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Table 3 Confusion matrices for the three proposed cut-offs for the model predictions regarding presence of definite radiographic sacroiliitis on the test 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 36 4 40
 Model predicts r-axSpA 93 219 312
  129 223 352
 Cohen’s kappa 0.3 (95% CI 0.21–0.4) Accuracy n = 255/352 (72.4%)
Cut-off 2, favouring specificity over sensitivity
 Model predicts nr-axSpA 120 41 161
 Model predicts r-axSpA 9 182 191
  129 223 352
 Cohen’s kappa 0.7 (95% CI 0.63–0.77) Accuracy n = 302/352 (85.8%)
Cut-off 3, optimal relationship between sensitivity and specificity
 Model predicts nr-axSpA 104 19 123
 Model predicts r-axSpA 25 204 229
  129 223 352
 Cohen’s kappa 0.72 (95% CI 0.65–0.8) Accuracy n = 308/352 (87.5%)