Table 2 Participant characteristics per phase

From: Prospective implementation of AI-assisted screen reading to improve early detection of breast cancer

Variable

Initial pilot (n = 3,746)

Extended pilot (n = 9,112)

Live use (n = 15,953)

Age (continuous, years), mean (s.d.)

58.2 (11.0)

58.2 (10.7)

58.6 (10.5)

Age group, n (%)

   

≤35 years

0 (0.0%)

0 (0.0%)

0 (0.0%)

36–45 years

518 (13.8%)

1,149 (12.6%)

1,583 (9.9%)

46–55 years

1,218 (32.5%)

2,998 (32.9%)

5,420 (34.0%)

56–65 years

940 (25.1%)

2,493 (27.4%)

4,699 (29.5%)

66–75 years

806 (21.5%)

1,902 (20.9%)

3,196 (20.0%)

>75 years

264 (7.0%)

570 (6.3%)

1,055 (6.6%)

Family historya, n (%)

   

No

3,620 (96.6%)

8,838 (97.0%)

15,338 (96.1%)

Yes

126 (3.4%)

274 (3.0%)

615 (3.9%)

Tábar classification of parenchymal patternsb, n (%)

   

1

1,506 (40.2%)

3,950 (43.3%)

7,468 (46.8%)

2

729 (19.5%)

1,697 (18.6%)

2,921 (18.3%)

3

336 (9.0%)

679 (7.5%)

465 (2.9%)

4

365 (9.7%)

848 (9.3%)

1,423 (8.9%)

5

114 (3.0%)

246 (2.7%)

310 (1.9%)

Missing

696 (18.6%)

1,692 (18.6%)

3,366 (21.1%)

  1. aFamily history of cancer = ‘yes’ if at least two first-degree female family members have been diagnosed with breast cancer.
  2. bA Tabár classification17 of 4 or 5 correlating with high density (BI-RADS (breast imaging and reporting data system) breast density class C or D).