Skip to main content
. 2021 Nov 9;11(11):1163. doi: 10.3390/jpm11111163

Table 3.

Comparison of state-of-the-art works with our proposed model.

Studies Train/Test/Validation Split % &
Dataset		 Target ACL Tears Experimental
Techniques		 Evaluation
Accuracy AUC Precision Specificity Sensitivity Test Loss
Stajduhar et al., 2017 [41] 10-fold cross-validation
917 ACL MRI cases		 Partially HOG + Lin SVM - 0.894 - - - -
Fully
ruptured		 HOG + RF - 0.943 - - - -
Bien et al., 2018 [42] 60:20:20
Knee MRI validation: 183 ACL MRI		 Partial,
ruptured		 Logistic Regression - 0.911 - - - -
Tsai et al., 2020 [43] 5-fold
ACL:129		 Ruptured ELNet (K = 2)
MultiSlice Norm + Blurpool		 - 0.913 - - - -
Namiri et al., 2020 [45] 70:20:10
1243 Knee MRI NIH		 Average 3 classes ACL 2D CNN - - - 94.6% 59.6% -
Average 3 classes ACL 3D CNN - - - 93.3% 63.3 % -
Dunnhofer et al., 2021 [55] 5-fold
80:20
917 ACL MRI		 Average 3 classes ACL MRNet with MRPyrNet 83.4% 0.914 - 84.3% 80.6% -
ELNet with MRPyrNet 85.1% 0.900 - 90.8% 67.9% -
Kapoor et al., 2021 [46] 917 ACL MRI Average 3 classes ACL CNN 82.0% - - - - 0.42
DNN 82.0% - - - - 0.43
RNN 81.8% - - - - 0.45
SVM 88.2% 0.910 - - -
M. J. Awan et al., 2021 [47] 75:25
917 ACL cases		 Average 3 classes ACL Customized ResNet-14 + Class balancing
Adam, LR: 0.001		 90.0% 0.973 89.0% 94.0% 88.7% 0.526
5-fold
917 ACL cases		 92% 0.980 91.7% 94.6% 91.7% 0.466
Li et al., 2021, [54] MRI group + Arthroscopy group
ACL:60 cases		 Grade 0
Grade 1
Grade II
Grade III		 Multi-modal feature fusion Deep CNN 92.1% 0.963 - 90.6 % 96.7% -
Proposed 70:30
75:25
917 ACL MRI		 Average 3 classes ACL Standard CNN Adam LR = 0.0001, 25% 96.3% 0.950 95.0% 96.9% 96.0 % 0.971
Proposed 70:30
75:25
917 ACL MRI		 Average 3 classes ACL Customized CNN Adam,
LR = 0.001, 25%		 97.1% 0.990 96.3% 97% 96.3% 0.230
Customized CNN RMSprop,
LR = 0.001, 25%		 98.6% 0.976 98.0% 98.5% 98.0% 0.164

The bold parts are author’s approaches.