基于深度学习乳腺X线摄影钙化识别分类模型的临床应用价值OA

[Objective]This article is to assess the clinical application value of a deep learning-based model for recognizing and classifying mammography calcifications.[Methods]Multicenter mammography data were employed,with lesion assessments conducted by both senior-junior radiologists and two junior radiologists.The deep learning-based model was used in both standalone and combined approaches.Diagnostic performance was then evaluated.[Results]The introduction of the deep learning model demonstrates comparable capabilities to senior-junior radi-ologists and two junior radiologists(miss rates:0.81%vs.0.65%,1.14%vs.1.63%,P>0.05).The deep learning model effectively assists senior-junior radiologists(sensitivity 0.926,AUC 0.81,P=0.014)and two junior radiolo-gists(sensitivity 0.896,AUC 0.79,P=0.049)in detecting suspicious calcifications,especially in benign lesions.[Limitations]The study requires more prospective multicenter data and different deep learning models to com-pare their clinical utility.[Conclusions]Deep learning frameworks offer valuable support for mammography cal-cification recognition and classification,providing rapid assistance for diagnosis and clinical strategy support.