使用深度学习模型对小肠异常的儿科视频囊内镜图像进行分类
Yi-Hsuan Huang1, Qian Lin1, Xin-Yan Jin2
1Department of Gastroenterology, Children's Hospital of Nanjing Medical University, Nanjing 210008, Jiangsu Province, China.
World journal of gastroenterology
|June 20, 2025
概括
深度学习模型在儿科视频囊内镜 (VCE) 图像中准确地分类小肠病变. 这种人工智能工具提高了儿童的诊断效率和准确性.
科学领域:
- 医学成像分析分析 医学成像分析
- 在儿科中使用人工智能
- 胃肠病学 胃肠病学
背景情况:
- 视频囊内镜 (VCE) 是儿童小肠异常的非侵入性诊断工具.
- 手动审查VCE图像是耗时和劳动密集的.
- 开发自动化方法对于高效的VCE图像分析至关重要.
研究的目的:
- 开发和评估深度学习模型,用于小肠病变的自动分类.
- 通过使用儿科VCE图像来评估不同深度学习架构的性能.
主要方法:
- 对来自162名儿科患者的2298张VCE图像进行了回顾性分析.
- 培训和评估四种深度学习模型:DenseNet121,VGG-16,ResNet50和视觉转换器.
- 使用准确度,精度,回忆,F1得分和AU-ROC的性能评估,通过Grad-CAM可视化损伤.
主要成果:
- 丹斯网121和ResNet50实现了高分类性能,分别准确率为90.6%和90.5%.
- 对于DenseNet121和ResNet50的AU-ROC值分别为93.7%和93.4%.
- 在93名儿童中检测到异常病变,腹部疼痛是VCE的最常见迹象.
结论:
- 深度学习模型有效地对儿科VCE图像中的病变进行分类.
- 开发的AI工具有助于更准确的诊断.
- 这种方法提高了儿科胃肠病学的诊断效率.
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