Résumé
Objective The diagnosis of nasal fractures poses challenges in forensic clinical evaluation.This study aims to develop and enhance an artificial intelligence-based model for nasal fracture recognition,evaluate its performance,and provide assistance and support for forensic clinical identification.Methods Multi-center nasal CT images were selected and screened according to the consensus standards set by Chinese experts in nasal CT examination and diagnosis.A recognition model was constructed,followed by external verification and evaluation.Additionally,the diagnostic capabilities of qualified appraisers/doctors with different professional titles(primary,intermediate,and senior)were compared with the performance of the intelligent recognition model.The accuracy,sensitivity,specificity),and negative predictive value(NP)of the intelligent recognition model were comprehensively evaluated.Results The intelligent recognition model exhibited high diagnostic efficiency and stability.It improved the diagnostic accuracy of radiologists and appraisers in detecting nasal fractures while effectively bridging the gap between inexperienced doctors/appraisers and experienced ones.Conclusion The intelligent recognition model for nasal fractures can assist appraisers in enhancing their ability to locate such fractures on CT images and improve work efficiency while enhancing appraisal opinions'accuracy and scientificity.
Résumé
Objective To investigate the recognition efficiency of AI model based on deep learning for cardiac conduction system(CCS).Methods HE staining sections of cardiac muscle and CCS of 17 cases of non-sudden death were selected,and the gold standard was unanimous recognition by 2 forensic pathologists with more than 20 years of CCS diagnosis experience.Inception V3 algorithm was used to establish AI model and complete CCS identification training and testing.Confusion matrix,accuracy,precision,recall,F1 score,ROC curve and AUC value were used to evaluate the effectiveness of AI model,and accuracy,sensitivity and specificity were used to evaluate the efficiency of manual independent and AI-assisted manual recognition for CCS.Results The accuracy of AI model was 87.3%,the precision was 91.9%,the recall was 81.9%,the F1 score was 86.6%,and the AUC value was 95.3%.The accuracy of AI model was higher than that of senior forensic pathologists.There was no statistical significance in the accuracy of AI-assisted senior forensic pathologists in identifying CCS compared with manual independent detection(P>0.05),while the accuracy of AI-assisted intermediate and junior forensic pathologists in identifying CCS was increased by 8%and 14.33%,respectively,with statistical significance(P<0.05).The accuracy rate of AI-assisted junior forensic pathologists to identify CCS was higher than that of intermediate forensic pathologists in self-diagnosis.Conclusion The AI model could be used for the automatic recognition of CCS,and could improve the diagnostic efficiency of CCS and narrow the gap between the forensic pathologists with low experience and that with high experience.
Résumé
Objective To conduct a comprehensive visual analysis of the application of Artificial Intelligence(AI)in forensic medicine using bibliometric tools so as to create knowledge maps of cooperation network,research hotspots,important findings,and potential future trends in this field.Methods The Web of Science(WoSCC)was utilized as the primary data source,search formula incorporating AI and forensic medicine as core subject words was constructed,resulting in a dataset comprising 2 287 literature records.Vosviewer,Citespace,and Bibliometrix were employed for analyzing various aspects such as cooperation network,keyword co-occurrence networks,clustering dynamics,clusters,centrality degree and thematic strategic coordinate charts.These analyses facilitated the creation of corresponding visual maps.Results The collaboration among authors still requires further strengthening;however significant groups have emerged among institutions and countries.Research hotspots and important findings predominantly revolve around algorithmic applications.Furthermore,"identification"related research appears to become a prominent future research trend.Conclusion By employing bibliometric analysis techniques on the application of artificial intelligence in forensic medicine domain,this study successfully elucidats cooperation networks,research hotspots,important findings,future research directions,and provides objective support through empirical evidence for related studies.