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Table 4 Literature review

From: Artificial intelligence improves the accuracy of residents in the diagnosis of hip fractures: a multicenter study

  Year Insti-tution Number of patients Number of images for machine learning Fracture type (femoral neck/ trochanteric fracture) Images including implants on hip or spine Accuracy (%) Sensitivity (%) Specificity (%) AUC Grad-CAM Clinician test (AI-aided test)
Adams et al. [17] 2018 1 805 805 femoral neck fracture excluded 90.6 N/A N/A 0.98 no no
Urakawa et al. [19] 2018 1 1773 3346 femoral trochanteric fracture excluded 95.5 93.9 97.4 0.97 no no
Cheng et al. [18] 2019 1 3605 3605 both included 91 98 84 0.98 yes no
Yamada et al. [21] 2019 1 1047 2923 both excluded 98.0 98.0 98.0 N/A no no
Krogue et al. [22] 2020 1 1118 3026 both included 93.7 93.2 94.2 0.98 yes yes
Cheng et al. [20] 2020 1 3605 3605 both excluded 91.0 98.0 84.0 N/A yes yes
Current study 2021 3 4851 10,484 both included 96.1 95.2 96.9 0.99 yes yes