在腹部CT中使用自由文本放射学报告检测无注释的多器官异常:多中心回顾性研究
Junya Sato1, Kento Sugimoto2, Yuki Suzuki3
1Department of Artificial Intelligence in Diagnostic Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan; Department of Radiology, Osaka University Graduate School of Medicine, 2-2, Yamadaoka, Suita, Osaka, 565-0871, Japan.
EBioMedicine
|November 29, 2024
概括
这项研究开发了一种人工智能 (AI) 系统,用于使用自由文本放射学报告检测腹部异常,消除了手动注释的需求,提高了诊断效率.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 放射学 信息学 信息学
背景情况:
- 开发用于腹部CT异常检测的AI对于减少放射科医生的工作量和改善诊断至关重要.
- 人工智能模型开发的一个重大挑战是,大型,专家注释的数据集的稀缺性.
- 这项研究利用自由文本放射学报告来克服注释限制.
研究的目的:
- 开发一个深度学习管道,用于全面检测腹部CT异常.
- 评估使用从放射学报告中自动提取的信息用于AI模型培训的有效性.
- 评估开发的AI模型在多个腹部器官的性能.
主要方法:
- 一个多中心的回顾性研究,利用腹部CT检查和相关的自由文本报告.
- 开发一个涉及多器官细分和信息提取方案的深度学习管道.
- 训练一个多个实例的学习模型,使用自动提取的标签从报告中检测异常.
主要成果:
- 人工智能模型在外部测试队列中检测器官异常的总体AUC为0.886.
- 在使用自动提取标签的大型数据集上训练的模型显著优于在人类注释标签上训练的模型.
- 开发的管道在检测肝脏,胆囊,胰腺,脏和脏的异常方面表现出很高的性能.
结论:
- 精确的AI异常检测模型用于腹部CT可以使用来自常规临床放射学报告的信息来开发.
- 这种方法消除了对人工专家注释的需求,简化了AI模型开发.
- 该方法可适应各种解剖部位,具有提高诊断工作流程的潜力.
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