使用深度学习对先天性心脏病产前诊断的成本效益分析
Gary M Ginsberg1,2, Lior Drukker3,4, Uri Pollak5,6
1Braun School of Public Health, Hebrew University, Jerusalem, Israel. gmginsberg@gmail.com.
Cost effectiveness and resource allocation : C/E
|May 21, 2024
概括
对于产前超声波 (美国) 的深度学习 (DL) 显著改善了先天性心脏病 (CHD) 的检测,提供了一个非常具有成本效益的解决方案. 这种先进的技术提高了诊断的准确性,并降低了长期的医疗保健成本.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 公共卫生 公共卫生
背景情况:
- 在产前阶段发现先天性心脏病 (CHD) 对于及时干预至关重要.
- 产前超声波 (US) 是一种标准的查工具,但其在检测心血管疾病方面的有效性可能有限.
- 深度学习 (DL) 提出了一种新的技术方法,以提高美国产前诊断心血管疾病的能力.
研究的目的:
- 评估将DL纳入产前超声波查中用于CHD的成本效益.
- 为考虑采用DL-US技术的决策者提供经济数据.
- 为了比较DL-US与美国常规查的诊断性能和经济影响.
主要方法:
- 使用电子表格模型进行了成本效益分析 (CUA).
- 该模型结合了人口统计,经济,流行病学和卫生服务利用数据.
- 增量成本效益比率 (CUR) 是通过比较DL-US与US加脉冲氧计 (POX) 来计算的.
主要成果:
- 与传统的美国相比,DL-US表现出显著更高的敏感性 (95%与58.1%相比).
- 通过DL-US预计将增加1,204个QALY,而成本在很大程度上被减少的治疗费用所抵消.
- 该技术被认为是"非常具有成本效益"的,每QALY1720美元,在许多场景中占据了传统方法的优势.
结论:
- DL-US是一种可行的,至少是成本有效的补充,用于产前心血管疾病查.
- 这项研究支持DL-US在改善产前护理诊断结果和资源配置方面的潜力.
- 进一步的CUA计算表明DL-US在临床实践中的采用有很大的潜力.
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