通过先进的深度学习技术,提高COVID-19患者的肺栓塞检测能力
Georgios Feretzakis1,2, Konstantinos Dalamarinis3, Dimitris Kalles1
1School of Science and Technology, Hellenic Open University, Patras, Greece.
Studies in health technology and informatics
|August 23, 2024
概括
这项研究表明,先进的AI,特别是YOLOv8模型,可以有效地检测COVID-19后患者的肺栓塞 (PE),使用CTPA扫描. 这种深度学习方法为改善临床环境中的诊断准确性提供了一个有希望的工具.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 肺部病理学 肺部病理学
背景情况:
- COVID-19增加了肺栓塞 (PE) 的发生率,造成了诊断挑战.
- 准确及时检测PE对于患者的治疗结果至关重要.
- 现有的诊断方法需要改进,特别是在流行病后的场景中.
研究的目的:
- 为了评估深度学习对后COVID-19患者增强PE检测的有效性.
- 评估Ultralytics YOLOv8模型在计算机断层扫描肺血管扫描 (CTPA) 扫描上的性能.
- 探索AI在提高PE诊断精度方面的潜力.
主要方法:
- 使用了来自25名患者的746张匿名CTPA图像的数据集.
- 超分析的YOLOv8物体检测模型在676张图像上进行了微调和训练.
- 该模型在70张图像上进行了验证,性能指标包括平均平均精度 (mAP) 和回忆.
主要成果:
- YOLOv8模型实现了0.683的平均平均精度 (mAP) 在0.50的IOU和0.246在0.50的IOU:0.95.
- 该模型表现出高诊断能力,最高精度为0.859,回忆率为0.815.
- 在200个时代中,培训时间大约为1.021小时.
结论:
- 像YOLOv8这样的深度学习模型显示出在COVID-19后患者中精确检测PE的巨大潜力.
- 由人工智能驱动的工具可以提高医学成像中的诊断能力.
- 这项研究有助于医疗保健创新和通过先进技术改善患者护理.
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