在血管和干预放射学中基于成像的人工智能:叙述性审查
Haseeb Mukhtar1,2, Ali Ganjizadeh1,3, Ajay Misra1,3
1Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Journal of imaging informatics in medicine
|January 6, 2026
概括
人工智能 (AI) 在血管干预放射学 (VIR) 中显著有前途,用于图像分析和预测任务. 进一步的研究和合作对于将其负责任地纳入患者护理至关重要.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 人工智能在诊断放射学方面表现出色,但由于数据的变化,其VIR应用受限.
- 血管干预放射学 (VIR) 程序通常涉及复杂的成像分析.
研究的目的:
- 在程序前,程序内和程序后的VIR阶段审查AI应用.
- 用各种成像方式评估AI对患者护理的影响.
主要方法:
- 在PubMed,Embase和Web of Science的综合文献搜索.
- 根据成像方式 (CT,MRI,光学/DSA,超声波,X射线,多式) 和任务类型 (细分,检测,预测) 分类人工智能研究.
主要成果:
- 深度学习在解剖细分 (例如动脉瘤) 中实现了高精度 (0.82-0.962).
- 人工智能检测任务显示出高性能 (例如,内漏检测的准确性为95%,狭窄的AUC为0.97).
- 预测模型 (AUC > 0.90) 在EVAR,TEVAR,TACE,TARE后的临床评估结果中表现出色.
结论:
- 人工智能在VIR中展示了变革的潜力,特别是用于治疗反应的放射学和机器学习.
- 挑战包括数据限制,偏见和可解释性.
- 临床医生与人工智能专家的合作对于干预放射学安全有效的部署至关重要.
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