人工智能辅助的方法来评估小儿炎性肠病的肠壁厚度,使用肠道超声波图像
Logiraj Kumaralingam1, Kenneth Le May2, Van Bao Dang1
1Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada.
这项研究开发了一种人工智能 (AI) 工具,用于自动化肠道超声波 (IUS) 图像分析,用于儿科炎性肠病 (IBD). 人工智能准确地区分正常和异常病例,有助于早期检测和监测.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 胃肠病学 胃肠病学
背景情况:
- 肠道超声波 (IUS) 为诊断炎症性肠病 (IBD) 提供了内镜的非侵入性替代方案.
- 解释IBD标记器的IUS图像是耗时的,需要专门的专业知识.
- 自动化IUS分析可以提高儿科IBD的诊断效率和可访问性.
研究的目的:
- 开发和验证人工智能 (AI) 算法,用于使用IUS图像自动分析儿科IBD.
- 根据IUS发现,区分儿科IBD的正常和异常病例.
- 评估AI工具在测量肠壁厚度方面的准确性和可靠性.
主要方法:
- 从260名儿科患者的4565张IUS图像的数据集上训练了一种AI算法.
- 在AI训练图像子集中注释关键接口 (光线/粘膜,肌肉/血清).
- 计算了平均肠壁厚度 (BWT),并使用切割值评估了分类性能.
主要成果:
- 人工智能算法在分割IUS图像接口方面表现出强的表现.
- 在2毫米的BWT切线下,AI在3953张图像的测试组中实现了90.29%的灵敏度和93.70%的特异性.
- 人工智能方法与手动临床测量有很高的一致性 (类间相关性:0.942).
结论:
- 一种人工智能驱动的方法可以可靠地自动分析儿科IBD IUS图像.
- 这项技术为儿童IBD的早期检测,精确表征和疾病监测提供了有前途的工具.
- 自动化IUS分析有可能减少对侵入性手术和替代成像方式的需求.
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