商业人工智能与放射科医生相比:基于大人口的数字乳腺学和图莫合成查乳腺学队列中的NPV和回忆率
Iris E Chen1, Melissa Joines1, Nina Capiro1
1Department of Radiology, University of California, Los Angeles, 200 UCLA Medical Plaza, Los Angeles, CA, 90095.
AJR. American journal of roentgenology
|September 3, 2025
概括
人工智能 (AI) 在乳房图解读方面表现出潜力,实现了高负预测值 (NPV),但与放射科医生相比,回忆率增加. 调整人工智能值可以检测更多的癌症,
科学领域:
- 放射学和医学成像
- 医疗保健中的人工智能
- 进行乳腺癌查
背景情况:
- 人工智能 (AI) 可以通过识别负面查检查来简化乳房镜检查,使放射科医生能够专注于高风险病例.
- 在临床乳腺癌查工作流程中评估AI与放射科医生的性能至关重要.
研究的目的:
- 将人工智能系统的性能与放射科医生进行比较,使用数字乳房影像 (DM) 和数字乳房断层合成 (DBT) 来分类查乳房影像.
- 分析不同积极分类门的AI性能,重点关注负预测值 (NPV) 和回忆率.
主要方法:
- 来自Athena乳腺健康网络的DM和DBT查检查的回顾性分析 (2010年1月 - 2019年12月).
- 放射科医生的解释与AI分类 (低,中等,高风险) 的比较以及1年内随后的乳腺癌诊断.
- 对AI和放射科医生计算了包括灵敏度,特异性,回忆率和NPV在内的性能指标.
主要成果:
- 在DM队列中,AI (高风险值) 的灵敏度 (74. 4%) 和特异性 (86. 3%) 比放射科医生 (88. 6%, 93. 3%) 低,回忆率更高 (14. 0% vs 7. 2%).
- 使用AI的中间/高风险值显著增加了敏感性 (94.0%),但在DM中显著提高了召回率 (41.8%).
- 在DBT队列中观察到类似的趋势,AI实现了高的NPV,但更高的召回率,特别是在包括中等风险分类时.
结论:
- 人工智能系统可以在DM和DBT之间实现高的NPV解释,支持它们在查工作流程中的潜在作用.
- 人工智能回忆率高于放射科医生,特别是当中等风险分类被包括在内时,需要策略来管理错误阳性.
- 虽然人工智能可以帮助提高效率,但需要进一步的研究来优化其使用并减轻虚假阳性结果的增加的影响.
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