数字双胞胎和人工智能技术用于评估IA型内
Sungsin Cho1, Hyangkyoung Kim2, Jinhyun Joh1
1Department of Surgery, Kyung Hee University Hospital at Gangdong, Kyung Hee University School of Medicine, Seoul 05278, Republic of Korea.
Bioengineering (Basel, Switzerland)
|January 28, 2026
概括
使用数字双胞胎的新人工智能模型准确地预测了内血管动脉瘤修复 (EVAR) 中的IA型内泄漏风险. 这种工具提供了100%的负预测值,提高了患者的安全性和个性化治疗计划.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
- 血管外科 血管外科
背景情况:
- 内血管动脉瘤修复 (EVAR) 是腹腔大动脉动脉瘤的标准.
- 类型IA内皮漏洞是一种严重的并发症,由近位密封不充分引起.
- 目前使用静态成像的规划方法无法预测动态密封失效.
研究的目的:
- 验证一种新的以物理为基础的数字双胞胎和人工智能模型,用于预测IA型内泄漏风险.
- 将人工智能模型的预测准确度与传统的静态规划方法进行比较.
主要方法:
- 追溯验证研究与15名EVAR患者 (5名IA型内孔,10名没有).
- 创建患者特定的数字双胞胎来模拟支架移植部署和生物力学.
- 后勤回归人工智能模型处理了超过16000个生物力学测量,以生成Endoleak风险指数 (ERI).
- 与倾向分数匹配的传统规划组 (n=45) 相比,ERI预测性能的比较.
主要成果:
- 人工智能模型的ERI在内泄阳性组 (0.85±0.10) 与内泄阴性组 (0.39±0.11) (p=0.011) 中显著更高.
- 数字双胞胎/人工智能模型实现了80%的准确性和0.85.5的AUC.
- 该模型证明了IA型内皮病的100%灵敏度和100%负预测值.
- 传统的规划只能达到51.1%的准确度和AUC为0.54.
结论:
- 由人工智能驱动的数字双胞胎框架准确地预测了EVAR后的IA类型的漏洞风险.
- 与静态测量相比,ERI提供了定量优势,作为一个可靠的安全工具,具有100%的NPV.
- 这项技术通过识别高风险解剖学并最大限度地减少并发症,推进了个性化的EVAR规划.
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