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Table 1 Characteristics of patients and X-ray images assigned to the training and test datasets

From: Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray

 

Training dataset

Test dataset

P value

Number of patients

827

190

 

Gender, n (%)

  

0.132

 Male

537 (64.9)

143 (75.3)

 

 Female

290 (35.1)

47 (24.7)

 

Age, median (range), years

58 (17–89)

56 (14–87)

0.038

Number of labeled lesions per image, n (%)

  

0.486

 One

656 (79.4)

144 (75.8)

 

 Two

112 (13.5)

32 (16.8)

 

 More than two

59 (7.1)

14 (7.4)

 

Location of urinary tract stone, n (%)

   

 Kidney

428 (51.8)

106 (55.8)

0.895

 Proximal ureter

334 (40.4)

72 (37.9)

0.582

 Mid-ureter

75 (9.1)

27 (14.2)

0.046

 Distal ureter

184 (22.2)

18 (9.5)

< 0.001

Staghorn calculus, n (%)

  

0.553

 Yes

17 (2.1)

2 (1.1)

 

 No

810 (97.9)

188 (98.9)

 

Artificial foreign body in image, n (%)

  

0.672

 Yes

53 (6.4)

10 (5.3)

 

 No

774 (93.6)

180 (94.7)