乳腺癌查中的人工智能:整合策略的系统审查和元分析
Eloïse Sossavi1, Catherine Roy1, Sébastien Molière1
1Radiology Department, Hautepierre Hospital, Strasbourg University Hospital, Strasbourg, France.
European journal of radiology open
|January 22, 2026
概括
乳腺癌查中的人工智能 (AI) 与癌症检测中的人类双重阅读相匹配. 使用人工智能的分类模型显著减少了放射科医生的工作量,并且在不影响灵敏度的情况下进行了回忆.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 瘤学和癌症查 癌症查
背景情况:
- 有组织的乳腺癌查计划通常由放射科医生进行双重阅读,以提高癌症检测率.
- 人工智能 (AI) 的整合为优化查工作流程和提高诊断准确性提供了潜在的途径.
- 评估人工智能增强与传统双重阅读的比较性能对于临床实施至关重要.
研究的目的:
- 为了比较人工智能增强与传统双重阅读在有组织的乳腺癌查中的有效性.
- 评估人工智能集成对癌症检测率 (CDR),召回率和放射科医生工作量的影响.
- 分析不同的AI集成模型,包括独立的第二读者,分类和并发重叠.
主要方法:
- 2017年至2024年间发表的13项研究 (130万个屏幕) 的系统审查和随机效应元分析.
- 研究包括商业或研究人工智能嵌入数字造乳镜或断层合成程序,至少有10,000个屏幕或100种癌症.
- 对人工智能增强的风险比率 (RRs) 和对CDR,召回和工作负载指标的双重读数进行了计算.
主要成果:
- 与双重阅读相比,人工智能增强的协议实现了可比的癌症检测率 (RR 1.01) 和召回率 (RR 1.00) 没有显著变化.
- 基于选的AI模型保持了CDR (RR 1.02),同时减少了11%的召回 (RR 0.89),并减少了44%至70%的初始读数.
- 独立阅读器人工智能模型保持了CDR (RR 0.98),但显示了可变的回忆效应 (RR 1.12),受仲裁逻辑和值的影响.
结论:
- 人工智能集成可以在乳腺癌检测性能方面与传统的双重阅读实现平价.
- 人工智能对工作流程效率的影响高度依赖于集成模型;分类方法提供了显著的工作量和召回减少.
- 未来的人工智能在乳房造影查中的实施应优先考虑工作流的优化,利用工作流意识的指标和潜在的门验证.
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