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. 2024 May 23;14:11818. doi: 10.1038/s41598-024-61960-0

Table 2.

Diagnostic performances of each subgroup after conducting AI-unassisted or AI-assisted evaluation for differentiating CCM and AIH.

Reviewers AI-unassisted AI-assisted P value
Total reviewers (%) (95% confidence interval)
 Accuracy 79.86 (75.03, 84.05) 86.92 (82.77, 90.28)  < 0.001
 Sensitivity 71.39 (68.56, 74.05) 81.21 (78.72, 83.47)
 Specificity 92.61 (90.41, 94.33) 95.51 (93.69, 96.81)
Emergency department physicians
 Accuracy 72.57 (67.29, 77.38) 80.73 (75.86, 84.96) 0.006
 Sensitivity 59.53 (54.29, 64.58) 70.23 (65.21, 74.81)
 Specificity 92.17 (87.97, 94.99) 96.52 (93.29, 98.22)
Radiology residents
 Accuracy 75.35 (69.95, 80.21) 84.21 (79.63, 88.04)  < 0.001
 Sensitivity 63.01 (57.80, 67.92) 77.17 (72.46, 81.28)
 Specificity 93.91 (90.04, 96.33) 94.78 (91.10, 96.99)
Neuroradiologists
 Accuracy 91.67 (87.88, 94.57) 95.83 (92.83, 97.83) 0.56
 Sensitivity 91.62 (88.22, 94.10) 96.24 (93.68, 97.79)
 Specificity 91.74 (87.46, 94.65) 95.22 (91.64, 97.31)

AI, artificial intelligence; CCM, cerebral cavernous malformation; AIH, acute intraparenchymal hemorrhage.