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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