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%) |