【摘要】 【目的】引入基于深度学习乳腺X线摄影钙化识别及分类模型，探讨深度学习技术对钙化灶的准确识别、分类和临床应用价值。【方法】采用多中心乳腺X线检查数据，分别由高-初级诊断医生及两名初级诊断医生采用不结合及结合深度学习模型进行病灶评估，评价其诊断效能。【结果】引入深度学习模型识别钙化灶能力与高-初级诊断医生及两名初级诊断医生识别钙化灶能力相仿（漏检率分别为0.81%vs.0.65%,1.14%vs.1.63%,P>0.05），深度学习模型能够有效帮助高-初级诊断医生(灵敏度0.926,AUC0.81,P=0.014)及两名初级诊断医生（灵敏度0.896,AUC0.79,P=0.049）检出可疑恶性钙化灶，特别是在良性病变中的准确率提升作用明显。【局限】仍需更多前瞻性多中心数据验证模型稳健性，也需引入不同深度学习模型比较其临床应用价值。【结论】深度学习模型有助于乳腺X线摄影钙化识别及分类评估，有助于乳腺癌大规模筛查背景下提供辅助诊断及临床策略支持。更多还原

【Abstract】 [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 learningbased 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 radiologists 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 radiologists(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 compare their clinical utility. [Conclusions] Deep learning frameworks offer valuable support for mammography calcification recognition and classification, providing rapid assistance for diagnosis and clinical strategy support.更多还原