小肠囊内镜检查和使用人工智能开放访问数据库:东南欧-人工智能项目
Akihito Yokote1, Junji Umeno1, Keisuke Kawasaki1
1Department of Medicine and Clinical Science Graduate School of Medical Science Kyushu University Fukuoka Japan.
这项研究开发了一个人工智能 (AI) 模型用于小肠囊内镜 (CE) 图像分析. 人工智能显示出有前途的潜力,可以协助在CE检查期间检测病变.
科学领域:
- 胃肠病学 胃肠病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 小肠囊内镜 (CE) 产生了大量的图像数据,这给手工解释带来了挑战.
- 人工智能 (AI) 提供了在CE中自动化图像分类的潜力.
- 开发有效的CE人工智能模型需要强大的数据集和验证的算法.
研究的目的:
- 创建一个全面的小肠CE图像数据集.
- 开发和验证一个物体检测AI模型,用于识别CE中的疾病病变.
- 探索AI在协助小肠CE解释方面的挑战和潜力.
主要方法:
- 从523个小肠CE手术中提取了18481张图像.
- 标注了23,033个疾病病变的12,320张图像,创建了一个带有正常图像的数据集.
- 使用YOLO v5开发了一个对象检测AI模型,并验证了它的性能.
主要成果:
- 人工智能模型在12种注释类型中实现了大约91%的灵敏度.
- 在某些注释中实现了0.98的接收器运行特征曲线下的高面积.
- 根据具体的注释类型,证明了可变检测质量.
结论:
- 使用YOLO v5的物体检测AI模型显示了小肠CE中有效阅读辅助的潜力.
- 开发的数据集,人工智能模型权重和演示是公开可用于进一步研究的.
- 人工智能模型的持续改进预计将用于增强小肠CE分析.
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