一种深度学习方法与专家临床医生小组对后部循环心脏病发作的分类
Leon S Edwards1, Milanka Visser2, Cecilia Cappelen-Smith1
1Department of Neurology and Neurophysiology, Liverpool Hospital, Sydney, NSW, Australia; South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia; Ingham Institute for Applied Medical Research, Sydney, NSW, Australia.
NeuroImage. Clinical
|January 18, 2025
概括
使用计算机断层扫描输液 (CTP) 的深度学习模型在诊断后部循环梗塞 (POCI) 中超过了专家临床医生. 这种人工智能方法有望改善中风诊断,特别是在专家专业知识有限的地方.
科学领域:
- 神经学 神经学
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
背景情况:
- 后部循环梗塞 (POCI) 是一种常见的中风类型.
- 早期检测POCI是具有挑战性的传统成像,如非对比CT (NCCT) 和扩散权重MRI.
- 计算机断层扫描输液 (CTP) 显示出改善POCI诊断的潜力,但专家解释往往是有限的.
研究的目的:
- 开发和验证深度学习 (DL) 模型,用于使用CTP进行自动化POCI分类.
- 将DL模型的诊断性能与专家临床医生进行比较.
主要方法:
- 一个3D密度卷积网络 (DenseNet) 在来自国际中风输血注册表 (INSPIRE) 的541名患者 (1:3 POCI-to-reference比率) 的CTP解卷地图上接受了培训.
- 六位中风专家使用多模式CT (mCT) 数据独立对患者进行了分类.
- 模型和临床医生的表现被评估使用AUC,灵敏度,特异性,准确性和精度;临床医生的同意被评估使用Fleiss'Kappa统计.
主要成果:
- 经过审查所有mCT数据后,专家临床医生获得了最佳平均准确度 (AUC:0.81,灵敏度:0.65),单个AUC在0.73-0.86.6之间.
- 使用NCCT和延迟时间图的DenseNet模型,与最佳平均临床医生相比,实现了优异的性能 (AUC:0.87) 与增强的灵敏度 (0.77).
- DL模型的性能优势在临床医生之间各不相同,AUC的改善为0.01-0.14.
结论:
- 综合性CTP审查提高了临床医生的诊断性能和协议.
- 开发的DenseNet模型在POCI分类中显著超过了最佳临床医生的平均表现.
- 结合临床医生-DL方法可以提高诊断准确性和临床医生间的协议,可能支持资源有限的环境.
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