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Table 2 Confusion matrices for the three proposed cut-offs for the model predictions regarding the presence of definite radiographic sacroiliitis on the validation dataset

From: Deep learning for detection of radiographic sacroiliitis: achieving expert-level performance

 

nr-axSpA

r-axSpA

 

Cut-off 1, favouring sensitivity over specificity

 Model predicts nr-axSpA

15

1

16

 Model predicts r-axSpA

64

149

227

 

79

150

229

 Cohen’s kappa

0.22 (95% CI 0.11–0.33)

Accuracy:

n = 164/229 (71.6%)

Cut-off 2, favouring specificity over sensitivity

 Model predicts nr-axSpA

78

38

116

 Model predicts r-axSpA

1

112

113

 

79

150

229

 Cohen’s kappa

0.66 (95% CI 0.57–0.76)

Accuracy:

n = 190/229 (83.0%)

Cut-off 3, optimal relationship between sensitivity and specificity

 Model predicts nr-axSpA

75

19

94

 Model predicts r-axSpA

4

131

135

 

79

150

229

 Cohen’s kappa

0.79 (95% CI 0.7–0.87)

Accuracy:

n = 206/229 (90.0%)