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实时人工智能的诊断性能,使用深度学习分析,对胆囊多性病变的内镜超声视频进行诊断

Young Hoon Choi¹, Jun Young Park², See Young Lee³

¹Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

Scientific reports

|December 8, 2025

概括

人工智能 (AI) 模型在分析内镜超声波 (EUS) 视频以诊断胆囊多时显示出高准确度. 这项技术可以改善对胆囊多的客观评估,提高诊断能力.

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:

内镜超声波 (EUS) 是有效的胆囊 (GB) 息肉诊断,但依赖于主观的解释.
人工智能 (AI) 已被应用于EUS静态图像,但其在EUS视频中用于GB聚评估的应用尚未探索.

研究的目的:

评估AI模型在分析EUS视频中的诊断性能,以评估GB多.
探索AI在从EUS视频数据分类GB多的应用.

主要方法:

使用了经过确认的GB息肉患者的EUS视频,分为训练和验证集.
分段模型 (注意U-Net,残留U-Net,DUCK网) 确定了多区域.
分类模型 (EfficientNet-B2,ResNet101,视觉变压器) 将多细胞分类为瘤或非瘤.

主要成果:

分段模型实现了高精度,注意力U-Net (0.998) 和DUCK Net (0.995) 在训练队列中表现最好.
在训练数据上,EfficientNet-B2表现出优异的分类性能 (准确率为0.957,AUC为0.991).
人工智能模型在验证数据集上保持了强的表现 (精度为0.879,回忆为0.968,F1得分为0.917).

结论:

人工智能模型在分析EUS视频以诊断胆囊多时显示出高准确度.
在EUS视频分析中AI的应用为客观和改进的胆囊多的评估提供了潜力.
需要进一步的前性验证,以在临床实践中证实这些发现.

关键词:

人工智能的人工智能是人工智能.诊断性表现诊断性表现内镜超声波视频的内镜超声波胆囊多是一种胆囊多.

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