商用深度学习软件的性能评估,用于在儿童群体中检测内出血
Hadiseh Kavandi1, Kyle Costenbader1, Sandrine Yazbek1
1Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
Journal of imaging informatics in medicine
|February 2, 2026
概括
这项研究表明,在成年人身上训练的AI工具可以在96%的敏感度下检测儿童的内出血 (ICH). 儿科特异性特征,如化,导致误报,表明需要量身定制的人工智能培训.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 儿科放射学 儿科放射学
背景情况:
- 在儿科患者中,及时诊断内出血 (ICH) 非常重要.
- 现有的人工智能 (AI) 工具用于ICH检测,主要是通过成人数据进行训练,从而在儿科应用中造成了研究缺口.
- 需要评估商业上可用的AI工具在儿科患者群体中的有效性.
研究的目的:
- 为了评估商业上可用的AI工具,Aidoc,最初对成年人进行了培训,其在儿童患者中检测内出血 (ICH) 的性能.
- 解决使用人工智能对儿童进行准确和及时的ICH诊断的需求.
- 确定儿童神经成像人工智能算法的局限性和改进领域.
主要方法:
- 一项回顾性研究包括2502名儿科患者 (6-17岁),他们在2017年1月至2022年11月期间接受了头部CT扫描.
- 放射学报告和CT图像分别通过自然语言处理 (NLP) 和基于图像的AI算法进行分析.
- 三名放射科医生审查了AI和NLP之间的不一致案例,以建立一个参考标准.
主要成果:
- 人工智能算法在2502名患者中标记了292名疑似ICH的患者.
- 在儿科患者中,AI对ICH检测的灵敏度为96.0%,特异性为93.7%.
- 常见的错误阳性包括胆脉结石化和高密度静脉鼻;错误阴性最常见的是皮肤下部出血.
结论:
- 在成人数据上训练的深度学习AI算法,在检测儿科ICH时具有高灵敏度和特异性.
- 儿童特异性发现,如化和静脉鼻,对成人训练的人工智能构成挑战,导致错误的阳性结果.
- 对人工智能算法的进一步开发和以儿科为重点的培训是必要的,以提高儿科人口中ICH的诊断准确性.
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