Citation

Zhou YY, Cao YJ, Yang Y, Wang YL, Deng KF, Ma KJ, Chen YJ, Qin ZQ, Zhang JH, Huang P, Zhang J, Chen LQ. Fa Yi Xue Za Zhi 2020; 36(2): 239-242.

Copyright

(Copyright © 2020, Si fa bu Si fa jian ding ke xue ji shu yan jiu suo)

DOI

10.12116/j.issn.1004-5619.2020.02.017

PMID

32530174

Abstract

OBJECTIVE To discuss the application of artificial intelligence automatic diatom identification system in practical cases, to provide reference for quantitative diatom analysis using the system and to validate the deep learning model incorporated into the system.

METHODS Organs from 10 corpses in water were collected and digested with diatom nitric acid; then the smears were digitally scanned using a digital slide scanner and the diatoms were tested qualitatively and quantitatively by artificial intelligence automatic diatom identification system.

RESULTS The area under the curve （AUC） of the receiver operator characteristic （ROC） curve of the deep learning model incorporated into the artificial intelligence automatic diatom identification system, reached 98.22% and the precision of diatom identification reached 92.45%.

CONCLUSION The artificial intelligence automatic diatom identification system is able to automatically identify diatoms, and can be used as an auxiliary tool in diatom testing in practical cases, to provide reference to drowning diagnosis.

Language: en

Keywords

forensic pathology; artificial intelligence; diatoms; death from drowning