为了充分整合可解释的人工智能在结肠囊内镜的途径
Esmaeil S Nadimi1, Jan-Matthias Braun2, Benedicte Schelde-Olesen3,4
1Applied AI and Data Science (AID), Maersk Mc-Kinney Moller Institute, Faculty of Engineering, University of Southern Denmark, Odense, Denmark. esi@mmmi.sdu.dk.
Scientific reports
|February 18, 2025
概括
人工智能 (AI) 通过自动检测和鉴定多体,显著改善结肠囊内镜 (CCE). 这种AI整合提高了诊断准确度,使CCE更接近光学结肠镜检查结肠直肠疾病的标准.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 结肠囊内镜 (CCE) 显示出结肠直肠疾病诊断的前景,但由于手动图像分析的挑战,其落后于光学结肠镜 (OC).
- 目前的局限性包括肠道准备不良,后勤问题以及耗时的CCE图像手动审查.
- 整合人工智能旨在通过自动化关键图像处理任务来弥合这一差距.
研究的目的:
- 开发和评估一个全面的人工智能系统,用于自主检测,定位,表征和大小估计CCE中的发现.
- 提高CCE分析的效率和准确性,促进其常规临床整合.
- 通过准确识别显著的结直肠多,减少不必要的后续程序.
主要方法:
- 开发可解释的深度神经网络 (DNN),用于在CCE图像序列中检测,表征和大小估计.
- 在广泛的,未增强的图像数据集上训练和验证算法,包括正常的粘膜和各种多类型.
- 实施一个多阶段的AI管道,用于自主图像处理和数据集成到CCE工作流.
主要成果:
- 聚合物检测DNN实现了高灵敏度 ([公式:见文本]) 和特异性 ([公式:见文本]),具有出色的负预测值 ([公式:见文本]).
- 鉴定DNN准确地将多细胞分类为瘤或非瘤,具有[公式:查看文本]的敏感性和[公式:查看文本]的特异性.
- 尺寸估计DNN在聚合物细分中显示出高精度 ([公式:参见文本]).
结论:
- 可解释的人工智能可以自动化CCE图像分析的关键方面,显著提高诊断性能.
- 这种由人工智能驱动的方法使CCE向临床实践的无整合迈进,与OC相似.
- 自动化分析减少了手动审查的负担,并通过确定临床上重要的发现来帮助优化患者管理.
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