Frontiers of Data and Computing ›› 2024, Vol. 6 ›› Issue (2): 68-79.

CSTR: 32002.14.jfdc.CN10-1649/TP.2024.02.007

doi: 10.11871/jfdc.issn.2096-742X.2024.02.007

• Special Issue: Advance of Intelligent Healthcare • Previous Articles     Next Articles

Assessing the Clinical Utility of a Deep Learning-Based Model for Calcification Recognition and Classification in Mammograms

YUAN Jialin1,2(),OUYANG Rushan3,DAI Yi4,LAI Xiaohui5,MA Jie1,2,*(),GONG Jingshan1,2   

  1. 1. The Second Clinical Medical College, Jinan University, Shenzhen, Guangdong 518020, China
    2. Department of Radiology, Shenzhen People's Hospital, Shenzhen, Guangdong 518020, China
    3. Department of Radiology, the Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong 518033, China
    4. Department of Radiology, Peking University Shenzhen Hospital, Shenzhen, Guangdong 518036, China
    5. Department of Radiology, Shenzhen Luohu People's Hospital, Shenzhen, Guangdong 518000, China
  • Received:2023-10-23 Online:2024-04-20 Published:2024-04-26


[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 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.

Key words: breast lesions, mammography, calcification recognition, deep learning