Automated Coronary Artery Calcium Scoring Using Convolutional Neural Networks: Enhancing Cardiovascular Risk Assessment in Chest CT Scans

Masab Mansoor, David J Grindem
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Abstract

Background: Coronary artery calcium (CAC) scoring is valuable for cardiovascular risk assessment but often time-consuming and subject to variability. This study aimed to develop and validate a convolutional neural network (CNN) model for automated CAC scoring in chest CT scans, potentially enhancing efficiency and accuracy. Methods: We utilized 10,000 chest CT scans from a public dataset, split into training (n=7,000), validation (n=1,500), and testing (n=1,500) sets. A 3D CNN model based on ResNet-50 was developed and trained for CAC detection and quantification. Performance was evaluated on the test set and compared to manual scoring by three experienced radiologists. Results: The CNN model achieved 93.7% accuracy in detecting CAC, with 87.4% sensitivity and 92.1% specificity for identifying clinically significant CAC (Agatston score >100) in the test set (n=1,500). The model showed strong correlation with manual CAC scores (r=0.89, p<0.001). Automated scoring reduced processing time by 78% compared to manual techniques, averaging 18.3 seconds per scan. The model demonstrated consistent performance across diverse patient demographics and CT types. In a subset of patients with follow-up data (n=500), the model's risk stratification was comparable to the Framingham Risk Score in predicting cardiovascular events (AUC 0.76 vs 0.74, p=0.09). Conclusions: The CNN-based automated CAC scoring system demonstrated high accuracy and efficiency, potentially enabling more widespread cardiovascular risk assessment in routine chest CT scans. Future research should focus on prospective validation and investigation of long-term patient outcomes when integrating this technology into clinical practice.
利用卷积神经网络自动进行冠状动脉钙化评分:加强胸部 CT 扫描中的心血管风险评估
背景:冠状动脉钙化(CAC)评分对心血管风险评估很有价值,但往往费时费力,且存在变异性。本研究旨在开发和验证一种卷积神经网络(CNN)模型,用于胸部 CT 扫描中的自动 CAC 评分,从而提高效率和准确性:我们利用了公共数据集中的 10,000 份胸部 CT 扫描,将其分为训练集(n=7,000)、验证集(n=1,500)和测试集(n=1,500)。开发并训练了基于 ResNet-50 的三维 CNN 模型,用于 CAC 检测和量化。对测试集的性能进行了评估,并与三位经验丰富的放射科医生的人工评分进行了比较:结果:CNN 模型检测 CAC 的准确率达到 93.7%,在测试集(n=1,500)中识别有临床意义的 CAC(Agatston 评分 >100)的灵敏度为 87.4%,特异度为 92.1%。该模型与人工 CAC 评分显示出很强的相关性(r=0.89,p<0.001)。与人工技术相比,自动评分减少了 78% 的处理时间,平均每次扫描时间为 18.3 秒。该模型在不同的患者人口统计学和 CT 类型中表现出一致的性能。在有随访数据的患者子集中(n=500),该模型的风险分层在预测心血管事件方面与弗雷明汉风险评分相当(AUC 0.76 vs 0.74,p=0.09):结论:基于 CNN 的 CAC 自动评分系统显示出很高的准确性和效率,有可能在常规胸部 CT 扫描中更广泛地进行心血管风险评估。未来的研究应侧重于前瞻性验证和调查将该技术融入临床实践时患者的长期疗效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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