Evaluation of Inspection Efficiency of Diatom Artificial Intelligence Search System Based on Scanning Electron Microscope.
10.12116/j.issn.1004-5619.2021.410719
- Author:
Dan-Yuan YU
1
;
Jing-Jian LIU
2
;
Chao LIU
3
;
Yu-Kun DU
4
;
Ping HUANG
5
;
Ji ZHANG
5
;
Wei-Min YU
6
;
Ying-Chao HU
7
;
Jian ZHAO
1
;
Jian-Ding CHENG
1
Author Information
1. Department of Forensic Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.
2. Department of Forensic Medicine, Kunming Medical University, Kunming 650500, China.
3. Guangzhou Forensic Science Institute & Key Laboratory of Forensic Pathology, Ministry of Public Security, Guangzhou 510442, China.
4. Department of Forensic Medicine, Southern Medical University, Guangzhou 510515, China.
5. 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.
6. Jiangsu JITRI Sioux Technologies Co., Ltd., Suzhou 215100, Jiangsu Province, China.
7. Suzhou LabWorld Scientific Technology Ltd., Suzhou 215100, Jiangsu Province, China.
- Publication Type:Journal Article
- Keywords:
artificial intelligence;
automatic searching;
diatom test;
drowning;
forensic pathology;
manual identification;
scanning electron microscope
- MeSH:
Artificial Intelligence;
Diatoms;
Drowning/diagnosis*;
Humans;
Liver;
Lung;
Microscopy, Electron, Scanning
- From:
Journal of Forensic Medicine
2022;38(1):40-45
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:To explore the application values of diatom artificial intelligence (AI) search system in the diagnosis of drowning.
METHODS:The liver and kidney tissues of 12 drowned corpses were taken and were performed with the diatom test, the view images were obtained by scanning electron microscopy (SEM). Diatom detection and forensic expert manual identification were carried out under the thresholds of 0.5, 0.7 and 0.9 of the diatom AI search system, respectively. Diatom recall rate, precision rate and image exclusion rate were used to detect and compare the efficiency of diatom AI search system.
RESULTS:There was no statistical difference between the number of diatoms detected in the target marked by the diatom AI search system and the number of diatoms identified manually (P>0.05); the recall rates of the diatom AI search system were statistically different under different thresholds (P<0.05); the precision rates of the diatom AI system were statistically different under different thresholds(P<0.05), and the highest precision rate was 53.15%; the image exclusion rates of the diatom AI search system were statistically different under different thresholds (P<0.05), and the highest image exclusion rate was 99.72%. For the same sample, the time taken by the diatom AI search system to identify diatoms was only 1/7 of that of manual identification.
CONCLUSIONS:Diatom AI search system has a good application prospect in drowning cases. Its automatic diatom search ability is equal to that of experienced forensic experts, and it can greatly reduce the workload of manual observation of images.
