人工智能辅助系统用于评估胃出血的福雷斯特分类:一个多中心诊断研究
Xiao-Jian He1,2,3, Xiao-Ling Wang4,5, Tian-Kang Su6
1Fuzong Clinical Medical College, Fujian Medical University, Fuzhou, China.
Endoscopy
|February 27, 2024
概括
一个深度卷积神经网络 (DCNN) 系统准确地分类胃潰瘍出血 (PUB) 森林分类实时. 这种人工智能工具可以帮助内镜医生,特别是初级内镜医生,在胃镜检查期间提高诊断准确度.
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:
- 不准确的胃出血的福雷斯特分类 (PUB) 可能会对患者的结果产生负面影响.
- 开发一个准确的实时系统对Forrest分类至关重要,特别是对于高风险患者.
研究的目的:
- 开发和评估一个实时深度卷积神经网络 (DCNN) 系统,用于评估PUB的Forrest分类.
- 为了比较DCNN系统与内镜医生的诊断性能.
主要方法:
- 一个DCNN系统在3868张内镜图像上进行了训练,并在834张内部和521张外部图像上进行了验证.
- 未来的验证涉及46个内镜视频,以评估实时性能.
- 将DCNN系统的性能与高级和初级内镜师的性能进行了比较.
主要成果:
- 在验证数据集 (AUC 0.80) 上,DCNN系统在Forrest分类中实现了91.2%的准确性.
- 实时视频分析显示,识别可疑区域的准确率为92.0%.
- DCNN系统表现出比内镜师更优越,更稳定的性能,显著提高了初级内镜师的能力.
结论:
- 该DCNN系统为PUB的Forrest分类提供了令人满意的诊断性能,略高于高级内镜师的诊断性能.
- 这种人工智能系统可以有效地帮助初级内镜医生在胃镜检查期间实时诊断.
相关概念视频
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