Sex Estimation of Medial Aspect of the Ischiopubic Ramus in Adults Based on Deep Learning.
10.12116/j.issn.1004-5619.2022.220505
- Author:
Yong-Gang MA
1
;
Yong-Jie CAO
2
;
Yi-Hua ZHAO
3
;
Xin-Jun ZHOU
1
;
Bin HUANG
1
;
Gao-Chao ZHANG
1
;
Ping HUANG
2
;
Ya-Hui WANG
2
;
Kai-Jun MA
4
;
Feng CHEN
5
;
Dong-Chuan ZHANG
4
;
Ji ZHANG
2
Author Information
1. 3201 Hospital Affiliated to Xi'an Jiaotong University, Hanzhong 723000, Shaanxi Province, 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.
3. Fujian Kesheng Judicial Expertise Center, Putian 351100, Fujian Province, China.
4. Institute of Forensic Science, Shanghai Public Security Bureau, Shanghai 200083, China.
5. Department of Forensic Medicine, School of Basic Medical Sciences, Nanjing Medical University, Nanjing 211166, China.
- Publication Type:Journal Article
- Keywords:
deep learning;
forensic anthropology;
medial aspect of the ischiopubic ramus;
pelvis;
sex estimation;
three- dimensional reconstruction;
transfer learning
- MeSH:
Adult;
Female;
Humans;
Male;
Deep Learning;
Imaging, Three-Dimensional;
Pelvis;
Reproducibility of Results;
Tomography, X-Ray Computed;
Young Adult;
Middle Aged;
Aged;
Aged, 80 and over
- From:
Journal of Forensic Medicine
2023;39(2):129-136
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:To investigate the reliability and accuracy of deep learning technology in automatic sex estimation using the 3D reconstructed images of the computed tomography (CT) from the Chinese Han population.
METHODS:The pelvic CT images of 700 individuals (350 males and 350 females) of the Chinese Han population aged 20 to 85 years were collected and reconstructed into 3D virtual skeletal models. The feature region images of the medial aspect of the ischiopubic ramus (MIPR) were intercepted. The Inception v4 was adopted as the image recognition model, and two methods of initial learning and transfer learning were used for training. Eighty percent of the individuals' images were randomly selected as the training and validation dataset, and the remaining were used as the test dataset. The left and right sides of the MIPR images were trained separately and combinedly. Subsequently, the models' performance was evaluated by overall accuracy, female accuracy, male accuracy, etc.
RESULTS:When both sides of the MIPR images were trained separately with initial learning, the overall accuracy of the right model was 95.7%, the female accuracy and male accuracy were both 95.7%; the overall accuracy of the left model was 92.1%, the female accuracy was 88.6% and the male accuracy was 95.7%. When the left and right MIPR images were combined to train with initial learning, the overall accuracy of the model was 94.6%, the female accuracy was 92.1% and the male accuracy was 97.1%. When the left and right MIPR images were combined to train with transfer learning, the model achieved an overall accuracy of 95.7%, and the female and male accuracies were both 95.7%.
CONCLUSIONS:The use of deep learning model of Inception v4 and transfer learning algorithm to construct a sex estimation model for pelvic MIPR images of Chinese Han population has high accuracy and well generalizability in human remains, which can effectively estimate the sex in adults.
