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

Journal: Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Published Date:

Abstract

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.

Authors

  • Kun Liu
    Department of Anesthesiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai 200030, China.
  • Mingyang Zhang
    School of Quality and Technical Supervision, Hebei University, Baoding, Hebei 071002, P.R.China.
  • Haoran Li
    School of Quality and Technical Supervision, Hebei University, Baoding, Hebei 071002, P.R.China.
  • Xianghui Wang
    School of Quality and Technical Supervision, Hebei University, Baoding, Hebei 071002, P.R.China.
  • Dongming Li
    School of Information Technology, Jilin Agricultural University, Changchun 130118, China.
  • Shuang Liu
    Key Laboratory for Applied Technology of Sophisticated Analytical Instruments of Shandong Province, Shandong Analysis and Test Center, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, China.
  • Kun Yang
    Department of Bone and Joint Surgery, Affiliated Hospital of Southwest Medical University, Luzhou Sichuan, 646000, P.R.China.
  • Zhenduo Sun
    School of Quality and Technical Supervision, Hebei University, Baoding, Hebei 071002, P.R.China.
  • Linyan Xue
    School of Quality and Technical Supervision, Hebei University, Baoding, Hebei 071002, P.R.China.
  • Zhenyu Cui
    Department of Urology, Affiliated Hospital of Hebei University, Baoding, Hebei 071000, P.R.China.