使用非对比计算机断层扫描对大动脉剖析进行YOLOv8的深度学习应用
Zheyu Tang1, Yuanquan Huang2, Shibing Hu3
1Department of Interventional and Vascular Surgery, The Third Affiliated Hospital of Nanjing Medical University (Changzhou Second People's Hospital), Changzhou, China.
概括
使用非对比计算机断层扫描 (CT) 的新型深度学习模型有效检测急性大动脉解剖 (AD),在速度和准确性方面表现优于放射科医生. 这种人工智能工具有望在临床环境中改善AD诊断.
科学领域:
- 医疗成像中的人工智能
- 心血管放射学
- 深度学习用于诊断支持
背景情况:
- 急性大动脉解剖 (AD) 是一种需要快速诊断的危急情况.
- 非对比计算断层扫描 (CT) 用于AD检测,但放射科医生的解释可能是可变和耗时的.
- 需要改进,高效,准确的阿尔茨海默病诊断工具.
研究的目的:
- 开发和验证可解释的YOLOv8深度学习模型,用于在非对比CT扫描上检测AD.
- 将AI模型的诊断性能和效率与放射科医生进行比较.
主要方法:
- 一项追溯研究涉及来自五所机构的1138个非对比CT扫描,分为培训,内部和外部验证队列.
- 在注释的CT图像上训练了一个可解释的YOLOv8深度学习模型.
- 使用AUC,灵敏度,特异性和推断时间来评估性能,并与血管干预放射科医生,一般放射科医生和住院医生进行比较. 用Grad-CAM进行解释.
主要成果:
- 在外部验证队列中,YOLOv8s模型表现出高的诊断性能,AUC为0. 970.
- 该模型的灵敏度 (0.976) 和特异性 (0.935) 与放射学家相比或更高.
- 该模型的推断时间 (3.47秒) 比放射科医生的平均解释时间 (25.32秒) 快得多.
结论:
- YOLOv8s深度学习模型可靠地检测非对比CT的AD,在速度和准确性方面超过了放射科医生.
- 这种人工智能工具有助于加强AD查,帮助临床决策,提高诊断质量.
- 该模型的可解释性得到Grad-CAM的证实,支持其临床实用性.
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