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深度学习方法用于使用骨光学单光子发射计算机断层扫描/计算机断层扫描在怀疑心脏粉症的患者心脏的自动细分

Abhijit Bhattaru¹, Chaitanya Rojulpote², Mahesh Vidula³

¹Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Cardiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA.

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology

|February 2, 2024

概括

深度学习准确地检测心肌吸收量,用于诊断心脏粉样性心肌病变 (ATTR-CM). 这种自动化工具可以通过SPECT/CT成像提高ATTR-CM的识别能力.

科学领域:

心脏病学心脏病学
放射学放射学是一门学科.
人工智能的人工智能

背景情况:

晶体氨酸心脏粉状心肌病 (ATTR-CM) 诊断依赖于确定心肌骨寻找吸收.
99mTc-pyrophosphate (PYP) 或基酸盐 (HDP) SPECT/CT是ATTR-CM评估的关键成像方式.

研究的目的:

开发和验证一个深度学习工具,用于ATTR-CM中自动检测心肌吸收.
与视觉评估相比,评估深度学习在分段和量化心肌吸收的准确性.

主要方法:

在CT扫描上使用2D U-net架构进行全心细分.
心肌吸收量通过心脏对血液池 (HBP) 的比率来量化.
深度学习模型的性能在初级队列 (n=77) 和验证队列 (n=93) 上进行了评估.

主要成果:

与基础真相相比,深度学习细分没有显著差异 (p=0.63).
自动化HBP比率量化差异化阳性和负心脏吸收 (3.1±1.4与1.4±0.2相比,p<0.01).
该模型在初级队列中达到100%的准确性,在验证队列中达到98%的准确性.

结论:

已经开发出一种非常准确的深度学习工具来诊断ATTR-CM.
该工具自动细分并识别出暗示ATTR-CM的心肌吸收.
这种自动化方法可以提高ATTR-CM的诊断效率和准确性.

关键词:

这就是为什么CTCTCTCTCTCT 心脏氨基粉症症的心脏粉症深度学习是一种深度学习.斯佩克特 (Spectre) 是一个运动场.

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