开发一种人工智能工具,用于检测炎症性肠病中的结直肠病变
Daniela Guerrero Vinsard1, Jeffrey R Fetzer1,2, Upasana Agrawal1,3
1Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA.
iGIE : innovation, investigation and insights
|February 6, 2026
概括
一种新的人工智能模型显著改善了炎症性肠病 (IBD) 患者的结直肠病变检测. 这种计算机辅助检测 (CADe) 工具提高了灵敏度和准确性,有助于早期预防癌症.
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:
- 炎症性肠病 (IBD) 患者患结直肠癌的风险增加.
- 早期检测生殖不良症对于预防IBD患者结肠直肠癌至关重要.
- 人工智能 (AI) 显示出增强内镜病变检测的潜力.
研究的目的:
- 开发和评估第一个计算机辅助检测 (CADe) 模型,专门用于IBD患者的结直肠病变.
- 评估重新训练的人工智能模型在IBD中检测生殖不良的性能.
- 为了提高结直肠癌查在IBD的准确性.
主要方法:
- 在IBD病变图像上测试了先前存在的CADe模型.
- 通过使用1266张高清白光内镜 (HDWLE) 和426张IBD相关病变的染色内镜图像,重新训练了CADe模型.
- 病变根据组织病理学,大小,形态和周围炎症进行了注释;模型的表现在重新训练之前和之后进行了评估.
主要成果:
- 经过重新训练的IBD-CADe模型在HDWLE图像上实现了95.1%的灵敏度和96.8%的准确性.
- 染色内镜图像的性能指标包括67.4%的灵敏度和77.8%的精度.
- 该模型在各种损伤大小中表现出高灵敏度,并在特定的巴黎分类类型上表现最好.
结论:
- 开发的IBD-CADe模型在检测IBD患者的结直肠病变方面取得了重大进展.
- 这种人工智能工具是开发先进的内镜工具的第一步,用于IBD中发育不良的检测.
- 这些发现表明,人工智能可以显著改善IBD患者的内镜监测.
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