实时人工智能辅助超声波系统对基于乳腺密度的BI-RADS C4乳腺病变的影响
Jeeyeon Lee1,2, Won Hwa Kim2,3,4, Jaeil Kim4,5
1Department of Surgery, School of Medicine, Kyungpook National University, Daegu 41566, Republic of Korea.
Cancers
|February 13, 2026
概括
乳房超声波中的人工智能计算机辅助诊断 (AI-CAD) 在更密集的乳房中显示出更好的准确性. 这种人工智能系统对于乳房构成密集的亚洲女性来说尤其有前途.
科学领域:
- 放射学 放射学是指放射学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 人工智能计算机辅助诊断 (AI-CAD) 系统越来越多地用于乳房超声波.
- 乳腺密度显著影响AI-CAD系统的诊断性能.
- 密集的乳房在亚洲人群中非常普遍,因此需要研究AI-CAD在这个人口群体中的有效性.
研究的目的:
- 评估乳腺密度对AI-CAD超声波系统对BI-RADS第四类乳腺病变的诊断准确性的影响.
- 评估乳腺密度如何影响AI-CAD超声波的灵敏度,特异性,PPV,NPV和整体精度.
- 确定AI分配的BI-RADS类别与不同乳腺密度的组织病理学发现之间的一致性.
主要方法:
- 对110个连续的BI-RADS第四类乳腺病变进行了回顾性审查.
- 一个AI-CAD超声波系统自动分类病变,并计算恶性瘤 (POM) 的概率.
- 基于乳腺密度 (BI-RADS B-D) 分析了诊断性能指标,基因病理作为参考标准.
主要成果:
- 总体灵敏度为81.3%和NPV为87.5%;特异性为53.8%和PPV为41.9%.
- 诊断性能和AI和组织病理学之间的一致性随着乳腺密度的增加而改善,密度D的最高指标是D.
- 在所有密度组中,非良性病变始终显示出更高的POM值.
结论:
- 乳腺密度显著影响BI-RADS C4病变的AI-CAD超声波性能,在密集的乳房中观察到更高的精度.
- 人工智能系统在密集的乳房环境中表现出更一致的病变解释,这表明它作为诊断辅助的实用性.
- 研究结果支持人工智能辅助超声波对有密集乳房的亚洲女性的潜在价值;建议进一步验证.
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