胰腺成像人工智能基础教程。
A primer on artificial intelligence in pancreatic imaging.
机构信息
The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
Sol Goldman Pancreatic Research Center, Department of Pathology, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
出版信息
Diagn Interv Imaging. 2023 Sep;104(9):435-447. doi: 10.1016/j.diii.2023.03.002. Epub 2023 Mar 24.
Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data contained in medical images. Deep learning and radiomics are the two main AI methods currently being applied within radiology. Deep learning uses a layered set of self-correcting algorithms to develop a mathematical model that best fits the data. Radiomics converts imaging data into mineable features such as signal intensity, shape, texture, and higher-order features. Both methods have the potential to improve disease detection, characterization, and prognostication. This article reviews the current status of artificial intelligence in pancreatic imaging and critically appraises the quality of existing evidence using the radiomics quality score.
人工智能（AI）有望通过利用医学图像中包含的大量数据来改变医学成像。深度学习和放射组学是目前在放射学中应用的两种主要 AI 方法。深度学习使用一组分层的自我修正算法来开发最适合数据的数学模型。放射组学将成像数据转换为可挖掘的特征，如信号强度、形状、纹理和更高阶的特征。这两种方法都有可能提高疾病的检测、特征描述和预后判断。本文综述了人工智能在胰腺成像中的现状，并使用放射组学质量评分对现有证据的质量进行了批判性评估。
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