Research Progress and Prospect of Machine Learning in Bone Age Assessment.
10.12116/j.issn.1004-5619.2020.01.018
- Author:
Li Qin PENG
1
;
Lei WAN
2
;
Mao Wen WANG
2
;
Zhuo LI
2
;
Hu ZHAO
1
;
Ya Hui WANG
2
Author Information
1. Faculty of Forensic Medicine, Zhongshan School of Medicine, Sun-Yat Sen University, Guangzhou 510080, China.
2. Shanghai Key Laboratory of Forensic Medicine, Key Laboratory of Forensic Science, Ministry of Justice, Shanghai Forensic Service Platform, Academy of Forensic Science, Shanghai 200063, China.
- Publication Type:Review
- Keywords:
forensic anthropology;
age determination by skeleton;
machine learning;
review
- MeSH:
Age Determination by Skeleton;
Humans;
Machine Learning
- From:
Journal of Forensic Medicine
2020;36(1):91-98
- CountryChina
- Language:English
-
Abstract:
Bone age assessment has always been one of the key issues and difficulties in forensic science. With the gradual development of machine learning in many industries, it has been widely introduced to imageology, genomics, oncology, pathology, surgery and other medical research fields in recent years. The reason why the above research fields can be closely combined with machine learning, is because the research subjects of the above branches of medicine belong to the computer vision category. Machine learning provides unique advantages for computer vision research and has made breakthroughs in medical image recognition. Based on the advantages of machine learning in image recognition, it was combined with bone age assessment research, in order to construct a recognition model suitable for forensic skeletal images. This paper reviews the research progress in bone age assessment made by scholars at home and abroad using machine learning technology in recent years.
