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一般目的深度学习减弱校正提高了SPECT MPI的诊断准确性:一个多中心研究

Aakash D Shanbhag¹, Robert J H Miller², Mark Lemley³

¹Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA; Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA.

JACC. Cardiovascular imaging

|August 8, 2025

概括

深度学习 (DL) 生成的合成SPECT图像改善了阻塞性冠状动脉疾病 (CAD) 的预测. 这种新的方法提高了SPECT心肌输液成像 (MPI) 的准确性,无需额外的设备或辐射.

科学领域:

心血管成像 - 心血管成像
人工智能在医学中的应用
放射学放射学是一门学科.

背景情况:

单光子发射计算断层扫描 (SPECT) 心肌 perfusion imaging (MPI) 依赖基于CT的减弱校正 (AC) 进行诊断准确.
深度学习 (DL) 通过生成合成AC图像提供了一个潜在的替代方案.

研究的目的:

评估DL生成的合成SPECT图像是否可以提高传统SPECT MPI的准确性.
评估基于DL的AC (DeepAC) 对阻塞性冠状动脉疾病 (CAD) 的诊断性能.

主要方法:

一个DL模型 (DeepAC) 被开发和训练在一个多中心队列4894名患者.
外部验证是在两个队列中进行的,共计来自临床试验的1,066名患者.
使用总 perfusion deficit (TPD) 评估了阻塞性CAD的诊断准确性,并与非减弱校正 (NC) 和基于CT的AC (AC) 进行了比较.

主要成果:

在第一个外部队列 (P < 0.001) 中,DeepAC TPD显示了与NC TPD (0.73) 相比,接收器操作特征曲线 (AUC) 下的区域较大 (0.77).
在第二个外部队列中,DeepAC的定量得分与实际的AC得分相比,与NC的一致性更好.
该模型在多个站点和患者组中得到验证.

结论:

在多中心外部队列中,DeepAC显著提高了阻塞性CAD的预测性能.
这种基于DL的方法可以提高传统SPECT系统的设施的诊断准确性.
DeepAC为基于CT的AC提供了一个有价值的替代方案,而不增加设备,成像时间或辐射暴露.

关键词:

斯佩克特 (Spectre) 是一个运动场.人工智能的人工智能是人工智能.减弱纠正的纠正减弱纠正.深度学习是一种深度学习.心肌 perfusion 影像成像,用于心肌 perfusion 的成像.

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