Deep learning in medical image analysis: A third eye for doctors
Deep learning in medical image analysis: A third eye for doctors
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DOI：
10.1016/j.jormas.2019.06.002
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发表时间：
2019-09-01
影响因子：
2.2
通讯作者：
Khonsari, R. H.
中科院分区：
文献类型：
--
作者：
Fourcade, A.;Khonsari, R. H.
目标和范围：人工智能（AI）在医学上是一个快速发展的领域。卷积神经网络（cnn）等深度学习算法的兴起，为医学图像分析的自动化提供了令人着迷的前景。在这篇系统综述文章中，我们筛选了当前的文献，并研究了以下问题：“用于图像识别的深度学习算法能否改善医学中的视觉诊断？”“材料和方法：我们对2019年5月之前发表在医学文献中的使用cnn进行医学图像分析的文章进行了系统综述。文章根据以下项目进行筛选：图像分析方法类型（检测或分类）、算法架构、使用的数据集、训练阶段、测试、比较方法（与专家或其他）、结果（准确性、敏感性和特异性）和结论。结果：我们在PubMed数据库中确定了352篇文章，并排除了327篇没有评估其性能的文章（综述文章），或者评估了除检测或分类以外的任务（如分割）。纳入的25篇论文发表于2013年至2019年，涉及大量医学专业。作者大多来自北美和亚洲。训练cnn需要大量的定性医学图像，这通常是国际合作的结果。为分析自然图像而设计的最常见的cnn，如AlexNet和GoogleNet，证明了它们对医学图像的适用性。结论：cnn不是医生的替代方案，但将有助于优化日常工作，从而对我们的实践产生潜在的积极影响。具有强烈视觉成分的专业，如放射学和病理学，将发生深刻的转变。包括外科医生在内的医疗从业人员在开发和实施这类装置方面可发挥关键作用。(C) 2019年由Elsevier Masson SAS出版。
Aim and scope: Artificial intelligence (AI) in medicine is a fast-growing field. The rise of deep learning algorithms, such as convolutional neural networks (CNNs), offers fascinating perspectives for the automation of medical image analysis. In this systematic review article, we screened the current literature and investigated the following question: "Can deep learning algorithms for image recognition improve visual diagnosis in medicine?''Materials and methods: We provide a systematic review of the articles using CNNs for medical image analysis, published in the medical literature before May 2019. Articles were screened based on the following items: type of image analysis approach (detection or classification), algorithm architecture, dataset used, training phase, test, comparison method (with specialists or other), results (accuracy, sensibility and specificity) and conclusion.Results: We identified 352 articles in the PubMed database and excluded 327 items for which performance was not assessed (review articles) or for which tasks other than detection or classification, such as segmentation, were assessed. The 25 included papers were published from 2013 to 2019 and were related to a vast array of medical specialties. Authors were mostly from North America and Asia. Large amounts of qualitative medical images were necessary to train the CNNs, often resulting from international collaboration. The most common CNNs such as AlexNet and GoogleNet, designed for the analysis of natural images, proved their applicability to medical images.Conclusion: CNNs are not replacement solutions for medical doctors, but will contribute to optimize routine tasks and thus have a potential positive impact on our practice. Specialties with a strong visual component such as radiology and pathology will be deeply transformed. Medical practitioners, including surgeons, have a key role to play in the development and implementation of such devices. (C) 2019 Published by Elsevier Masson SAS.