人工智能应用用于肺结节的诊断
1Professor of Medicine, MD Anderson Cancer Center, 1400 Pressler St., Unit 1462 FCT12.5099, Houston, Texas, USA.
Current opinion in pulmonary medicine
|May 6, 2025
概括
人工智能 (AI) 在使用成像和血液检测诊断单独的肺结节 (SPNs) 方面表现有前途. 然而,人工智能需要进一步验证和临床医生培训才能在临床实践中成为可靠的第二意见.
科学领域:
- 肺部医学 肺部医学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 孤独性肺结节 (SPNs) 是常见的发现,需要准确的诊断.
- 人工智能 (AI) 越来越多地被探索其在医学诊断中的潜力.
- 目前用于SPN的诊断方法在准确性和效率方面面临挑战.
研究的目的:
- 审查AI在诊断SPNs中的临床应用和局限性.
- 评估AI在SPN成像和基于血液/组织的诊断中的作用.
- 强调用于SPN诊断的AI实施的实际挑战.
主要方法:
- 对基于计算机断层扫描 (CT) 的计算机辅助诊断 (CAD) 中的人工智能应用的审查.
- 在结节检测,细分和分类中分析AI性能.
- 使用RNA测序和临床数据对人工智能驱动的血液测试进行评估.
主要成果:
- 人工智能在基于CT的CAD中表现出很高的性能,分段化Dice系数为0.70-0.92和恶性瘤分类AUC为0.86-0.97.
- 人工智能血液测试可以达到高达0.907的AUC,以区分良性和恶性结节.
- 显著的局限性包括缺乏前性,多机构验证,潜在的过拟合,以及概括性问题.
结论:
- 人工智能对SPN诊断具有前景,但需要在不同人群中进行严格的验证.
- 有效的临床整合需要加强临床医生的培训和对AI性能指标的理解.
- 人工智能应该充当补充工具,提供第二意见,而不是取代医生的判断.
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