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Application of Artificial Intelligence Automatic Diatom Identification System in Practical Cases.
Zhou, Y Y; Cao, Y J; Yang, Y; Wang, Y L; Deng, K F; Ma, K J; Chen, Y J; Qin, Z Q; Zhang, J H; Huang, P; Zhang, J; Chen, L Q.
Afiliación
  • Zhou YY; Department of Forensic Medicine, Inner Mongolia Medical University, Hohhot 010030, China.
  • Cao YJ; 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.
  • Yang Y; Department of Forensic Medicine, Nanjing Medical University, Nanjing 210000, China.
  • Wang YL; Department of Forensic Medicine, Inner Mongolia Medical University, Hohhot 010030, China.
  • Deng KF; Department of Forensic Medicine, Inner Mongolia Medical University, Hohhot 010030, China.
  • Ma KJ; 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.
  • Chen YJ; Shanghai Research Institute of Criminal Science and Technology, Shanghai 200083, China.
  • Qin ZQ; 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.
  • Zhang JH; 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.
  • Huang P; 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.
  • Zhang J; 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.
  • Chen LQ; 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.
Fa Yi Xue Za Zhi ; 36(2): 239-242, 2020 Apr.
Article en En, Zh | MEDLINE | ID: mdl-32530174
ABSTRACT
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.
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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Inteligencia Artificial / Diatomeas Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En / Zh Revista: Fa Yi Xue Za Zhi Asunto de la revista: JURISPRUDENCIA Año: 2020 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Inteligencia Artificial / Diatomeas Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En / Zh Revista: Fa Yi Xue Za Zhi Asunto de la revista: JURISPRUDENCIA Año: 2020 Tipo del documento: Article País de afiliación: China