摘要
目的探讨人工智能影像组学辅助X射线诊断腰椎骨质疏松性椎体压缩性骨折(OVCF)的效能。方法收集北部战区总医院经MRI诊断为腰椎OVCF的455例患者的临床资料。将患者分为训练组(n=364)和验证组(n=91),提取X射线片,进行图像勾画、特征提取、数据分析,应用人工智能影像组学深度学习并建立OVCF的诊断模型。通过受试者工作特征(ROC)曲线、曲线下面积(AUC)、校准曲线及决策曲线分析(DCA)验证模型有效性后,对比人工阅片、模型阅片、模型辅助人工阅片诊断OVCF的效能。结果ROC曲线、AUC、校准曲线证明,模型具有良好的区分度和校准度,诊断性能优异;DCA证明模型临床净收益较高。人工阅片组诊断效能:准确率0.89,召回率0.62;模型阅片组诊断效能:准确率0.93,召回率0.86,模型诊断表现出良好的预测性,明显优于人工阅片组;模型辅助人工阅片组诊断效能:准确率0.92,召回率0.72,模型辅助人工阅片组召回率高于人工阅片组,但低于模型阅片组,表明该模型具有良好的诊断能力。结论本研究基于人工智能影像组学建立的OVCF诊断模型效能达到理想水平,诊断效能优于人工阅片,可用于辅助X射线诊断早期OVCF。
Objective To explore the efficiency of artificial intelligence and radiomics-assisted X-ray in diagnosis of lumbar osteoporotic vertebral compression fractures(OVCF).Methods The clinical data of 455 patients diagnosed as lumbar OVCF by MRI in our hospital were selected.The patients were divided into the training group(n=364)and the validation group(n=91),X-ray films were extracted,the image delineation,feature extraction and data analysis were carried out,and the artificial intelligence radiomics deep learning was applied to establish a diagnostic model for OVCF.After verifying the effectiveness of the model by receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration curve,and decision curve analysis(DCA),the efficiencies of manual reading,model reading,and modelassisted manual reading of X-ray in the early diagnosis of OVCF were compared.Results The ROC curve,AUC and calibration curve proved that the model had good discrimination and calibration,and excellent diagnostic performance.DCA demonstrated that the model had a higher clinical net benefit.The diagnostic efficiency of the manual reading group:the accuracy rate was 0.89,the recall rate was 0.62.The diagnostic efficiency of the model reading group:the accuracy rate was 0.93,the recall rate was 0.86,the model diagnosis showed good predictive performance,which was significantly better than the manual reading group.The diagnostic efficiency of the model-assisted manual reading group:the accuracy rate was 0.92,the recall rate was 0.72,and the recall rate of the model-assisted manual reading group was higher than that of the manual reading group,but lower than that of the model reading group,indicating the superiority of the model diagnosis.Conclusion The diagnostic model established based on artificial intelligence and radiomics in this study has reached an ideal level of efficacy,with better diagnostic efficacy compared with manual reading,and can be used to assist X-ray in the early diagnosis of OVCF.
作者
韩康恩
王洪伟
顾洪闻
胡寅
唐世磊
张智昊
于海龙
HAN Kang-en;WANG Hong-wei;GU Hong-wen;HU Yin;TANG Shi-lei;ZHANG Zhi-hao;YU Hai-long(Graduate School,Dalian Medical University,Dalian Liaoning 116044,China;Department of Orthopedics,General Hospital of Northern Theater Command,Shenyang Liaoning 110016,China)
出处
《局解手术学杂志》
2024年第7期579-583,共5页
Journal of Regional Anatomy and Operative Surgery
基金
辽宁省科技计划联合计划(2023JH2/101700130)
辽宁省应用基础研究计划(2022JH2/101300024)。
关键词
影像组学
人工智能
X射线
骨质疏松性椎体压缩性骨折
radiomics
artificial intelligence
X-ray
osteoporotic vertebral compression fractures
