Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports-猫眼课题宝

DOI：

10.48550/arxiv.2306.08749

发表时间：

2023-06

期刊：

ArXiv

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Qingqing Zhu;T. Mathai;P. Mukherjee;Yifan Peng;R. M. Summers;Zhiyong Lu

尽管使用语音识别软件减少了放射学报告的周转时间，但持续的通信错误会显著影响放射学报告的解释。预填充放射学报告有望减少报告错误，尽管文献中有多种努力来生成全面的医疗报告，但缺乏利用MIMIC-CXR数据集中患者就诊记录的纵向性质的方法。为了解决这一差距，我们建议使用纵向多模态数据，即，既往患者访视CXR、当前访视CXR和既往访视报告，以预先填写患者当前访视的“发现”部分。我们首先从MIMIC-CXR数据集中收集了26，625名患者的纵向访视信息，并创建了一个名为Longitudinal-MIMIC的新数据集。利用这个新的数据集，训练基于变换器的模型，通过基于交叉注意的多模态融合模块和分层存储器驱动的解码器从患者就诊记录（CXR图像+报告）中捕获多模态纵向信息。与以前只使用当前访问数据作为输入来训练模型的工作相比，我们的工作利用了可用于预填充放射学报告的“发现”部分的纵向信息。实验表明，我们的方法优于最近的几种方法。代码将在https://github.com/CelestialShine/Longitudinal-Chest-X-Ray上发布

Despite the reduction in turn-around times in radiology reporting with the use of speech recognition software, persistent communication errors can significantly impact the interpretation of radiology reports. Pre-filling a radiology report holds promise in mitigating reporting errors, and despite multiple efforts in literature to generate comprehensive medical reports, there lacks approaches that exploit the longitudinal nature of patient visit records in the MIMIC-CXR dataset. To address this gap, we propose to use longitudinal multi-modal data, i.e., previous patient visit CXR, current visit CXR, and the previous visit report, to pre-fill the “findings” section of the patient’s current visit. We first gathered the longitudinal visit information for 26,625 patients from the MIMIC-CXR dataset, and created a new dataset called Longitudinal-MIMIC. With this new dataset, a transformer-based model was trained to capture the multi-modal longitudinal information from patient visit records (CXR images + reports) via a cross-attention-based multi-modal fusion module and a hierarchical memory-driven decoder. In contrast to previous works that only uses current visit data as input to train a model, our work exploits the longitudinal information available to pre-fill the “findings” section of radiology reports. Experiments show that our approach outperforms several recent approaches. Code will be published at https://github.com/CelestialShine/Longitudinal-Chest-X-Ray

Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports

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