基于京都胃炎评分的胃镜表现识别的深度学习方法
Ao Liu1, Xilin Zhang2,3, Jiaxin Zhong1
1School of Software Technology, Dalian University of Technology, Dalian, China.
Annals of medicine
|November 5, 2024
概括
人工智能模型使用京都胃炎评分准确地识别了胃癌前兆,表现优于内镜学家. 这种深度学习方法对早期检测胃癌和降低死亡率具有前途.
科学领域:
- 胃肠病学 胃肠病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 胃癌风险与胃镜检查结果和京都胃炎评分相关.
- 早期检测对于减少胃癌死亡率至关重要.
研究的目的:
- 根据"京都胃炎评分"验证AI识别胃镜表现.
- 提高胃癌的早期检测,降低死亡率.
主要方法:
- 一项回顾性研究分析了29,013张胃内镜图像.
- 开发了五种GAM-EfficientNet模型,用于多标签识别缩,扩散性红色,扩大的,肠道转化和结节性.
- 人工智能性能与具有不同经验水平的内镜师进行了比较.
主要成果:
- 与经验丰富的 (72.63%, 0.73) 和经验较少的内镜师 (66.60%, 0.66) 相比,人工智能方法获得了更高的精度 (78.70%) 和F1得分 (0.78).
- 在关键性能指标 (p < 0.05) 上,人工智能显著超过了所有内镜组的表现.
结论:
- 深度学习模型在识别胃表现方面显示出潜力,超过了人类内镜师.
- 人工智能可以作为改善早期胃癌检测的辅助工具,等待潜在的验证.
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