Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines.
Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines.
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DOI：
10.1038/s41746-020-00341-z
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发表时间：
2020
影响因子：
15.2
通讯作者：
Lungren MP
中科院分区：
文献类型：
--
作者：
Huang SC;Pareek A;Seyyedi S;Banerjee I;Lungren MP
深度学习技术的进步有可能为医疗保健做出重大贡献，特别是在利用医学成像进行诊断、预后和治疗决策的领域。目前用于放射学应用的最先进的深度学习模型仅考虑像素值信息，而没有告知临床背景的数据。然而，在实践中，基于临床病史和实验室数据的相关和准确的非成像数据使医生能够在适当的临床背景下解释成像结果，从而提高诊断准确性，做出信息丰富的临床决策，并改善患者预后。为了使用深度学习实现类似的目标，基于像素的医学成像模型还必须实现处理来自电子健康记录（EHR）的上下文数据以及像素数据的能力。在本文中，我们描述了不同的数据融合技术，可以应用于联合收割机医学成像与电子病历，并系统地回顾了2012年和2020年之间发表的医学数据融合文献。我们在PubMed和Scopus上进行了系统性检索，以寻找利用深度学习融合多模态数据的原始研究文章。我们总共筛选了985项研究，并从17篇论文中提取了数据。通过这一系统的审查，我们目前的知识，总结了重要的成果，并提供实施指南，作为研究人员感兴趣的应用多模态融合在医学成像的参考。
Advancements in deep learning techniques carry the potential to make significant contributions to healthcare, particularly in fields that utilize medical imaging for diagnosis, prognosis, and treatment decisions. The current state-of-the-art deep learning models for radiology applications consider only pixel-value information without data informing clinical context. Yet in practice, pertinent and accurate non-imaging data based on the clinical history and laboratory data enable physicians to interpret imaging findings in the appropriate clinical context, leading to a higher diagnostic accuracy, informative clinical decision making, and improved patient outcomes. To achieve a similar goal using deep learning, medical imaging pixel-based models must also achieve the capability to process contextual data from electronic health records (EHR) in addition to pixel data. In this paper, we describe different data fusion techniques that can be applied to combine medical imaging with EHR, and systematically review medical data fusion literature published between 2012 and 2020. We conducted a systematic search on PubMed and Scopus for original research articles leveraging deep learning for fusion of multimodality data. In total, we screened 985 studies and extracted data from 17 papers. By means of this systematic review, we present current knowledge, summarize important results and provide implementation guidelines to serve as a reference for researchers interested in the application of multimodal fusion in medical imaging.
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影响因子：
2.6
作者：
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通讯作者：
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影响因子：
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DOI：
10.1109/jbhi.2018.2824327
发表时间：
2019-03-01
影响因子：
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作者：
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