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.