人工智能量化和经验丰富的读者计算机断层扫描分析,以区分正常的冠状动脉与最小和轻度病变的冠状动脉:早期现实世界的兼容性研究
Amr Idris1, Mahdi Hurreh2, Thomas Knickelbine2,3
1University of Kentucky-King's Daughters Medical Center, 2201 Lexington Ave, Ashland, KY, 41101, USA. Amr.idris@kdmc.kdhs.us.
The international journal of cardiovascular imaging
|February 28, 2025
概括
人工智能定量计算断层扫描 (AI-QCT) 分析显示,与正常的冠状动脉相比,最小和轻度病变的冠状动脉中斑块体积显著增加. 在冠状动脉计算机断层扫描 (CCTA) 中,AI-QCT总斑块体积切线<12.3 mm3有效区分正常与患病的动脉.
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:
- 在冠状动脉计算机断层扫描 (CCTA) 上区分正常与患病的冠状动脉对于患者管理至关重要.
- 使用人工智能定量计算断层扫描 (AI-QCT) 检测亚临床动脉样硬化可能会提高诊断准确度.
研究的目的:
- 评估AI-QCT在区分正常冠状动脉与在CCTA上患有最小或轻度疾病的冠状动脉的能力.
- 确定最佳的AI-QCT斑块体积值,以区分正常与患病的冠状动脉.
主要方法:
- 对411名因疑似冠状动脉疾病接受CCTA和AI-QCT的患者的回顾性分析.
- 冠状动脉根据读者解释和评分被归类为正常,最小或轻度疾病.
- AI-QCT全斑块体积在不同类别中进行比较;用于找到最佳值的Youden指数和AUC分析.
主要成果:
- AI-QCT表明,总斑块体积和非化斑块体积从正常到最小到轻度疾病类别逐步显著增加 (p < 0.001).
- 总斑块体积值<12.3mm3确定了80.4%的正常病例,并排除了77.3%的最小/轻度疾病病例,AUC为0.86.
- 在评估冠状动脉疾病严重程度时,AI-QCT显示出与读者解释有希望的一致性.
结论:
- AI-QCT有效地区分正常的冠状动脉与人类读者评估的最小和轻度疾病的冠状动脉.
- 一个AI-QCT总斑块体积<12.3 mm3可以作为可靠的初始切断点来识别CCTA上的正常冠状动脉.
- AI-QCT有可能提高CCTA在诊断冠状动脉疾病中的解释.
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