基于深度学习的自动胰腺细分在慢性胰腺炎患者的CT扫描上
Surenth Nalliah1, Esben Bolvig Mark2, Marjolein Henrieke Liedenbaum3
1Radiology Research Center, Department of Radiology, Aalborg University Hospital, Aalborg, Denmark; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark.
European journal of radiology
|May 23, 2025
概括
一个人工智能 (AI) 模型在慢性胰腺炎 (CP) 患者的计算机断层扫描 (CT) 中准确地细分胰腺. 这种人工智能工具在临床应用方面表现有前途,在各种数据集中表现出强大的性能.
科学领域:
- 放射学和医学成像学 医学成像学
- 人工智能在医学中的应用
- 计算病理学计算病理学
背景情况:
- 由于复杂的解剖学变异,慢性胰腺炎 (CP) 存在诊断挑战.
- 精确的胰腺细分对于评估疾病严重程度和治疗计划至关重要.
- 目前的细分方法可能缺乏复杂的胰腺解剖学所需的精度.
研究的目的:
- 开发和验证基于人工智能的模型,用于使用计算机断层扫描 (CT) 扫描精确的胰腺细分.
- 评估模型在区分健康受试者和慢性胰腺炎患者的表现.
- 调查患者特异性因素,如内脏脂肪和胰腺体积对细分精度的影响.
主要方法:
- 使用了550张CT扫描 (224张CP,80张健康,246张CP来自阿尔堡,伯根和NIH) 的多中心数据集.
- 在开发AI细分模型时使用nnU-Net架构.
- 使用Sørensen-Dice指数量化性能,并在内部和外部测试集上进行验证.
主要成果:
- 人工智能模型实现了高细分精度,Dice的得分为0.85 ± 0.08 (阿尔堡),0.79 ± 0.19 (伯根) 和0.79 ± 0.18 (NIH).
- 在细分精度 (迪斯分数),内脏脂肪面积 (r=0.45) 和胰腺体积 (r=0.53) (p < 0.0001) 之间观察到正相关性.
- CT参数没有显著影响模型性能 (p > 0.07).
结论:
- 开发的AI模型在慢性胰腺炎患者和健康人群中显示出胰腺细分的高准确性和稳定性.
- 该模型在不同临床场所和CT扫描仪上的一致性能表明它适合常规临床应用.
- 人工智能驱动的细分为改善慢性胰腺炎的诊断和治疗管理提供了一个有希望的工具.
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