Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease.
Artificial Intelligence Enhances Studies on Inflammatory Bowel Disease.
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人工智能增强炎症性肠病的研究
DOI：
10.3389/fbioe.2021.635764
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发表时间：
2021
影响因子：
5.7
通讯作者：
Shen J
中科院分区：
文献类型：
--
作者：
Chen G;Shen J
炎症性肠病(IBD)包括溃疡性结肠炎(UC)和克罗恩病(CD)，是一种与遗传易感宿主对共生肠道菌群的免疫反应失调有关的特发性疾病。作为一种全球疾病，IBD的发病率达到每10万人84.3人，并反映出持续逐步上升的轨迹。IBD的医疗费用也非常高。例如，在欧洲，每个患者每年分别有3500欧元的CD和2000欧元的UC。此外，考虑到工作生产率的损失和生活质量的降低，间接成本是不可估量的。在现代，IBD的诊断仍然是基于实验室检查和医学图像的主观判断。因此，早期诊断和干预是一个具有挑战性的目标，也是控制其进展的关键。人工智能(AI)辅助诊断和预后预测在包括胃肠病学在内的许多领域都被证明是有效的。在这项研究中，支持向量机被用来区分IBD的显著特征。因此，IBD诊断的可靠性因其在分类和解决区域问题方面的出色表现而得到提高。卷积神经网络是目前存在的高级图像处理算法。因此，通过自动检测和分类病变，可以更好地理解消化内窥镜图像。本研究旨在总结人工智能在IBD领域的应用，客观评价这些方法的性能，最终了解算法与数据集结合在研究中的作用。
Inflammatory bowel disease (IBD), which includes ulcerative colitis (UC) and Crohn’s disease (CD), is an idiopathic condition related to a dysregulated immune response to commensal intestinal microflora in a genetically susceptible host. As a global disease, the morbidity of IBD reached a rate of 84.3 per 100,000 persons and reflected a continued gradual upward trajectory. The medical cost of IBD is also notably extremely high. For example, in Europe, it has €3,500 in CD and €2,000 in UC per patient per year, respectively. In addition, taking into account the work productivity loss and the reduced quality of life, the indirect costs are incalculable. In modern times, the diagnosis of IBD is still a subjective judgment based on laboratory tests and medical images. Its early diagnosis and intervention is therefore a challenging goal and also the key to control its progression. Artificial intelligence (AI)-assisted diagnosis and prognosis prediction has proven effective in many fields including gastroenterology. In this study, support vector machines were utilized to distinguish the significant features in IBD. As a result, the reliability of IBD diagnosis due to its impressive performance in classifying and addressing region problems was improved. Convolutional neural networks are advanced image processing algorithms that are currently in existence. Digestive endoscopic images can therefore be better understood by automatically detecting and classifying lesions. This study aims to summarize AI application in the area of IBD, objectively evaluate the performance of these methods, and ultimately understand the algorithm–dataset combination in the studies.
支持载体机分类器，用于雌激素受体阳性和负早期发作的乳腺癌。
影响因子：
3.7
作者：
Upstill-Goddard R;Eccles D;Ennis S;Rafiq S;Tapper W;Fliege J;Collins A
通讯作者：
Collins A