基于X射线冠状动脉血管学的深度学习对冠状动脉狭窄的自动诊断
Chengyu Mao1, Huasu Zeng1, Kandi Zhang1
1Department of Cardiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Quantitative imaging in medicine and surgery
|November 10, 2025
概括
一个新的深度学习模型自动细分和分级X射线血管学中的冠状动脉狭窄. 这种人工智能工具有助于诊断冠状动脉疾病 (CAD) 和规划患者治疗.
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:
- 冠状动脉疾病 (CAD) 是全球主要的死亡原因,通常与动脉中的斑块积累有关.
- X射线冠心血管造影是诊断CAD的标准,但对狭窄症严重程度的图像解释具有挑战性.
- 现有的方法很难从血管图像中对冠状动脉狭窄症的严重程度进行一致的解释.
研究的目的:
- 开发和验证深度学习 (DL) 方法,用于冠状动脉狭窄的自动细分和分级.
- 为了提高X射线冠状动脉血管学中狭窄性评估的准确性和一致性.
- 提供一种帮助诊断冠状动脉疾病 (CAD) 和患者治疗计划的工具.
主要方法:
- 一个双输出深卷积神经网络 (CNN) 使用来自168名患者的383张血管图像被开发出来.
- 狭窄症的严重程度被手动标注为五个临床级别:非阻塞性 (1-49%),中间性 (50-70%),严重性 (71-95%),次总封闭性 (96-99%) 和总封闭性 (100%).
- DL模型进行了冠状动脉狭窄的自动细分和分级.
主要成果:
- 开发的模型实现了冠状动脉狭窄症细分和分级的高性能指标.
- 关键绩效指标包括0.92的平均交叉与欧盟 (IoU),0.95的子得分,0.93的精度和0.96.96的灵敏度.
- 该模型在识别临床显著的狭窄度 (71-100%) 中表现出强大的能力.
结论:
- 成功开发了一种基于图像的血管分析方法,使用DL进行X射线冠状动脉血管学.
- 该方法准确地定位和分级冠状动脉狭窄,特别是临界水平.
- 这种DL方法显示了增强CAD诊断和个性化治疗策略的潜力.
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