Enzymatic Digestion Method Coupled with Artificial Intelligence Techniques in Forensic Drowning Diatom Detection
10.13471/j.cnki.j.sun.yat-sen.univ（med.sci）.2023.0309
- VernacularTitle:酶消化法结合人工智能技术在法医学溺死硅藻检验中的应用
- Author:
Hui-xiao YU
1
;
Yong-zheng ZHU
2
;
Tian-qi ZHAO
1
;
Qi CHENG
2
;
Zhou-ru LI
1
;
Wen-jiang YIN
1
;
Min CHEN
2
;
Hong-xing CAI
1
Author Information
1. Department of Forensic Medicine， Xuzhou Medical University， Xuzhou 221000， 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:Journal Article
- Keywords:
forensic pathology;
diatoms;
drowning;
proteinase K;
artificial intelligence;
convolutional neural network
- From:
Journal of Sun Yat-sen University(Medical Sciences)
2023;44(3):430-438
- CountryChina
- Language:Chinese
-
Abstract:
ObjectiveArtificial intelligence （AI） full smear automated diatom detection technology can perform forensic pathology drowning diatom detection more quickly and efficiently than human experts.However， this technique was only used in conjunction with the strong acid digestion method， which has a low extraction rate of diatoms. In this study， we propose to use the more efficient proteinase K tissue digestion method （hereinafter referred to as enzyme digestion method） as a diatom extraction method to investigate the generalization ability and feasibility of this technique in other diatom extraction methods. MethodsLung tissues from 6 drowned cadavers were collected for proteinase K ablation and made into smears， and the smears were digitized using the digital image matrix cutting method and a diatom and background database was established accordingly.The data set was divided into training set， validation set and test set in the ratio of 3：1：1， and the convolutional neural network （CNN） models were trained， internally validated， and externally tested on the basis of ImageNet pre-training. ResultsThe results showed that the accuracy rate of the external test of the best model was 97.65 %， and the area where the model features were extracted was the area where the diatoms were located. The best CNN model in practice had a precision of more than 80 % for diatom detection of drowned corpses. ConclusionIt is shown that the AI automated diatom detection technique based on CNN model and enzymatic digestion method in combination can efficiently identify diatoms and can be used as an auxiliary method for diatom detection in drowning identification.
