人工智能和光学相干断层扫描技术在人类动脉粥样硬化斑块自动特征化中的应用。
Artificial intelligence and optical coherence tomography for the automatic characterisation of human atherosclerotic plaques.
机构信息
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
出版信息
EuroIntervention. 2021 May 17;17(1):41-50. doi: 10.4244/EIJ-D-20-01355.
BACKGROUND
Intravascular optical coherence tomography (IVOCT) enables detailed plaque characterisation in vivo, but visual assessment is time-consuming and subjective.
AIMS
This study aimed to develop and validate an automatic framework for IVOCT plaque characterisation using artificial intelligence (AI).
METHODS
IVOCT pullbacks from five international centres were analysed in a core lab, annotating basic plaque components, inflammatory markers and other structures. A deep convolutional network with encoding-decoding architecture and pseudo-3D input was developed and trained using hybrid loss. The proposed network was integrated into commercial software to be externally validated on additional IVOCT pullbacks from three international core labs, taking the consensus among core labs as reference.
RESULTS
Annotated images from 509 pullbacks (391 patients) were divided into 10,517 and 1,156 cross-sections for the training and testing data sets, respectively. The Dice coefficient of the model was 0.906 for fibrous plaque, 0.848 for calcium and 0.772 for lipid in the testing data set. Excellent agreement in plaque burden quantification was observed between the model and manual measurements (R2=0.98). In the external validation, the software correctly identified 518 out of 598 plaque regions from 300 IVOCT cross-sections, with a diagnostic accuracy of 97.6% (95% CI: 93.4-99.3%) in fibrous plaque, 90.5% (95% CI: 85.2-94.1%) in lipid and 88.5% (95% CI: 82.4-92.7%) in calcium. The median time required for analysis was 21.4 (18.6-25.0) seconds per pullback.
CONCLUSIONS
A novel AI framework for automatic plaque characterisation in IVOCT was developed, providing excellent diagnostic accuracy in both internal and external validation. This model might reduce subjectivity in image interpretation and facilitate IVOCT quantification of plaque composition, with potential applications in research and IVOCT-guided PCI.
背景
血管内光学相干断层扫描（IVOCT）能够对体内斑块进行详细的特征描述，但视觉评估既耗时又主观。
目的
本研究旨在开发和验证一种使用人工智能（AI）对 IVOCT 斑块特征进行自动分析的框架。
方法
在核心实验室对来自五个国际中心的 IVOCT 拉回进行分析，对基本斑块成分、炎症标志物和其他结构进行注释。开发了一种具有编码-解码结构和伪 3D 输入的深度卷积网络，并使用混合损失进行训练。将提出的网络集成到商业软件中，以便在三个国际核心实验室的其他 IVOCT 拉回上进行外部验证，以核心实验室的共识作为参考。
结果
来自 509 次拉回（391 例患者）的注释图像被分为训练数据集的 10517 个和测试数据集的 1156 个横截面。模型在测试数据集上的纤维斑块、钙和脂质的 Dice 系数分别为 0.906、0.848 和 0.772。模型和手动测量在斑块负担定量方面表现出极好的一致性（R2=0.98）。在外部验证中，该软件正确识别了 300 个 IVOCT 横截面上的 598 个斑块区域中的 518 个，纤维斑块的诊断准确率为 97.6%（95%CI：93.4-99.3%），脂质为 90.5%（95%CI：85.2-94.1%），钙为 88.5%（95%CI：82.4-92.7%）。分析每段血管所需的中位数时间为 21.4（18.6-25.0）秒。
结论
开发了一种用于 IVOCT 中自动斑块特征分析的新型 AI 框架，在内部和外部验证中均具有出色的诊断准确性。该模型可能会减少图像解释的主观性，并促进 IVOCT 对斑块成分的定量，在研究和 IVOCT 指导的 PCI 中具有潜在的应用。
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