一个新开发的基于深度学习的系统,用于在双气球肠镜检查期间自动检测和分类小肠病变
Yijie Zhu1,2,3, Xiaoguang Lyu1,2,3, Xiao Tao1,2,3
1Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
BMC gastroenterology
|January 3, 2024
概括
一个新的计算机辅助诊断 (CAD) 系统在双气球肠镜检查 (DBE) 过程中改善了小肠病变的检测. 这种人工智能工具帮助内镜医生,特别是新手,识别和分类异常,减少错过诊断.
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:
- 双气球肠镜 (DBE) 对于诊断和治疗小肠疾病至关重要.
- 由于人类的监督或有限的经验,存在DBE假阴性结果的可能性.
- 需要开发自动诊断工具来提高准确性.
研究的目的:
- 开发计算机辅助诊断 (CAD) 系统,用于在DBE期间自动检测和分类小肠异常.
- 提高诊断准确度,减少DBE程序中的错误率.
主要方法:
- 在5201张图像上训练了一种检测模型,在3021张DBE程序图像上训练了一种分类模型.
- 模型在独立的测试组中评估了1318个正常和915个异常图像,以及65个视频.
- 性能与8名内镜师进行了比较,专家的共识作为基本真相.
主要成果:
- 检测模型在图像测试组中实现了92%的灵敏度和0.947 AUC.
- 该分类模型在图像测试集上显示了86%的准确性.
- 在视频分析中,CAD系统 (85%的准确度) 超过了内镜 (77%±6%),证明优于新手和与专家相比.
结论:
- 为DBE成功开发了一个实时CAD系统 (ENDOANGEL-DBE),性能强.
- 该系统显示出在临床实践中有助于内镜师,特别是新手的潜力.
- 实施这种CAD系统可能会显著降低错过小肠病变的发生率.
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