使用纵向胸部X射线和报告来预先填写放射学报告
Qingqing Zhu1, Tejas Sudharshan Mathai2, Pritam Mukherjee2
1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|March 19, 2024
概括
这项研究引入了一种新的方法,用于使用纵向患者数据预填写放射学报告. 该方法利用以前的胸部X射线 (CXR) 和报告来提高当前发现的准确性,减少医疗报告中的错误.
科学领域:
- 人工智能在医学中的应用
- 医学成像信息学 医疗成像信息学
- 医疗保健的自然语言处理.
背景情况:
- 语音识别软件减少了放射学报告时间,但持续的通信错误仍然是一个挑战.
- 现有的生成医疗报告的方法往往忽视了纵向患者数据的价值.
- 精确解释放射学报告对于患者护理至关重要.
研究的目的:
- 为弥补利用纵向患者数据用于放射学报告生成的差距.
- 提出一种新的方法,利用多模式纵向数据预先填写放射学报告的"发现"部分.
- 通过结合患者历史信息来减轻报告错误.
主要方法:
- 开发了使用MIMIC-CXR数据集中的26625名患者的"纵向-MIMIC"数据集.
- 训练了一个基于变压器的模型,结合了以前的CXR图像,当前的CXR图像和以前的报告.
- 采用了交叉注意力多模式融合模块和层次记忆驱动解码器来处理纵向信息.
主要成果:
- 拟议的模型显著优于最近在预填充放射学报告发现的方法.
- 在F1得分中实现了≥3%的改善,在BLEU-4,METEOR和ROUGE-L指标中实现了≥2%的改善.
- 证明了利用纵向多模式数据提高报告准确性的有效性.
结论:
- 利用纵向多模式数据是减少放射学报告错误的有希望的策略.
- 开发的基于变压器的模型有效地利用历史患者访问数据来预先填写报告.
- 这种方法有可能提高放射学报告系统的可靠性和效率.
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