Table 4. Diagnostic performance of humans for differentiating tuberculous from viral meningitis.
| TP | FP | TN | FN | Sensitivity (% [95% CI]) | Specificity (% [95% CI]) | Accuracy (% [95% CI]) | AUC (95% CI) | Artificial neural network with IterativeImputer | ||
|---|---|---|---|---|---|---|---|---|---|---|
| P1a | P2b | |||||||||
| Resident #1 | 32 | 20 | 123 | 28 | 53.3 (40.0 - 66.3) | 86.0 (79.2 - 91.2) | 76.4 (69.9 - 82.0) | 0.70 (0.63 - 0.76) | <0.001 | 0.0002 |
| Resident #2 | 23 | 7 | 136 | 37 | 38.3 (26.1 - 51.8) | 95.1 (90.2 - 96.0) | 78.3 (72.0 - 83.8) | 0.67 (0.60 - 0.73) | <0.001 | <0.001 |
| Resident #3 | 31 | 20 | 123 | 29 | 51.7 (38.4 - 64.8) | 86.0 (79.2 - 91.2) | 75.9 (69.4 - 81.6) | 0.69 (0.62 - 0.75) | <0.001 | 0.0001 |
| Resident #4 | 30 | 9 | 134 | 30 | 50.0 (36.8 - 63.2) | 93.7 (88.4 - 97.1) | 80.8 (74.7 - 86.0) | 0.72 (0.65 - 0.78) | <0.001 | 0.0004 |
| ID specialist #1 | 39 | 18 | 125 | 21 | 65.0 (51.6 - 76.9) | 87.4 (80.8 - 92.4) | 80.8 (74.7 - 86.0) | 0.76 (0.70 - 0.82) | <0.001 | 0.03 |
| ID specialist #2 | 46 | 26 | 117 | 14 | 76.7 (64.0 - 86.6) | 81.8 (74.5 - 87.8) | 80.3 (74.2 - 85.6) | 0.79 (0.73 - 0.85) | <0.001 | 0.16 |
aCohen’s kappa statistic was used to test the diagnostic agreement between machine-learning and human judgment.
bComparison of the AUC of the machine-learning with that of human judgment.
True positive means a correct diagnosis of tuberculous meningitis and true negative means a correct diagnosis of viral meningitis.
TP, true positive; FP, false positive; TN, true negative; FN, false negative; AUC, area under the receiver operating characteristics curve; 95% CI, 95% confidence interval; ID, infectious disease.