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Table 1 Diagnostic accuracy of GenTB RandomForest and GenTB wide and deep neural network compared with two other leading prediction tools on a depth filtered dataset

From: GenTB: A user-friendly genome-based predictor for tuberculosis resistance powered by machine learning

Drug name Phenotype GenTB - RF GenTB - WDNN Mykrobe TB-Profiler
  R (n) S (n) Sensitivity (95% CI) Specificity (95% CI) Sensitivity (95% CI) Specificity (95% CI) Sensitivity (95% CI) Specificity (95% CI) Sensitivity (95% CI) Specificity (95% CI)
Isoniazid 6,043 13,112 91% (91 to 92) 98% (97 to 98) 90% (89 to 91) 99% (99 to 99) 87% (86 to 88) 98% (98 to 98) 91% (90 to 92) 98% (97 to 98)
Rifampicin 5,068 14,474 93% (93 to 94) 98% (98 to 98) 88% (88 to 89) 99% (99 to 99) 90% (89 to 91) 98% (98 to 99) 92% (91 to 93) 98% (98 to 99)
Ethambutol 2,936 12,362 86% (85 to 87) 92% (92 to 93) 82% (80 to 83) 93% (93 to 94) 79% (77 to 80) 93% (93 to 94) 86% (85 to 88) 92% (92 to 93)
Pyrazinamide 508 1,544 79% (76 to 83) 94% (93 to 95) 80% (79 to 82) 95% (94 to 95) 72% (71 to 74) 98% (97 to 98) 83% (80 to 86) 96% (96 to 97)
Amikacin 618 3,458 67% (63 to 71) 99% (99 to 100) 66% (62 to 70) 99% (99 to 100) 63% (60 to 67) 99% (99 to 100) 55% (51 to 59) 99% (99 to 100)
Capreomycin 648 3,733 63% (59 to 67) 97% (97 to 98) 57% (53 to 61) 98% (98 to 99) 60% (56 to 64) 98% (98 to 99) 56% (52 to 60) 96% (95 to 96)
Ethionamide 502 1,094 67% (63 to 72) 78% (75 to 80) - - - - 70% (66 to 74) 73% (70 to 76)
Kanamycin 576 3,707 68% (64 to 72) 99% (98 to 99) 66% (62 to 70) 100% (99 to 100) 66% (63 to 70) 99 (99 to 100) 68% (64 to 71) 98% (98 to 99)
Streptomycin 2,126 4,968 82% (80 to 83) 89% (88 to 90) 87% (85 to 88) 87% (86 to 88) 68% (66 to 70) 95% (95 to 96) 71% (70 to 73) 95% (95 to 96%)
Ofloxacin 743 4,038 68% (65 to 72) 99% (98 to 99) 62% (58 to 66) 96% (95 to 96) 62% (58 to 65) 99% (98 to 99) 67% (63 to 70) 98 (98 to 99)