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鼻区骨折智能识别模型的构建与应用价值研究

Research on the construction and application value of artificial intelligent recognition model of nasal fracture
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摘要 目的 鼻区骨折的判定是法医临床学鉴定的难点。本研究旨在开发、完善一种基于人工智能技术的鼻区骨折识别模型,并对其性能展开评价,以期为法医临床鉴定工作提供帮助与支持。方法 选取多中心收集的鼻区CT图像,按照我国鼻部CT检查及诊断专家共识的标准进行数据筛选,构建鼻区骨折智能识别模型,并开展外部验证评估。同时,将鼻区骨折智能识别模型的表现与不同职称的具有相关资质的鉴定人/医师(初级、中级、高级职称鉴定人/医师)的能力进行比较,全面评估鼻区骨折智能识别模型的准确性、灵敏度、特异性、阳性预测率和阴性预测率。结果 鼻区骨折智能识别模型具有较高的诊断效能和稳定性;提高了放射科医师和鉴定人在鼻区骨折检测中的诊断准确性;可有效缩小经验不足的医师、鉴定人与经验丰富的医师、鉴定人之间在鼻区骨折检测方面的差距。结论 鼻区骨折智能识别模型可以帮助鉴定人提高其在CT图像上定位鼻区骨折的诊断能力,提高工作效率,增强鉴定意见的准确性和科学性。 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.
作者 朱海标 朱坤姝 张孟周 魏宣 李畅 马隽 王宇聪 钟悦 王旭 杨天潼 Zhu Haibiao;Zhu Kunshu;Zhang Mengzhou;Wei Xuan;Li Chang;Ma Jun;Wang Yucong;Zhong Yue;Wang Xu;Yang Tiantong(Key Laboratory of Evidence Science(China University of Political Science and Law),Ministry of Education,Beijing 100088,China;Collaborative Innovation Center of Judicial Civilization,Beijing 100088,China;Peking University People’s Hospithal,Beijing 100044,China;Chuiyangliu Hospital affiliated to Tsinhua University 100080,China)
出处 《中国法医学杂志》 CSCD 2023年第6期609-613,共5页 Chinese Journal of Forensic Medicine
基金 中国政法大学青年教师资助计划项目(10820710) 国家重点研发计划青年科学家项目(2022YFC3310300)。
关键词 法医临床学 人工智能 鼻区骨折 损伤程度 卷积神经网络 Forensic clinical Artificial Intelligence(AI) Nasal fracture Degree of injury Convolutional neural networks
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