一种新的方法来克服AI的黑盒子在结肠镜检查中的光学诊断
Youmin Shin1,2, Jung Ho Bae3, Jung Kim4
1Department of Transdisciplinary Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
Scientific reports
|July 2, 2025
概括
一种可解释的人工智能 (AI) 方法,niceAI,使用放射学和深度特征准确地分类结直肠多. 这种方法提高了光学诊断的透明度,提高了结肠镜检查的准确性和临床决策.
科学领域:
- 胃肠病学 胃肠病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 在结肠镜检查期间对结肠直肠病变进行准确的实时光学诊断对于降低病理学评估成本和防止不必要的程序至关重要.
- 区分瘤与非瘤聚体有助于临床决策和患者管理.
研究的目的:
- 开发一种可解释的人工智能 (AI) 方法,命名为 niceAI,用于分类超塑性和腺瘤结肠直肠多.
- 为了提高人工智能驱动的光学诊断在结肠镜中的透明度和可解释性.
主要方法:
- 一个多步骤的过程,涉及特征提取 (放射学,颜色,深度学习) 和使用斯皮尔曼相关性分析进行选择.
- 选择深度特征与窄带成像技术的整合 国际结直肠内镜分级,以提供可解释的诊断输出.
- 通过将2048个深度特征减少到14个,并选择24个放射性特征来开发niceAI.
主要成果:
- niceAI模型实现了高诊断性能:曲线下的面积 (AUC) 为0.946,精度为0.883,灵敏度为0.888,特异性为0.879.
- 该系统的准确性与现有的深度学习模型相提并论,同时提供了更好的解释性.
- 没有选择颜色特征,而放射学和深度学习特征证明对分类具有重要意义.
结论:
- 这种新的niceAI系统有效地结合了放射学和深度特征,用于准确和可解释的结直肠多的光学诊断.
- 这种方法弥合了人工智能预测和临床评估之间的差距,为内镜医生提供了一个实用的工具.
- 开发的方法有可能提高诊断准确度,并在结肠镜检查期间实时支持临床决策.
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