Research status and prospect of artificial intelligence technology in the diagnosis of urinary system tumors.
10.7507/1001-5515.202103010
- Author:
Kun LIU
1
;
Mingyang ZHANG
1
;
Haoran LI
1
;
Xianghui WANG
1
;
Dongming LI
2
;
Shuang LIU
1
;
Kun YANG
1
;
Zhenduo SUN
1
;
Linyan XUE
1
;
Zhenyu CUI
2
Author Information
1. School of Quality and Technical Supervision, Hebei University, Baoding, Hebei 071002, P.R.China.
2. Department of Urology, Affiliated Hospital of Hebei University, Baoding, Hebei 071000, P.R.China.
- Publication Type:Journal Article
- Keywords:
artificial intelligence;
bladder cancer;
prostate cancer;
renal cell carcinoma;
urinary system
- MeSH:
Algorithms;
Artificial Intelligence;
Humans;
Male;
Prognosis;
Prostatic Neoplasms/diagnosis*;
Technology
- From:
Journal of Biomedical Engineering
2021;38(6):1219-1228
- CountryChina
- Language:Chinese
-
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.
