深度学习使得在印度北部基于超声波检测胆囊癌:一项前性诊断研究
Pankaj Gupta1, Soumen Basu2, Pratyaksha Rana1
1Department of Radiodiagnosis and Imaging, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India.
The Lancet regional health. Southeast Asia
|May 17, 2024
概括
一个新的深度学习 (DL) 模型可以使用腹部超声波 (US) 检测胆囊癌 (GBC),其准确性与放射科医生相美. 这种人工智能工具有望改善GBC诊断,特别是在具有挑战性的情况下.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 胆囊癌 (GBC) 是具有攻击性且难以诊断的,原因是图像特征与良性病变重叠.
- 准确及时诊断GBC对于有效治疗和改善患者的结果至关重要.
研究的目的:
- 开发和验证深度学习 (DL) 模型,用于使用腹部超声波 (美国) 自动检测GBC.
- 将DL模型的诊断性能与经验丰富的放射科医生进行比较.
主要方法:
- 一个基于二级聚合的多尺度DL分类器在美国的胆囊病变患者的数据上进行了训练和验证.
- 在一个单独的测试队列上评估了DL模型的性能,并与两个放射科医生的评估进行了比较.
主要成果:
- 在检测GBC时,DL模型实现了高灵敏度 (92.3%) 和AUC (0.887),与放射科医生相比.
- DL方法表现出强大的性能,即使在胆结石或小病变等具有挑战性的因素下也是如此.
- 对于壁画加厚的DL敏感性,GBC超越了一个放射科医生,尽管特异性较低.
结论:
- 深度学习模型显示,通过美国的GBC检测,放射科医生的诊断性能与放射科医生的诊断性能相当.
- 需要进一步的多中心研究才能充分实现DL在GBC诊断中的潜力.
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