Skip to main content

Table 1 Representative studies in AI-enabled breast density evaluation from mammographic images

From: Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review

Study Model development dataset Model design Model performance
Image format # images (# women) Vendors (# sites) Model architecture Output density measure Density maps
Roth et al. [35] FFDM (Processed) 109,849 images (N/R) N/R (7 sites) DenseNet-121 BI-RADS density No Four-class K = 0.62–0.77
Dontchos et al. [25] FFDM (Processed) N/R (2174 women) Hologic (1 site) ResNet-18 BI-RADS density (13 interpreting radiologists) No Dense versus non-dense Acc:
94.9% (academic radiologists)
90.7% (community radiologists)
Matthews et al. [26] FFDM (Processed) and SM FFDM: 750,752 images (57,492 women)
SM: 78,445 images (11,399 women)
Hologic (2 sites) ResNet-34 BI-RADS density (11 interpreting radiologists) No Four-class K = 0.72 for FFDM, Site 1
Four-class K = 0.72 for SM, Site 1
Four-class K = 0.79 for SM, Site 2
Saffari et al. [27] FFDM 410 images (115 women) Siemens (1 site) cGAN, CNN BI-RADS density Yes DSC = 98% in dense tissue segmentation
Deng et al. [28] FFDM 18,157 images (women) Hologic (1 site) SE-Attention CNN BI-RADS density No Acc = 92.17%
Perez Benito et al. [29] FFDM (Processed) 6680 images (1785 women) Fujifilm, Hologic, Siemens, GE, IMS (11 sites) ECNN BI-RADS density (2 interpreting radiologists) Yes DSC = 0.77
Chang et al. [30] FFDM (Raw) 108,230 images (21,759 women) GE, Kodak, Fischer (33 sites) ResNet-50 BI-RADS density (92 interpreting radiologists) No Four-class K = 0.67
Ciritsis et al. [31] FFDM 20,578 images (5221 women) N/R (1 site) CNN BI-RADS density (consensus of 2 interpreting radiologists) No AUC = 0.98 for MLO views
AUC = 0.97 for CC views
Lehman et al. [32] FFDM (Processed) 58,894 images (39,272 women) Hologic (1 site) ResNet-18* BI-RADS density (12 interpreting radiologists) No Four-class K = 0.67
Mohamed et al. [33] FFDM (Processed) 22,000 images (1427 women) Hologic (1 site) CNN AlexNet BI-RADS density No AUC = 0.94
Mohamed et al. [34] FFDM (Processed) 15,415 images (963 women) Hologic (1 site) CNN AlexNet BI-RADS density No AUC = 0.95 for MLO views
AUC = 0.88 for CC views
Haji Maghsoudi et al. [38] FFDM (Raw) 15,661 images (4437 women) Hologic (2 Sites) U-net* APD% Yes DSC = 92.5% in breast segmentation
APDdiff = 4.2–4.9%
Li et al. [37] FFDM (Raw) 661 images (444 women) GE (1 site) CNN APD% Yes DSC = 76% in dense tissue segmentation
Kallenberg et al. [36] FFDM (Raw) N/R (493 women) Hologic (1 site) CSAE APD% Yes DSC = 63% in dense tissue segmentation
  1. The table describes the development image dataset used in each study, including format of mammographic images, sample size, and vendors, as well as methodological details for the AI model (output breast density measure, model architecture and availability of spatial density maps) and the model performance in breast density evaluation
  2. FFDM: full-field digital mammography, SM 2D synthetic mammographic image acquired with digital breast tomosynthesis, APD% area percent density, MLO medio-lateral oblique, CC cranio-caudal, cGAN conditional generative adversarial network, CNN convolutional neural network, ECNN entirely convolutional neural network, CSAE convolutional sparse auto encoder, DSC dice score, APDdiff difference in APD%, K Cohen kappa coefficient, AUC area under the ROC curve, Acc accuracy
  3. *Indicates publicly available AI model. N/R not explicitly reported in the paper