Application and prospect of deep learning in primary liver cancer-related diagnostic model
10.3969/j.issn.1001-5256.2022.01.003
- VernacularTitle:深度学习在原发性肝癌相关诊断模型中的应用与前景
- Author:
Qinghua ZHANG
1
;
Haitao LI
;
Guoxu FANG
;
Pengfei GUO
;
Jingfeng LIU
2
Author Information
1. Graduate School of Fujian Medical University, Fuzhou 350108, China
2. Department of Hepatobiliary and Pancreatic Tumor Surgery, Fujian Provincial Tumor Hospital, Fuzhou 350014, China
- Publication Type:Discussions by Experts
- Keywords:
Liver Neoplasm;
Deep Learning;
Machine Learning
- From:
Journal of Clinical Hepatology
2022;38(1):20-25
- CountryChina
- Language:Chinese
-
Abstract:
Deep learning is a process in which machine learning obtains new knowledge and skills by simulating the learning behavior of human brain through massive data training and analysis. With the development of medical technology, a large amount of data has been accumulated in the medical field, and the research on data may help to understand the relationships and rules within data and predict the onset and prognosis of human diseases. Deep learning can find the hidden information in data and has been increasingly used in the medical field. Primary liver cancer is a malignant tumor with high incidence and mortality rates, poor prognosis, and a high recurrence rate, and early diagnosis, timely treatment, and prediction of recurrence have always been the research hotspots in recent years. This article reviews the advances in the application of deep learning in the diagnosis and recurrence of liver cancer from the aspects of risk prediction, postoperative recurrence, and survival risk prediction.
