Motivation

The early screening of cardiovascular diseases (CVD) can lead to effective treatment. Thus, accurate and reliable atherosclerotic carotid wall detection and plaque measurements are crucial. Current measurement methods are time-consuming and do not utilize the power of knowledge-based paradigms such as artificial intelligence (AI). We present an AI-based methodology for the joint automated detection and measurement of wall thickness and carotid plaque (CP) in the form of carotid intima-media thickness (cIMT) and total plaque area (TPA), a class of AtheroEdge™ system (AtheroPoint™, CA, USA).

Method

The novel system consists of two stages, and each stage comprises an independent deep learning (DL) model. In Stage I, the first DL model segregates the common carotid artery (CCA) patches from ultrasound (US) images into the rectangular wall and non-wall patches. The characterized wall patches are integrated to form the region of interest (ROI), which is then fed into Stage II. In Stage II, the second DL model segments the far wall region. Lumen-intima (LI) and media-adventitial (MA) boundaries are then extracted from the wall region, which is then used for cIMT and PA measurement.

Results

Using the database of 250 carotid scans, the cIMT error using the AI model is $0.0935\pm 0.0637$ mm, which is lower than those of all previous methods. The PA error is found to be $2.7939\pm 2.3702$ mm². The system's correlation coefficient (CC) between AI and ground truth (GT) values for cIMT is 0.99 (p < 0.0001), which is higher compared with the CC of 0.96 (p < 0.0001) shown by the earlier DL method. The CC for PA between AI and GT values is 0.89 (p < 0.0001).

Conclusion

A novel AI-based strategy was applied to carotid US images for the joint detection of carotid wall thickness (cWT) and plaque area (PA), followed by cIMT and PA measurement. This AI-based strategy shows improved performance using the patch technique compared with previous methods using full carotid scans.

中文翻译：

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用于联合测量颈动脉超声中动脉粥样硬化壁厚度和斑块负担的两阶段人工智能模型：一种用于心血管/中风风险评估的筛查工具。

动机

心血管疾病（CVD）的早期筛查可以导致有效的治疗。因此，准确和可靠的动脉粥样硬化颈动脉壁检测和斑块测量至关重要。当前的测量方法很耗时，并且没有利用基于知识的范例（例如人工智能（AI））的功能。我们提出了一种基于AI的方法，用于自动检测和测量壁厚和颈动脉斑块（CP），形式为颈动脉内膜中层厚度（cIMT）和总斑块面积（TPA），这是一类AtheroEdge™系统（美国加利福尼亚州AtheroPoint™）。

方法

新颖的系统包括两个阶段，每个阶段都包含一个独立的深度学习（DL）模型。在阶段I中，第一个DL模型将颈总动脉（CCA）斑块与超声（US）图像分离为矩形壁斑块和非壁斑块。将特征化的墙贴集成在一起以形成感兴趣区域（ROI），然后将其馈入II期。在阶段II中，第二个DL模型分割了远墙区域。然后从壁区域提取内膜内膜（LI）和外膜外膜（MA）边界，然后将其用于cIMT和PA测量。

结果

使用250个颈动脉扫描的数据库，使用AI模型的cIMT错误为 $0.0935\pm 0.0637$mm，低于所有以前的方法。发现PA错误是$2.7939\pm 2.3702$毫米²。cIMT的AI和地面真值（GT）值之间的系统相关系数（CC）为0.99（p <0.0001），这与早期DL方法显示的0.96（p <0.0001）的CC相比更高。AI和GT值之间的PA的CC为0.89（p <0.0001）。

结论

一种新颖的基于AI的策略已应用于颈动脉US图像，用于联合检测颈动脉壁厚（cWT）和斑块面积（PA），然后进行cIMT和PA测量。与以前使用全颈动脉扫描的方法相比，这种基于AI的策略显示了使用补丁技术的性能提高。