基于大血管封闭性中风的入院CT血管造影,用于预测血栓切除术后结果的深度学习
Jakob Sommer1,2, Fiona Dierksen1, Tal Zeevi1,3
1Section of Neuroradiology, Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, United States.
Frontiers in artificial intelligence
|August 16, 2024
概括
深度学习模型使用计算机断层扫描血管学 (CTA) 和临床数据准确预测中风的结果. 这种自动化方法有助于在具有挑战性的临床场景中预测.
科学领域:
- 神经成像是一种神经成像.
- 人工智能在医学中的应用
- 脑卒中神经学 脑卒中神经学
背景情况:
- 计算机断层扫描血管造影 (CTA) 对于诊断大血管封闭 (LVO) 中风至关重要.
- 在LVO血栓切除术后预测3个月的结果对于患者管理至关重要.
- 目前的结果预测方法在某些临床情况下可能受到限制.
研究的目的:
- 开发和验证自动化的深度学习模型,用于预测前循环LVO中风患者的3个月结果.
- 用不同的输入数据组合来评估模型的性能:单独CTA,CTA包含治疗信息,CTA包含治疗和临床数据.
主要方法:
- 在591名患者的数据集上培训了端到端的深度学习管道,分为培训/验证和独立测试集.
- 使用预训练的ResNet-50 3D卷积神经网络 (MedicalNet) 与CTA预处理.
- 对比的模型包括"CTA"图像",CTA + 治疗" (血栓切除时间,再输血成功) 和"CTA + 治疗 + 临床" (年龄,性别,NIHSS).
主要成果:
- 整体模型仅在CTA方面就实现了0.70的曲线下的面积 (AUC).
- 结合治疗和临床数据的模型显示性能有所改善,AUC分别为0.79和0.86.
- 使用"治疗+临床"数据的后勤回归模型也实现了0.86.6的高AUC.
结论:
- 证明了自动化深度学习模型的可行性,用于从入院CTA和血栓切除术后数据中预测中风结果.
- 这些模型可以在远程医疗转移和有通信障碍的情况中显著帮助预测.
- 临床和治疗数据的整合提高了对患者结果的预测准确性.
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