人工智能技术在泌尿系统肿瘤诊断中的研究现状及展望  

Research status and prospect of artificial intelligence technology in the diagnosis of urinary system tumors

作　　者：刘琨 张明洋[1] 李浩然 王向辉 李冬明 刘爽 杨昆[1] 孙振铎[1,4] 薛林雁(综述) 崔振宇(审校)[2] LIU Kun;ZHANG Mingyang;LI Haoran;WANG Xianghui;LI Dongming;LIU Shuang;YANG Kun;SUN Zhenduo;XUE Linyan;CUI Zhenyu(School of Quality and Technical Supervision,Hebei University,Baoding,Hebei 071002,P.R.China;Department of Urology,Affiliated Hospital of Hebei University,Baoding,Hebei 071000,P.R.China;Postdoctoral Research Station of Optical Engineering,Hebei University,Baoding,Hebei 071002,P.R.China;National and Local Joint Engineering Research Center for Measuring Instruments and Systems,Baoding,Hebei 071002,P.R.China)

机构地区：[1]河北大学质量技术监督学院,河北保定071002 [2]河北大学附属医院泌尿外科,河北保定071000 [3]河北大学光学工程博士后科研流动站,河北保定071002 [4]计量仪器与系统国家地方联合工程研究中心,河北保定071002

出　　处：《生物医学工程学杂志》2021年第6期1219-1228,共10页Journal of Biomedical Engineering

基　　金：教育部“春晖计划”合作科研项目;河北省高层次人才资助项目(B20190030010);河北省自然科学基金面上项目(H2019201378);河北大学校长科研基金项目(XZJJ201917);河北大学医学学科培育项目(2021X07);河北省大中学生科技创新能力培育专项项目(2021H060306)。

摘　　要：人工智能技术目前发展迅速,近年来研究人员将其应用在泌尿系统各类肿瘤诊断中,并获得了很多有价值的研究成果。本文从论文数量、图像数据、临床任务三个方面对近年人工智能技术在肾脏肿瘤、膀胱肿瘤、前列腺肿瘤等领域的研究状况进行了整理,旨在总结和分析研究现状,发现有价值的研究思路。结果显示,基于数字影像、病理图像等医学数据建立的人工智能模型,完成泌尿系统肿瘤的基本诊断、肿瘤浸润区域或特定器官的影像分割、基因突变预测和预后效果预测等医学应用的效果良好,但大多数模型在临床应用的要求方面仍有改进空间。一方面,需要进一步提高核心算法的检测、分类、分割等效能;另一方面,需要整合更多标准化的医学数据库,才能有效提高人工智能模型的诊断精度,使之发挥更大的临床价值。With the rapid development of artificial intelligence technology, researchers have applied it to the diagnosis of various tumors in the urinary system in recent years, and have obtained many valuable research results. The article sorted the research status of artificial intelligence technology in the fields of renal tumors, bladder tumors and prostate tumors from three aspects: the number of papers, image data, and clinical tasks. The purpose is to summarize and analyze the research status and find new valuable research ideas in the future. The results show that the artificial intelligence model based on medical data such as digital imaging and pathological images is effective in completing basic diagnosis of urinary system tumors, image segmentation of tumor infiltration areas or specific organs, gene mutation prediction and prognostic effect prediction, but most of the models for the requirement of clinical application still need to be improved. On the one hand, it is necessary to further improve the detection, classification, segmentation and other performance of the core algorithm. On the other hand, it is necessary to integrate more standardized medical databases to effectively improve the diagnostic accuracy of artificial intelligence models and make it play greater clinical value.

关 键 词：泌尿系统 人工智能 肾细胞癌 膀胱癌 前列腺癌 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] R737.1[自动化与计算机技术—控制科学与工程]