人工智能支持偶发性质的MRI成像:早期健康技术评估
Alexander W Marka1, Johanna Luitjens2, Florian T Gassert3
1Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, School of Medicine, Technical University of Munich, Institut für diagnostische und interventionelle Radiologie, Ismaninger Str. 22, 81675, Munich, Germany. alexander.marka@tum.de.
European radiology
|February 22, 2024
概括
整合人工智能 (AI) 与MRI用于病变分化,显示了潜在的成本效益. 人工智能辅助的方法可以降低成本,同时保持偶然质量的诊断质量.
科学领域:
- 放射学 放射学是一门学科.
- 卫生经济学 卫生经济学
- 人工智能的人工智能
背景情况:
- 偶然的病变需要准确地区分良性和恶性类型.
- 磁共振成像 (MRI) 是评估病变的关键工具.
- 整合人工智能等先进技术可能会改善诊断工作流程.
研究的目的:
- 评估人工智能辅助系统的成本效益,以区分MRI上的偶然病变.
- 将标准的MRI策略与人工智能增强的MRI策略进行比较.
主要方法:
- 开发了一个决策模型来估计MRI和MRI+AI策略的质量调整寿命年 (QALYs) 和终身成本.
- 模型参数来源于现有文献,支付意愿 (WTP) 门为100,000美元/QALY.
- 进行了敏感性分析,以评估模型的稳定性和人工智能系统成本的影响.
主要成果:
- 核磁共振+人工智能策略的结果是,与单独核磁共振 (8054美元) 相比,平均总成本略低 (7939美元).
- 这两种策略的效果相似 (MRI+AI的QALY为8.77,MRI的QALY为8.76).
- 发现MRI+AI战略在经济上占主导地位,其强大的增量成本效益比率 (ICER) 低于WTP值.
结论:
- 人工智能辅助的MRI显示出作为一种成本效益高的方法来区分偶然的病变的潜力.
- 在病变的诊断工作中,人工智能的额外使用可能会降低成本.
- 需要进一步的研究来验证这些发现,并将AI纳入常规临床实践.
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