成人训练的人工智能模型在儿科成像中的性能 - - 范围审查
Lene Bjerke Laborie1,2, Jennifer Lee3, Edward Antram4,5
1Mohn Medical Imaging and Visualization Centre, Department of Radiology, Haukeland University Hospital, Bergen, Norway. lene.bjerke.laborie@helse-bergen.no.
European radiology
|February 11, 2026
概括
成人放射学的人工智能 (AI) 工具显示儿童,特别是2岁以下儿童的表现下降. 在将成人训练的人工智能应用于儿科成像之前,验证至关重要.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 儿科成像学 儿科成像学
背景情况:
- 人工智能 (AI) 模型越来越多地用于医学成像.
- 成人训练的人工智能工具通常在没有特定适应的情况下应用于儿科患者.
- 这些成年人训练的人工智能工具在儿童中的性能尚未得到充分理解.
研究的目的:
- 评估成年人训练人工智能模型在儿科成像数据集上的表现.
- 量化不同成像模式,人工智能任务和儿科年龄组的性能退化.
- 确定应用成人人工智能在儿科放射学中的具体挑战.
主要方法:
- 进行了全面的文献搜索,涵盖了10年的研究.
- 如果这些研究评估了儿童成像数据的成年人训练人工智能模型,则将研究纳入.
- 数据提取和叙事分析是由两个独立的审稿人进行的.
主要成果:
- 20项研究符合纳入标准,评估了各种儿科队列和成像模式 (放射学,CT,MRI,DEXA,超声波) 的AI工具.
- 大多数研究报告说,当成人AI应用于儿科数据时,其性能下降,检测任务显示最显著的下降.
- 2岁以下的儿童在所有AI任务中始终表现出最大的绩效缺陷.
结论:
- 成人训练的人工智能模型在儿科患者中表现较差,特别是在新生儿和婴儿中.
- 显著的性能下降需要在儿童临床使用之前仔细评估和调整AI工具.
- 在儿科放射学中,成人人工智能的临床实施需要验证和潜在的微调.
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