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人工智能在泌尿外科影像学诊断的现状及展望

杨龙雨禾 王跃强 邱学德 张贵福 杨智明 黄曦 俞林

杨龙雨禾, 王跃强, 邱学德, 张贵福, 杨智明, 黄曦, 俞林. 人工智能在泌尿外科影像学诊断的现状及展望[J]. 分子影像学杂志, 2020, 43(2): 225-229. doi: 10.12122/j.issn.1674-4500.2020.02.10
引用本文: 杨龙雨禾, 王跃强, 邱学德, 张贵福, 杨智明, 黄曦, 俞林. 人工智能在泌尿外科影像学诊断的现状及展望[J]. 分子影像学杂志, 2020, 43(2): 225-229. doi: 10.12122/j.issn.1674-4500.2020.02.10
Longyuhe YANG, Yueqiang WANG, Xuede QIU, Guifu ZHANG, Zhiming YANG, Xi HUANG, Lin YU. Application and prospect of artificial intelligence in department of urology[J]. Journal of Molecular Imaging, 2020, 43(2): 225-229. doi: 10.12122/j.issn.1674-4500.2020.02.10
Citation: Longyuhe YANG, Yueqiang WANG, Xuede QIU, Guifu ZHANG, Zhiming YANG, Xi HUANG, Lin YU. Application and prospect of artificial intelligence in department of urology[J]. Journal of Molecular Imaging, 2020, 43(2): 225-229. doi: 10.12122/j.issn.1674-4500.2020.02.10

人工智能在泌尿外科影像学诊断的现状及展望

doi: 10.12122/j.issn.1674-4500.2020.02.10
详细信息
    作者简介:

    杨龙雨禾,硕士,住院医师,E-mail: 394774084@qq.com

    通讯作者:

    王跃强,主任医师,E-mail: wyq_wsk@163.com

Application and prospect of artificial intelligence in department of urology

  • 摘要: 目前在国内外,人工智能已经在泌尿外科领域的日常医疗保健中有所运用,如通过建立神经网络,从CT结果上预测尿石症的预后,结石清除率,预测术中出血量等,使医生更好地选择治疗方案。通过CT纹理研究,经深度学习和机器学习,准确鉴别肾脏良恶性肿瘤,提高透明细胞癌的检出率,大大降低漏诊及误诊率。使用放射学和纹理特征分析来研究膀胱癌,区分低级别和高级别肿瘤,使医生选定对病人创伤小的术式。并使用算法预测治疗反应,肿瘤复发率,从而延长患者生存率。开发前列腺癌Gleason评分预测、MRI计算机辅助算法诊断,手术结果和生化复发预测等,针对不同病人提供个体化治疗方案。研究发现,这些方法优于传统的统计方法。本文旨在全面回顾近年来人工智能在泌尿外科领域影像学应用的发展,从尿石症、肾肿瘤、膀胱癌、前列腺癌4种常见泌尿系疾病的影像学应用综述,为今后人工智能的临床运用提供更开阔的思路。

     

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  • 收稿日期:  2020-03-24
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