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在冠状动脉疾病 (CAD) 诊断中评估心肌输液成像技术和人工智能 (AI) 工具,通过多标准决策方法进行诊断
Hasan Erdagli1, Dilber Uzun Ozsahin2,3,4, Berna Uzun4,5
1Department of Biomedical Engineering, Near East University, Nicosia, Turkey.
Cardiovascular diagnosis and therapy
|January 10, 2025
概括
随机森林 (RF) AI和心血管磁共振成像 (CMR) 是诊断冠状动脉疾病 (CAD) 的最有效方法. 这项研究优先考虑了人工智能工具和MPI技术,以提高CAD诊断率.
科学领域:
- 心脏病学 心脏病学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因.
- 心肌输液成像 (MPI) 对于评估心脏功能和诊断冠状动脉疾病 (CAD) 至关重要.
- 人工智能 (AI) 整合到MPI等诊断方法中旨在提高患者的治疗结果.
研究的目的:
- 研究参数和性能指标如何影响MPI技术和CAD诊断人工智能工具的选择.
- 确定MPI和AI中最有效的方法,以提高CAD诊断率.
主要方法:
- 评估了机器学习 (ML) 模型 (随机森林,KNN,SVM,Naïve Bayes) 和卷积神经网络 (CNN) 模型 (InceptionV3,VGG16,ResNet50,DenseNet121).
- 评估核MPI技术 (PET,SPECT) 和非核MPI (CMR),使用特异性,灵敏度,辐射剂量,成本和持续时间等标准.
- 利用PROMETHEE多标准决策方法对AI工具和MPI技术进行分析和比较.
主要成果:
- 随机森林 (RF) 成为CAD诊断中SPECT MPI最有效的AI工具 (净流量:0.3778).
- 心血管磁共振成像 (CMR) 被确定为CAD诊断中最有效的MPI技术 (净流量:0.3666).
结论:
- 与核MPI技术相比,CMR表现出优越的性能.
- 尽管SPECT MPI的整体优势较低,但在与CAD诊断的有效射频算法集成时显示出潜力.
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