基于深度学习的病变检测和小肠克罗恩病的严重程度分级在双气球内镜图像上的双气球内镜图像上
Wanqing Xie1, Jing Hu2, Pengcheng Liang3
1Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei, China; Beth Israel Deaconess Medical Center, Harvard Medical School, Harvard University, Boston, Massachusetts, USA.
Gastrointestinal endoscopy
|December 8, 2023
概括
一个人工智能模型使用双气球内镜 (DBE) 图像准确检测了小肠克罗恩病 (CD) 病变和分级. 这种人工智能方法为改善CD管理提供了客观的评估.
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:
- 双气球内镜 (DBE) 对于诊断小肠克罗恩病 (CD) 是至关重要的.
- 不准确的病变检测和内镜医生的主观评估可能导致错误诊断和低于最佳的CD管理.
- 需要客观和准确的方法来评估小肠CD.
研究的目的:
- 开发和评估人工智能 (AI) 模型,以准确检测和客观评估小肠CD.
- 提高诊断准确度,完善克罗恩病的疾病管理策略.
主要方法:
- 在来自628名患者的28155张DBE图像上训练了一种深度学习模型 (EfficientNet-b5).
- 图像被专家胃肠病学家标记,最终决定由至少两名内镜医生做.
- 人工智能模型被训练来检测,非炎症性狭窄症和炎症性狭窄症,并对的表面,尺寸和深度进行分级.
主要成果:
- 人工智能模型实现了很高的检测准确率:的96.3%,非炎症性狭窄的95.7%,炎症性狭窄的96.7%.
- 对于的分类,该模型的平均准确率为87.3% (表面),87.8% (尺寸) 和85.2% (深度).
- 人工智能模型的性能与内镜评估进行了比较.
结论:
- EfficientNet-b5人工智能模型在检测CD病变和分级DBE图像中的时表现出高准确度.
- 这种人工智能工具提供专家级准确性和客观评估,有可能优化小肠CD的临床治疗计划.
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