定位多.
- 从多个来源收集了多样化的数据集,包括白光内镜 (WLE) 和窄带成像 (NBI) 图像.
- 进行了全面的评估,并使用聚大小和坐标矩阵分析了数据集的适用性.
主要成果:
- 实现了高性能指标:96.4%的精度,93.89%的回忆和94.46%的F1分数.
- 证明了均衡的超参数组合和综合数据集的有效性.
- 验证了数据集适用于结直肠聚局部化的适用性.
结论:
- 基于人工智能的聚位定位助手显示了显著的希望,改善了微小的状结直肠聚的检测.
- 这项技术可以推进结直肠癌的诊断,特别是在线下设置和分析内镜囊图像.
- 这项研究强调了人工智能的潜力,可以增强胃肠科医生的诊断能力并减少错误.
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