在非诊断性 (GB-RADS 0) 超声波患者中,基于深度学习的胆囊病变的分类
Pankaj Gupta1, Ruby Siddiqui1, Thakur D Yadav1
1PGIMER, Chandigarh, India.
Clinical and experimental hepatology
|April 28, 2025
概括
深度学习模型显示了在非诊断性超声波 (US) 图像上对胆囊病变进行分类的潜力. 虽然有希望,但在胆囊癌 (GBC) 检测中需要进一步的性能改进以获得临床应用.
科学领域:
- 医学成像医学成像
- 放射学中的人工智能
- 胆囊病理 胆囊病理
背景情况:
- 胆囊非诊断性超声波 (US) 在评估病变方面存在挑战.
- 胆囊因素,如结石,可以掩盖详细的美国评估.
- 对胆囊病变的准确分类对于患者管理至关重要.
研究的目的:
- 评估深度学习 (DL) 模型的诊断性能,用于对美国图像上的胆囊病变进行分类.
- 评估由于胆囊因素导致的非诊断性美国患者的DL模型.
- 为了比较各种DL架构,包括CNN,变压器和混合模型.
主要方法:
- 鉴定出一组由于胆囊因素导致的非诊断性US患者.
- 深度学习模型 (ResNet50,GBCNet,ViT,RadFormer,MedViT) 在公开的胆囊数据集 (GBCU) 上进行训练.
- 模型的性能在非诊断性美国图像 (GB-RADS 0) 上进行了测试,以将病变分类为良性或恶性.
主要成果:
- GBCNet和MedViT模型在检测胆囊癌 (GBC) 方面表现最好,AUC分别为0.709和0.714.
- 在检测良性胆囊病变方面,MedViT获得了最高的准确性 (73.1%).
- 该研究测试了来自26名患者的304张图像上的模型,其中26名患者患有未诊断的美国.
结论:
- 深度学习模型显示了对非诊断性胆囊患者分层的潜力.
- 目前的性能水平需要进一步提升,以获得广泛的临床采用.
- 基于AI的分类可能有助于管理未确定胆囊发现的患者.
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