实时深度学习NICE分类和退出速度监测用于结直肠内镜
Ganhong Wang1, Limei Yin2, Zhijia Shen2
1Department of Gastroenterology, Changshu Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou, Jiangsu, China.
Digital health
|November 3, 2025
概括
使用EfficientNet开发了一个用于结直肠病变分类的深度学习系统 (NICE),实现了高精度和实时性能. 这种工具提高了诊断的一致性,并加快了内镜师的培训.
科学领域:
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
- 胃肠病学 胃肠病学
背景情况:
- 对结直肠病变的准确分类对于早期癌症检测至关重要.
- 内镜诊断的一致性和培训效率仍然是挑战.
- 深度学习为自动诊断辅助提供了潜力.
研究的目的:
- 开发和部署一个深度学习系统,用于对结直肠病变进行自动NICE分类.
- 将实时提取速度监控纳入.
- 缩短内镜师的学习曲线.
主要方法:
- 在2605张结肠镜图像上对CNN (EfficientNet,ResNet50,VGG19) 和变压器 (ViT,Swin,CvT) 模型进行了微调.
- 使用Grad-CAM,指导Grad-CAM和SHAP的模型可解释性.
- 通过ONNX进行实时推断 (>50 FPS) 与撤回速度监控的部署.
主要成果:
- EfficientNet实现了最高的内部精度 (0.910) 和F1得分 (0.916).
- 外部验证得出宏观平均AUC为0.994,精度为0.936和回忆为0.918.
- 该系统与NICE 2和NICE 3的内镜灵敏度/特异性相匹配或超过,并提供实时速度警告.
结论:
- 在ONNX系统中首次集成基于EfficientNet的高精度NICE分类,可解释性和实时速度监控.
- 该工具显示了提高早期结直肠癌的光学诊断一致性的潜力.
- 该系统可以加速新手内镜师的培训.
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